Advances in National Brand and Private Label Marketing: 10th International Conference, 2023 3031328930, 9783031328930

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Springer Proceedings in Business and Economics

Juan Carlos Gázquez-Abad Francisco J. Martínez-López Katrijn Gielens   Editors

Advances in National Brand and Private Label Marketing 10th International Conference, 2023

Springer Proceedings in Business and Economics

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Juan Carlos Gázquez-Abad · Francisco J. Martínez-López · Katrijn Gielens Editors

Advances in National Brand and Private Label Marketing 10th International Conference, 2023

Editors Juan Carlos Gázquez-Abad Department of Economics and Business University of Almería Almería, Spain

Francisco J. Martínez-López Department of Business Administration 1, Business School University of Granada Granada, Spain

Katrijn Gielens Kenan–Flagler Business School The University of North Carolina at Chapel Hill Chapel Hill, NC, USA

ISSN 2198-7246 ISSN 2198-7254 (electronic) Springer Proceedings in Business and Economics ISBN 978-3-031-32893-0 ISBN 978-3-031-32894-7 (eBook) https://doi.org/10.1007/978-3-031-32894-7 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

The International Conference on Advances in National Brand and Private Label Marketing (NB&PL) is the first international forum to present and discuss original, rigorous, and significant contributions specifically on NB and PL issues. Our aim is to promote, stimulate, and publish high-quality contributions approaching any topic on national brands and private labels in retailing, which could help retailers and manufacturers deal with a diversity of issues; this is why considering managerial implications is encouraged too. NB and PL marketing aims at becoming the most relevant international reference for advancing NBs and PL research. As the OECD points out, “COVID-19 has dramatically disrupted the retail sector, with the shock differing massively between brick-and-mortar versus online shops, essential versus non-essential stores, and small versus large retailers”. Indeed, a lot of companies have used the COVID-19 crisis to critically evaluate the size (breadth and depth) of their portfolio to increase profitability. Such evaluation has included taking decisions on the balance between marketing private labels vs. national brands. Looking at those aspects, underlying this new marketing context offers exciting opportunities for researchers. It is with this goal in mind that this Tenth International Conference on Research on National Brand & Private Label Marketing (NB&PL 2023) has been launched and organized. After the success of the nine previous editions, this tenth edition is still believed to be a unique international forum to present and discuss original, rigorous, and significant contributions on topics related to any retailing, private label, or national brand issues. Each paper submitted to NB and PL 2023 has gone through a stringent peer-review process by members of the Program Committee, comprising 51 internationally renowned researchers from 15 countries. A total of 16 papers have been accepted, and they address diverse areas of application such as branding strategies, innovation in private labels, private label consumers, customer databases, COVID-19 consequences, loyalty programs, sustainability, and online grocery retailing, among others. A wide variety of theoretical and methodological approached have been used in these areas. We believe that this tenth edition has continued with the same goals as the nine previous editions: promote, stimulate, and publish high-quality contributions on national brands and private labels, which could help retailers and manufacturers deal with diversity of issues, especially with those related to the impact of COVID-19. Nevertheless, we hope to keep organizing this Conference which is aimed to become an international reference for advancing this promising research field. Finally, we wish to acknowledge the support of the sponsors University of Barcelona, Information Resources Inc. (IRI), and Manufacturers-and-Retailers Spanish Multisectoral Association (AECOC). We would also like to thank all the contributing authors, members of the Program Committee, and the rest of the Organizing Committee for their highly valuable work in enabling the success of this tenth edition of NB and PL.

vi

Preface

Thanks for your generous contribution—IC-NB&PL 2023 would not have been possible without you all. Juan Carlos Gázquez-Abad Francisco J. Martínez-López Katrijn Gielens

Organization

Conference Chairs Juan Carlos Gázquez-Abad Francisco J. Martínez-López Katrijn Gielens

University of Almería, Spain University of Granada, Spain UNC Kenan Flagler, USA

Program Committee Kusum L. Ailawadi Nawel Amrouche Chris Baumann Els Breugelmans Philipp Brüggemann Enrique Bigné James Brown Cristina Calvo-Porral Ioannis E. Chaniotakis Liwen (Brandon) Chen Alexander Chernev Chan Choi Gérard Cliquet Giuseppe Colangelo Ronald W. Cotterill Barbara Deleersnyder John Dawes Els Gijsbrechts Mónica Gómez J. Tomas Gomez-Arias Oscar González-Benito Csilla Horváth Marco Ieva Eugene Jones Jitender Kumar Robert Paul Jones Lien Lamey Elisa Martinelli

Tuck School of Business at Dartmouth, USA Long Island University, USA Macquarie University, Australia KU Leuven, Belgium University of Hagen, Germany University of Valencia, Spain West Virginia University, USA University of La Coruña, Spain University of the Aegean, Greece City University of Hong Kong, China Northwestern University, USA Rutgers Business School, USA Université de Rennes 1, France Catholic University of Milan, Italy University of Connecticut, USA Tilburg University, Netherlands University of South Australia, Australia Tilburg University, Netherlands Autonomous University of Madrid, Spain Saint Mary’s College of California, USA University of Salamanca, Spain Radboud University, The Netherlands University of Parma, Italy The Ohio State University, USA Birla Institute of Management Technology, India The University of Texas at Tyler, USA Katholieke Universiteit Leuven, Belgium University of Modena and Reggio Emilia, Italy

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Organization

Mercedes Martos-Partal Sebastián Molinillo Jiménez Dirk Morschett Martin Natter Nicoletta Occhiocupo Anne L. Roggeveen William P. Putsis Natalia Rubio-Benito Raj Sethuraman Hanna Schramm-Klein Randall Shannon Ian Clark Sinapuelas Harry Timmermans Yaron Timmor Gianfranco Walsh María Jesús Yagüe Guillén Jie Zhang Cristina Ziliani

University of Salamanca, Spain University of Malaga, Spain University of Fribourg, Switzerland Goethe University Frankfurt am Main, Germany UIC-Barcelona, Spain, and Oxford Brookes University, UK Babson College, MA, USA University of North Carolina at Chapel Hill, USA Autonomous University of Madrid, Spain Southern Methodist University, TX, USA University of Siegen, Germany Mahidol University, Thailand San Francisco State University, USA Eindhoven University of Technology, Netherlands Arison School of Business, Israel Friedrich Schiller University of Jena, Germany Autonomous University of Madrid, Spain University of Maryland, USA University of Parma, Italy

Program Organizing Committee Ana Argila Irurita Javier Arroyo Cañada Jordi Aymerich Martínez Jordi Campo Fernández Paz Carreira Gutiérrez Irene Esteban-Millat Santiago Forgas Coll Ruben Huertas García Jaime Gil Lafuente José Luis Ruiz Real María Luisa Solé Moro Emilio Vizuete Luciano

University of Barcelona, Spain University of Barcelona, Spain University of Barcelona, Spain University of Barcelona, Spain University of Barcelona, Spain Open University of Catalonia, Spain University of Barcelona, Spain University of Barcelona, Spain University of Barcelona, Spain University of Almería, Spain University of Barcelona, Spain University of Barcelona, Spain

Contents

National Brands and Private Labels Modeling Heterogeneity in Choice Models, Household Level vs. Intra-household Heterogeneity in Reference Price Effects: Should National Brands Care? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Parneet Pahwa, Nanda Kumar, and B. P. S. Murthi

3

What Are the Main Levers to Convert Occasional and Non-buyers into Regular Buyers of Private-Label Brands? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Samy Belaid, Sedki Karoui, Jérôme Lacoeuilhe, and Dorsaf Fehri

13

Does Retailer Activism Increase Consumers’ Perception of Private Label Brand Equity? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mario D’Arco, Vittoria Marino, and Riccardo Resciniti

23

Psychographic Clusters of Private Label Consumers . . . . . . . . . . . . . . . . . . . . . . . . Morana Fuduri´c, Sandra Horvat, Vatroslav Škare, and Ákos Varga

31

Consumer Behaviour Blockchain-Enabled Banking Services and Customers’ Perceived Financial Well-Being: A Structural Nexus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Maya F. Farah, Muhammad Naveed, and Shoaib Ali

41

How Are US Retailers Protecting Their Customer Data While Growing Their Ad Promotions Business? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Darrell Bartholomew, Stephen Hampton, and Hunter Briegel

50

Generations and Their Preferences for Loyalty Program Rewards in Supermarket Retailing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Giada Salvietti, Marco Ieva, and Cristina Ziliani

57

Investigating the Combinations of Target Products and Gifts: Metal Accounting Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yi-Mu Chen, Allen Chen, and I.-Hsuan Yang

65

The Examination of Social and Service Relational Aspects on Customers’ Retention . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zahy Ramadan, Maya F. Farah, and Salwa Bekdache

70

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Contents

Branding Brand Attitude and Frugality as a Lifestyle: Evidence from Coffee Shop Customers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . María Villavicencio and Walesska Schlesinger The Mediating Role of Self-image Congruence and Perceived Product Quality on the Relationship Between Brand Personality and Brand Equity in the Belgian Beer Market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Johan Hellemans, Kim Willems, and Malaika Brengman

83

89

Sustainable Brands and Retail: A Bibliometric Analysis . . . . . . . . . . . . . . . . . . . . . 100 Emili Vizuete-Luciano, Miguel Guillén-Pujadas, David Alaminos, María Luisa Solé-Moro, and Ana María Argila-Irurita Online Context and COVID-19 Shift in National Brand and Private Label Shares with Households Commencing Online Grocery Shopping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 Philipp Brüggemann and Carsten D. Schultz Online Booking Versus Personalised Service in the Context of a Sports Retailer: A Qualitative Approach to Golf Courses . . . . . . . . . . . . . . . . . . . . . . . . . . 127 María Del Mar Martín-García, José Luis Ruiz-Real, Juan Carlos Gázquez-Abad, and Juan Uribe-Toril The Influence of the Covid-19 Pandemic on Social Media Engagement of Luxury Hotels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 Mónica Gómez-Suárez, Mónica Veloso, and Myriam Quinones Changes of Online Shopping Among the Elderly During the Corona-19 Pandemic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142 Hanna Gendel Guterman, Idit Sohlberg, and Shalom Levy Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151

National Brands and Private Labels

Modeling Heterogeneity in Choice Models, Household Level vs. Intra-household Heterogeneity in Reference Price Effects: Should National Brands Care? Parneet Pahwa, Nanda Kumar(B) , and B. P. S. Murthi Naveen Jindal School of Management, The University of Texas at Dallas, Richardson, TX, USA [email protected]

Abstract. Much of the extant empirical work on consumers’ grocery purchases employ models that are estimated on household scanner panel data. A known limitation of these models is that households may have multiple decision makers, and a decision maker may have brand preferences and marketing mix sensitivities that are distinct from other decision makers in the household. We seek to study whether models using individual customer data provide substantially different insights and managerial implications relative to models that use household data. This important issue has not been addressed in the literature, possibly due to limitations of scanner panel data. Using a unique data set that identifies choices made by individual customers within a household, we estimate multinomial choice models at the household level with and without incorporating intra-household heterogeneity using Markov Chain Monte Carlo (MCMC) procedures. We incorporate controls for unobserved heterogeneity by estimating random coefficients models which allows the brand preferences and the price sensitivity parameters to vary across households. We find that in each product category the estimates obtained at the customer level are significantly different from those obtained at the household level. Our findings imply that targeting promotions based on customer level estimates will result in outcomes that are significantly more profitable relative to targeting based on household level estimates. Keywords: Discrete Choice · Multinomial Logit · Markov Chain Monte Carlo · Retail promotions

1 Introduction There is a considerable amount of research in marketing and economics on the reference price models, which recognize that consumers use some reference point to evaluate whether or not the observed price is an attractive deal (Emery 1970; Monroe 1973). This idea spawned a stream of empirical work where researchers examined the effect of reference price in brand choice decisions (Kalwani, Yim, Rinne and Sujita 1990; Kalyanaram and Little 1994; Mayhew and Winer 1992; Winer 1986 etc.). A robust finding from this body of work is that including reference price effect produces models that fit the data © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. C. Gázquez-Abad et al. (Eds.): NB&PL 2023, SPBE, pp. 3–12, 2023. https://doi.org/10.1007/978-3-031-32894-7_1

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P. Pahwa et al.

better relative to models that ignore the reference price effect (for example, Guadagni and Little 1983). In addition to improving model fit incorporating reference price effects is also important in developing the optimal promotional strategies (Greenleaf 1995; Kopalle, Rao and Assuncao 1996). While there is consensus in the literature that reference price plays an important role in consumer brand choice decisions, there is considerable variation in the conceptualization and consequently the operationalization of the reference price construct. A popular conceptualization of reference price is that it is memory based i.e. consumers’ reference price is based on the memory of prices encountered by them on past purchase occasions. The memory-based view, models reference price as a function of lagged prices (Lattin and Bucklin 1989; Kalyanaram and Little 1994; Krishnamurthi, Mazumdar and Raj 1992; Mayhew and Winer 1992). Kalwani et.al. (1990) and Winer (1986) have extended this idea by making reference price not only a function of lagged prices but other contextual factors such as a price trend and market share. An alternative view argues that consumers may not have a strong memory of past prices and therefore use current prices of certain brands at the point of purchase to form reference prices (Hardie, Johnson and Fader 1993; Rajendran and Tellis 1994). Given the differences across studies in how reference price is operationalized Briesch, Krishnamurthi, Mazumdar and Raj (1997) compare five alternative models (operationalizations of reference price); three that are memory based and two which are stimulus based, with consumers using point of purchase information to construct reference prices. Using a latent class approach to account for heterogeneity across households they find that a memory-based model, one that uses the brand’s own price history and allows for households to have brand specific reference prices offers the best fit and prediction. In this study we too examine how references prices affect brand choice decisions and contribute to the extant literature by investigating how reference prices affect brand choice decisions made by the household and members within the household. The research questions that we seek to address in this study are: (a) whether the estimates obtained from a model which treats the household as a single decision maker are different from ones obtained from a model which treats members within each household as a decision maker? (b) If they are different what is the magnitude of this difference? and most importantly (c) How does it impact a manager’s segmentation, targeting and promotional strategies and brand profits? To this effect we use a unique data set that tracks the choices made by households and members within a household to estimate two models; one that treats the household as a decision-making unit without distinguishing who within the household makes the purchase and one that treats individual customers within the household as a decisionmaking unit while recognizing that they belong to the same household. We find that the estimates of brand preferences, sensitivities to price, previous brand purchased, promotion and reference price obtained from the two models are significantly different. In addition, the brand preferences and sensitivities to marketing mix of customers within a household are quite different implying that there is substantial intra-household heterogeneity which the household level models essentially assume away. Do these differences matter? We find that household level estimates of price sensitivity tend to be lower on average than those obtained from the customer level model. To quantify the impact of the

Modeling Heterogeneity in Choice Models

5

difference in the household and customer level estimates on brands’ strategies and profits we use these estimates to conduct a policy simulation to derive the optimal promotional strategies. We find that the optimal strategy implied by the estimates obtained from a model that treats the household as the decision-making unit can be quite different from that obtained from a model that treats the customer within household as the decisionmaking unit. Most importantly, promotional strategies based on customer level estimate result in higher profits relative to profits obtained from strategies based on household level estimates. Even if the prescriptions for promotional strategies (depth/frequency) based on household level estimates are the same as that from customer level estimates, using the customer level estimates result in higher profits. Consequently, we believe that our findings have important managerial implications.

2 Model To account for intra-household heterogeneity and contrast it with a model which does not, we develop two models. We first consider a model which treats the household as a single decision-making unit and then develop a model which incorporates intrahousehold heterogeneity by treating consumers within a household as different decisionmaking units i.e. we allow for the brand preferences, sensitivity to price, past purchases, promotion and reference price to be different for consumers within a household. In the first model, the utility that a household, h, treated as a single decision-making unit, derives from choosing alternative j on choice occasion t is: Uhjt = Xhjt βh + εhjt , h = {1, 2, ..., H }, j = {1, 2, ..., J }, t = {1, 2, ..., Th }

(1)

Xhjt contains (J-1) brand dummies, price/oz of the alternative, a dummy variable, loyalty (to capture state dependence with variable taking a value 1 if the alternative was chosen by the household in the previous choice occasion or zero otherwise), a promotion dummy (to denote whether the brand was on promotion or not) and PRMRP, which is the difference between the price/oz and the reference price. We assume that the error, εhjt has an extreme value distribution. With these assumptions, the probability that the decision maker h, chooses alternative yht in choice occasion t takes the standard multinomial logit form:   exp Xyht t βh , yht = {1, 2, ..., J } Pr(yht |βh ) = (2) J    exp Xjt βh j=1

  If we let yh = yh1 , . . . , yhTh denote the sequence of choices made by decision maker h, then the likelihood of the observed choice sequence conditional on the decision    exp Xyht t βh (3) maker’s parameter, βh is: L(yh |βh ) = J  t exp(Xhjt βh ) j=1

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Therefore the likelihood function for the observed string of purchases by all households in the sample is given by:  L(Y ) = L(yh |βh ) (4) h

where, Y denotes data; the choice sequence of all households in the sample. In our second model, to recognize that different customers within a household may make trips on different occasions and that their preferences may be distinct from the other decision maker in the household we modify the utility function in Eq. (1) as follows:     Uhjt = Xh1jt βh1 Ch1t + Xh2jt βh2 (1 − Ch1t ) + εhjt , h = {1, 2, ..., H }, j = {1, 2, ..., J }, t = {1, 2, ..., Th } (5) Ch1t = 1, if frequent shopper in household h buys at t =0, otherwise An important distinction of this model from our base model in Eq. (1) is that in this model we recognize who in the household makes the shopping trip and we allow for the preferences to be heterogeneous across the customers in a given household. The dummy variable, Ch1t in the utility function in Eq. (5) takes into account which customer in the household makes the shopping trip. So, for instance if the frequent shopper in household h makes the brand choice decision in choice occasion t, then Ch1t takes a value 1 and takes a value zero otherwise. Note that the brand preferences and sensitivities to marketing mix, maker (customer) on that choice occasion. We βh1 and βh2 are specific to the decision

βh1 which yields the preferences and sensitivities estimate this model to obtain βh = βh2 to marketing mix variables of both customers in each household. The brand choice probability, the household’s likelihood function and the

sample likelihood function for βh1 in Eqs. (2) - (4). We obtain the this model can be computed by replacing βh = βh2 household level parameters and obtain individual parameters for each household using Bayesian estimation procedures suggested by Allenby and Rossi (1997). Given the space limitation we have left out the details of the estimation procedure but interested readers may request the authors.

3 Data We use transaction data from a large retail chain, which records each purchase transaction made by the household and the customer within the household in each shopping trip over a three-year period.1 For each trip, we have information on the number of units of each UPC purchased, the spending on that UPC in that trip, the date/time of purchase and the store that it was purchased from. Each customer in the data set has a unique customer_id 1 The identity of the large retail chain was not provided to us by the manufacturer who provided

us with this data. We were told this is a supermarket similar to Safeway, Albertsons etc. and we suspect given the temporal price variation we see in the data, that it is Hi-Lo store.

Modeling Heterogeneity in Choice Models

7

which in turn belongs to a unique household_id. Given our objective, we only analyze households that have two customers with non-zero spending in a product category. We have complete product description for each UPC in the frozen meals category.2 We use these product descriptions to identify the brand and the size (in ounces) of the UPC that was purchased by the customer. We then use the spending on the UPC, the number of units purchased and the size of each unit purchased to compute the price per ounce for the chosen brand. The prices of alternatives not purchased by customers/households were computed by taking the average price per ounce of the alternatives purchased by other customers in the same store in the same week. In addition to price, we create a variable to control for the effect of customer/households’ past brand choice on the current choice. This variable takes a value 1, if the alternative was purchased in the previous choice occasion and is 0 otherwise. This variable serves to capture state dependence in brand choice. If the estimate is positive then we can conclude that the customer exhibits inertia (or loyalty) and if the estimate is negative, then there is evidence of variety seeking in the category. Many studies using scanner data have found evidence of inertial behavior (or positive state dependence) in a number of categories (Seetharaman, Ainslie and Chintagunta 1999, Dube, Hitsch and Rossi 2010). We wish to test if the result holds for individual level estimates as well. In addition, we construct measures to control for the effect of promotions and reference price. As mentioned earlier the product description identifies the brand purchased in the frozen meals category. While there are several brands, we restrict our attention to the top six brands which account for 81% of the market share frozen meals. In the frozen meals category, we have 524 households (1048 customers) making a total of 19,459 choices over the three-year period. However, given our interest in examining the effect of reference prices on brand choice we focus on households where each of the two customers has made at least 5 choices yielding 261 households (522 customers) resulting in 14,331 choices over the three-year period. In Table 1, we present descriptive statistics for the product category. Table 1. Descriptive Statistics Brand

Market Share

Average Price (per ounce)

BANQUET

0.10

0.13

BIRDS

0.04

0.18

HEALTHY CHOICE

0.06

0.30

MARIE

0.05

0.24

STOUFFER

0.60

0.27

WEIGHT WATCHERS

0.15

0.32

2 The choice of frozen meals as a category was driven primarily by data limitation. Without

performing the analysis on other categories, we cannot speak to whether this category is representative of the rest of the categories in the store. We thank an anonymous reviewer for alerting us to this issue.

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P. Pahwa et al.

Of the 81% share of the market accounted for by these six brands, Stouffer has the largest share, followed by Weight Watchers and Banquet. The share of the other three brands Birds, Healthy Choice and Marie are ~ 5%. The average prices appear to fall into three tiers with Health Choice and Weight Watchers being relatively higher priced; Stouffer and Marie moderately priced and Banquet and Birds being priced lower. We would like to note that the reference price of the households is not observed and so is a latent variable. We use the framework developed in Winer (1986) to impute the reference price.

4 Estimation Results In the Tables 2a and 2b, A and D are the population or hyper-parameters of prior distribution of the household (customer) parameters. In Table 2a and 2b, we report the estimated ˆ the means of the posterior distribution of A and D respectively. In parameters Aˆ and Dˆ Table 2a, A is a 9x1 vector of the posterior means of the 9 parameters. The diagonal ˆ which is a measure of the unobserved elements of the estimated covariance matrix, D, heterogeneity in the respective parameters across the households are reported in the third column. We then contrast these with the estimates obtained from our model that allows individual customers within a household to have distinct preferences. From Table 2a, we can see that relative to the base brand Birds, on average Stouffer is the most preferred and Banquet is the least preferred. The estimates of the variance suggest that there is substantial heterogeneity across households in brand preferences. The estimate of the price coefficient is -6.3 and the diagonal element of the covariance parameter corresponding to this parameter is 16.38 implies that there is a lot of heterogeneity across households in their price sensitivity. As expected, the coefficient of reference price and promotion are positive and there is substantial heterogeneity across households in both their sensitivity to reference price and promotions. Consistent with prior research, we find evidence of inertia in purchase decisions over time at the household level and there is not much heterogeneity in the inertia across households. In Table 2b ˆ ˆ we report we report the estimates, Aand Dfor the two customers, frequent and infrequent shopper in each household. Table 2b offers a preview of the substantial intra-household heterogeneity in brand preferences and sensitivity to marketing mix that exists within a household. Table 2a. Estimates for Frozen Meals without intra-household heterogeneity Aˆ

ˆ D

Mean

Variance

STOUFFER

3.22*

2.37*

WEIGHT

0.89*

5.45*

Covariate

(continued)

Modeling Heterogeneity in Choice Models

9

Table 2a. (continued) Covariate

Aˆ

ˆ D

HEALTHY

0.07

4.0*

MARIE

-0.35

5.34*

BANQUET

-0.51

8.67*

PRICE

-6.30*

16.38*

RP

2.13*

5.27*

PROMO

0.12

0.92*

LOYALTY

1.70*

0.43*

Table 2b. Estimates for Frozen Meals with intra-household heterogeneity Covariate

Frequent Shopper ˆ ˆ Mean (A) Variance (D)

Infrequent Shopper ˆ ˆ Mean (A) Variance (D)

STOUFFER

3.18*

2.96*

3.32*

2.89*

WEIGHT

0.47

8.17*

0.68*

6.60*

-0.45

5.61*

-0.007

5.40*

HEALTHY MARIE

-0.96*

5.95*

-0.37

7.50*

BANQUET

-1.27*

9.52*

-0.53

10.44*

PRICE

-6.87*

21.29*

-5.21*

23.55*

RP

2.45*

37.84*

-1.2*

10.21*

PROMO

0.22

1.36*

0.38*

1.46*

LOYALTY

1.60*

0.61*

1.67*

0.69*

5 Managerial Implications In this section, we illustrate the difference in targeting implications based on household and customer level estimates. For the purpose of this illustration, we assume that a manager wants to run a promotion and target households with a coupon.3 In devising a promotional campaign a brand manager must address the issue of which households to target with a promotion? The details of the policy simulation are available upon request. We present the results in Table 3 below.

3 It is possible given our individual household and customer level estimates to customize the

face value of the coupon at the household level. However, in this policy simulation we focus on blanket coupons, so the targeted households get a discount while others do not.

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P. Pahwa et al. Table 3. Profit Implications

Brand

Expected Profits in m $ units HH

CC

Incremental Profits

Banquet

1920

2696

40%

Birds

2918.4

4178.8

43%

Healthy Ch

4736

6740

42%

Marie

3686.4

5257.2

43%

Stouffers

1792

2527.5

41%

Weight Watchers

4633.6

6605.2

43%

From Table 3, we can see that the ‘Expected Profits’ using the customer level estimates are uniformly higher from that using household level estimates. The last column reports the percentage difference in incremental profits from using customer level estimates vis-à-vis household level estimates. The profit increase ranges from 40–43% depending on the brand. The variation in profit increase across brands can be explained by the magnitude of the elasticity as well as the difference in elasticity obtained from customer level estimates and household level estimates. For instance, the elasticity of Weight Watchers is the highest and so is the difference between the customer level elasticity and household level elasticity so the difference in the incremental profits is 43%. Brands with the lowest elasticity Banquet and Stouffers still stand to increase their profits by ~ 40%.

6 Conclusion In this paper, we study the importance of estimating brand choice models at the individual level, since the purchase decisions are made by individual shoppers. Prior research has been unable to assess this issue since they relied on household level grocery scanner panel data. In these datasets, the individual who goes shopping on a given occasion is not identified. Over the last twenty years, scanner data has been employed to understand the effects of prices and promotions on brand choice and a number of managerial actions have been influenced by these parameters. We examine whether the results from previous research would change if the same models were estimated at the individual level instead of at the household level. Further, if the results changed, what is the magnitude of the difference in estimates and what are the consequent effects on managerial actions? We use a unique data set in which the brand choices made by each individual within a household are identified. The datasets permit us to estimate the brand choice models both at the household level and at the customer level for two product categories, namely frozen meals and popcorn. We are then in a position to compare the resulting estimates and assess how important it is for managers to understand intra-household heterogeneity. Based on past research, we estimate random coefficients multinomial logit choice models using Markov Chain Monte Carlo techniques, which permit us to obtain parameters of interest for each household and for each customer, as in Rossi and Allenby (1993). Further, we

Modeling Heterogeneity in Choice Models

11

incorporate state dependence in the model to study variety seeking or inertial behavior at an individual level. We find that in each product category the estimates obtained at the individual customer level are significantly different from those obtained at the household level. We find that the model estimated at the individual level suggests greater price sensitivity and lower loyalty relative to the household model. These differences are significant even after attempts were made to weight the individual parameters for a given household. We find that households with a frequent (dominant) shopper tend to be more price sensitive than households in which customers’ shopping trips are split evenly. Finally, our findings imply that targeting promotions based on customer level estimates will result in outcomes that are very different relative to targeting based on household level estimates. More importantly, we find using a policy simulation that offering a promotion to customers identified as price sensitive using customer level estimates can increase profits by 40–43% relative to promotions to customers identified as price sensitive using household level estimates. This suggests that intra-household heterogeneity is an important problem that cannot be wished away or ignored. Our study provides the first attempt to quantify the magnitude of this effect using a state-of-the-art model specification and estimation techniques.

References Allenby, G.M., Rossi, P.E.: Marketing Models of Consumer Heterogeneity. Journal of Econometrics 89, 57–78 (1999) Aribarg, Anocha, Neeraj Arora, H. Onur Bodur (2002) Understanding the Role of Preference Revision and Concession in Group Decisions. Journal of Marketing Research: August 2002, Vol. 39, No. 3, pp. 336–349 Aribarg, Anocha, Neeraj Arora and Moon Young Kang (2010), “Predicting Joint Choice Using Individual Data,” Marketing Science, 29 (1), 139–157 Arora, Neeraj (2006) “Estimating Joint Preference Using Data Imputation: A Sub-sampling Approach,” International Journal of Research in Marketing, Vol. 23, Issue 4, p. 409–418 Arora, N., Allenby, G.M.: Measuring the Influence of Individual Preference Structures in Group Decision Making. J. Mark. Res. 36, 476–487 (1999) Bucklin, R.E., Lattin, J.M.: “ A Two State Model of Purchase Incidence and Brand Choice,” Marketing Science. Winter 10(1), 24–39 (1991) Chenting, S., Fern, E.F., Ye, K.: A Temporal Dynamic Model of Spousal Family Purchase-Decision Behavior. J. Mark. Res. 40(3), 268–281 (2003) Chintagunta, P.K., Jain, D.C., Vilcassim, N.J.: Investigating Heterogeneity in Brand Preferences in Logit Models for Panel Data. J. Mark. Res. 28(November), 417–428 (1991) Corfman, K.P., Lehmann, D.R.: Models of Cooperative Group Decision-Making and Relative Influence: An Experimental Investigation of Family Purchase Decisions. Journal of Consumer Research 14(1), 1–13 (1987) Dube, J.P., Hitsch, G.J., Rossi, P.E.: State Dependence and Alternative Explanations for Consumer Inertia. RAND Journal of Economics 41(3), 417–445 (2010) Dube, J.P., Hitsch, G.J., Rossi, P.E., Vitorino, M.A.: Category Pricing with State-Dependent Utility. MarketingScience 27(3), 417–429 (2008) Erdem, T.: A Dynamic Analysis of Market Structure based on Panel Data. Mark. Sci. 15(4), 359–378 (1996) Givon, M.: Variety Seeking through brand switching. Mark. Sci. 3(1), 1–22 (1984)

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Gonul, F., Srinivasan, K.: Modeling Unobserved Heterogeneity in Multinomial Logit Models: Methodological and Managerial Implications. Mark. Sci. 12, 213–229 (1993) Guadagni, P.M., Little, J.D.C.: A Logit Model of Brand Choice. Mark. Sci. 2(Summer), 203–238 (1983) Heckman, J.J. (1981). “Statistical Models for Discrete Panel Data.” in C.F. Manski and D. McFadden, eds., Structural Analysis of Discrete Data with Econometric Applications. Cambridge, Mass.: MIT Press Jain, D.C., Vilcassim, N.J., Chintagunta, P.K.: A Random-Coefficients Logit Brand-Choice Model Applied to Panel Data. Journal of Business and Economic Statistics 12(3), 317–328 (1994) Kamakura, W., Russell, G.: A Probabilistic Choice Model for Market Segmentation and Elasticity Structure. J. Mark. Res. 26(November), 379–390 (1989) Menasco, M.B., Curry, D.J.: Utility and Choice: An Empirical Study of Wife/Husband Decision Making. Journal of Consumer Research 16(1), 87–97 (1989) Murthi, B.P.S., Srinivasan, K.: Consumers’ Extent of Evaluation in Brand Choice. J. Bus. 72(2), 229–256 (1999) Rossi, P., Allenby, G.: A Bayesian Approach to estimating Household Parameters. J. Mark. Res. 30(2), 171–182 (1993) Rossi, P., Allenby, G.: Bayesian Statistics and Marketing. Mark. Sci. 22(3), 304–328 (2003) Seetharaman, P.B., Ainslie, A.K., Chintagunta, P.K.: Investigating household state dependence effects across categories. J. Mark. Res. 36(4), 488–500 (1999) Seetharaman, P.B., Chintagunta, P.K.: A Model of Inertia and Variety-seeking with marketing variables. Int. J. Res. Mark. 15, 1–17 (1998) Sha Yang, Yi Zhao, Tülin Erdem, Ying Zhao (2010) Modeling the Intrahousehold Behavioral Interaction. Journal of Marketing Research: June 2010, Vol. 47, No. 3, pp. 470–484 Winer, R.S.: A Reference Price Model of Demand of Frequently-Purchased Products. Journal of Consumer Research 13(September), 250–256 (1986)

What Are the Main Levers to Convert Occasional and Non-buyers into Regular Buyers of Private-Label Brands? Samy Belaid1(B) , Sedki Karoui2 , Jérôme Lacoeuilhe3 , and Dorsaf Fehri4 1 EM Normandie Business School, Métis Lab, Le Havre, France

[email protected]

2 Associate Professor of Marketing. Laboratoire de Recherche en Marketing, Sfax University,

Sfax, Tunisia [email protected] 3 Université Paris-Est Créteil - IUT Sénart-Fontainebleau, Fontainebleau, IRG, France [email protected] 4 Marketing Consultant, Paris, France

Abstract. This study aims to identify the determinants of purchasing private label brands (PLBs) by non-buyers and occasional buyers. This study aims to identify levers that may raise the purchasing rate of non and occasional Private Label Brands (PLBs) buyers. We conducted online quantitative research with 151 customers from a panel attending one of the French retail chains. In addition, we used PLS-SEM to test the research model. The findings show that attitude toward PLBs plays a central role in converting non and occasional PLBs buyers into more regular buyers. Furthermore, theoretical contributions and managerial implications are developed. Keywords: Private Label Brand · Attitude · Trust · Occasional Buyers

1 Introduction The European market share of Private label brands (PLB) is around 30% (Nielsen et al., 2020, in PLMA’s 2021 International Private Label Yearbook). To identify the levers that explain PLB consumption, a dedicated research stream has focused on exploring and evaluating the determinants of buying PLBs’ underlying attitude (Belaid and Lacœuilhe 2018) and purchase intention as indicators of buying behavior (Richardson et al 1994; Rubio et al 2017; Liu et al 2018). The study of the different motives influences the intention to purchase PLBs shows that, on the one hand, research focuses on physiological and social-psychological needs (e.g., prestige and recognition) and, on the other hand, studies developing and testing models that mobilize perceptual factors. It mainly concerns consumer trust in PLBs, quality, perceived value, price sensitivity, brand sensitivity, ordinary resistance, and being a “smart shopper” (Belaid and Lacœuilhe 2018). The focal point of these studies lies in focus on purchase intention, given its ability to explain, to a large extent, future purchase behavior. Nevertheless, we sometimes © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. C. Gázquez-Abad et al. (Eds.): NB&PL 2023, SPBE, pp. 13–22, 2023. https://doi.org/10.1007/978-3-031-32894-7_2

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observe discrepancies between declarative and actual behavior (Chandon et al. 2005; Jiménez Torres and S. San Martín Gutiérrez, , 2007This gap is a fundamental bias that can affect demand forecasts. Despite the grievances associated with purchase intention, little research has investigated the impact of PLB purchase levers on actual behavior (purchase vs. non-purchase). In addition, purchase frequency is a factor that has more impact on sales volume growth (market shares) than spending on PLBs (Sethuraman and Gielens 2014; Martinelli and Di Canio 2021). While regular buyers of PLBs are loyal and guarantee to maintain market share, their propensity to purchase more remains lower than that of occasional and non-buyers if converted to buying PLBs. Transforming these two groups into regular buyers by activating impactful levers would increase PLB market shares. Thus, this research aims to study the effect of the main levers identified by the literature (i.e., trust in the brand, price sensitivity, perceived difference between brands, and attitude towards PLBs) on the actual purchasing behavior of PLBs while focusing only on none and occasional buyers. The following section reviews the existing literature to develop the conceptual framework and research hypothesis further. The methodology and key findings are then presented. Finally, the conclusion, limitations, and potential future research directions are developed.

2 Literature Review 2.1 The perceptual determinants of private label brands purchases. Purchase frequency is a powerful lever influencing PLBs’ market share (Sethuraman and Gielens 2014). Focusing only on regular PLBs buyers means discarding a nonnegligible proportion of potential PLBs consumers, who could be tempted to become more frequent buyers, to increase PLBs’ market share Truong et al. (2021). Based on these developments, the decision of occasional buyers to buy (or not) PLBs is influenced by determinants whose significance and importance are specific to them. 2.2 Price sensitivity as a determinant of attitude towards PLBs and actual purchase. Consumers’ final decision to buy or not a product is associated with their sensitivity to the price of this product. This directly impacts consumer purchasing attitudes Moore and Carpenter (2010) by negatively influencing them (Kacen and Lee 2002). Thus, the price remains an important determinant of the choice of PLBs (Burton et al 1998; Ailawadi et al., 2001). Based on the previous reasoning, the following two hypotheses could be suggested: - H1a: There is a statistically significant negative relationship between price sensitivity and attitude towards PLBs. - H1b: Consumer price sensitivity is positively related to PLBs consumption.

What Are the Main Levers to Convert Occasional

15

2.3 Trust in distributors is an antecedent of attitude and actual purchasing behavior. Trust is a critical variable in any relationship and an antecedent of commitment to a given brand (Richardson et al., 1994), particularly PLBs (Lacœuilhe et al., 2017; Rubio et al. 2017). It also significantly predicts customer attitudes and future purchasing behavior, including buying PLBs (Garbarino and Johnson 1999; Wu and Wang 2005; Bernard and Gifford 2006). Thus, the following two hypotheses could be established: - H2a: Trust in the Brand Has a Positive Influence on Attitude Towards PLBs - H2b: Trust in the Brand Has a Positive Influence on the Actual Purchase of PLBs 2.4 Attitude toward private labels as a determinant of the effective purchase of private labels. In the study of the determinants of PLBs purchases, attitude is traditionally defined as the orientation toward this type of brand (Beylier et al. 2012; Belaid and Lacœuilhe, 2018). As with any other brand, a positive attitude towards a particular PLB increases its purchase intention (Burton et al. 1998; Calvo-Porral and Lang 2015). Purchase intention is a predictor of the act of purchase. According to the theory of planned behavior (Ajzen 1991), attitudes are the main determinants of human behavior. Thus, we could hypothesize the following: - H3: Attitude positively influences the actual purchase of PLBs. 2.5 The perceived difference between brands as a determinant of the purchase of a PLB. Previous studies have shown that consumers choose national brands (NBs) more than PLBs when dealing with high perceived-risk products (Manikandan 2020). Now, the perception towards PLBs has changed from a simple alternative to NBs, to a real competitor in their own right (Bauner et al. 2019; Xu 2019). Goldsmith et al. (2010) show that PLBs buyers perceive an equivalent performance between NB and PLBs, while NB buyers think they have better qualities. The propensity to opt for PLBs is greater when consumers perceive a similarity between brands (NBs vs. PLBs) within a product category (Sprott and Shimp 2004; Olson 2012). Then, it could be assumed that: - H4: The Perceived Difference Between Brands Negatively Influences the Purchase of PLBs.

3 Research Design 3.1 Sample study. An online questionnaire was sent to a panel of 5,000 individuals who are customers of at least one of the following French supermarket chains: Carrefour, Casino, Leclerc, and Intermarché. We collected 479 questionnaires, from which we selected only none and occasional buyers of PLBs, thanks to a filter question. Thus, 151 usable questionnaires

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were collected. However, we ensured that these consumers are used to purchasing the categories under study, such as fruit juices and cosmetics PLBs. A description of the sample is presented in table 1. Table 1. Descriptive statistics of the sample Variable

Frequency

Percentage

72 79

47.7 52.3

48 103

31.8 68.2

2 68 75 6

1.3 45 49.7 4

67 84

44.4 55.6

Product: Cosmetic Jus Actual Buying behavior: Purchasing Not purchasing Age: [0–20] [21–40] [41–60] [61 +] Gender: Men Women

3.2 Scales measures. To reach the research objective, several measurement scales showing good psychometric properties were selected from the literature. The seven-point Likert-type scale assessed all items, ranging from strongly disagree (1) to agree (7) strongly. Table 2 shows the measurement scales used, along with their references”. Table 2. Measurement scales Construct

Scale

Price sensitivity

Batra and Sinha, (2000)

Trust in the retail chain

Kaabachi, (2005)

Attitude towards private labels

Belaid and Lacoeuilhe, (2015)

The Y final target variable in the model is a dummy one; it shows two actual opposed actions toward PLB in the market, purchasing (Y = 1) or not purchasing them (Y = 0).

What Are the Main Levers to Convert Occasional

17

Table 3. Reliability and Convergent Validity Test Item’s loading

Cronbach’s α

CR

AVE

Attitude

0.861 0.938 0.937 0.910

0.932

0.952

0.832

Perceived difference

0.937 0.567 0.706 0.561

0.777

0.813

0.530

Price sensitivity

0.843 0.876 0.814

0.801

0.882

0.713

Trust

0.924 0.904 0.871 0.823 0.885

0.929

0.946

0.778

Table 4. Discriminant validity test Attitude

Buying behavior

Perceived difference

Buying behavior

0.701

Perceived difference

0.113

0.085

Price sensitivity

0.667

0.475

0.144

Trust

0.664

0.536

0.137

Price sensitivity

0.476

4 Data Analysis, a Dummy Dependent Variable in PLS-SEM 4.1 Outer measurement model assessment. This first step in the PLS-SEM method involves testing the measurement model for reliability, convergent and discriminant validity (Hair et al. 2014). Results indicated (Tables 3 and 4) that all obtained values for our model were within the recommended and acceptable ranges for internal consistency and construct validity presence (Ringle et al. 2020; Hair et al. 2019). 4.2 Hypotheses testing and result interpretation. For this second step of PLS-SEM analysis, structural paths’ significance was tested via a bootstrap analysis, where a linear link is considered statistically significant if the associated student’s t is above 1.95 and its p-value is less than 0.05 Sarstedt and C. M. and J. F. Hair (2017).

18

S. Belaid et al. Table 5. Hypotheses testing

Path link:

Hyp Stand coeff Stand. Devi t Statistics p values Conclusion

Attitude - > Buying behavior

H3

0.257

0.037

6.953

0.000

Accepted

Perceived difference - > H4 Buying behavior

-0.009

0.031

0.287

0.774

Rejected

Price sensitivity - > Attitude

H1a

0.392

0.067

5.842

0.000

Accepted

Price sensitivity - > Buying behavior

H1b

0.022

0.031

0.719

0.472

Rejected

Trust - > Attitude

H2a

0.457

0.063

7.301

0.000

Accepted

Trust - > Buying behavior

H2b

0.069

0.031

2.211

0.027

Accepted

Table 6. indirect effects testing. Path link:

Stand coeff

Stand. Devi t Statistics p value Conclusion

Price consciousness - 0.118 > Attitude - > Buying behavior

0.025

4.764

0.000

Full mediation

Trust - > Attitude > Buying behavior

0.023

4.415

0.000

Partial mediation

0.101

4.2.1 Inner model assessment. Inner model quality was assessed regarding its level of predictive ability. This could be established by simply checking the coefficient of determination R2 and the crossvalidated redundancy Q2 (Hair et al. 2014; Sarstedt and C. M. and J. F. Hair, , 2017. Path analysis indicated that our model expresses a satisfactory explanatory and predictive power level with an R2 = 0.527, Q2 = 0.475 for the Attitude endogenous variable, and an R2 = 0.475, Q2 = 0.526 for the model’s final target construct (Hair et al. 2019). 4.2.2 Mediation analysis. PLS-SEM uses a bootstrap technique to test mediation or the indirect effects between two latent variables. This method has been considered more robust than classical methods like the Sobel test (Nitzl et al. 2016). The results showed that Attitude mediates the relationship between Price sensitivity and buying behavior and the relationship between Trust and buying behavior; the first is a full mediation, while the latter is a partial mediation (Nitzl et al. 2016).

What Are the Main Levers to Convert Occasional

19

4.2.3 The importance-performance map analysis (IPMA). IPMA or priority map analysis is a “useful analysis approach in PLS-SEM that extends the standard results reporting of path coefficient estimates by adding a dimension that considers the average values of the latent variable scores” (Ringle and Sarstedt 2016, p. 1865). IPMA contrasts the total effect of the predecessors of a specific target construct (importance) with their average latent variable scores with the aim which identify predecessor among them has the higher importance and the lower performance for managerial purposes (Ringle and Sarstedt 2016; Hair et al. 2019). As expected, the PLS-IMPA showed that Attitude towards private labels is our sample’s most important predecessor for the PLB’s actual purchasing behavior, followed by Trust in the retail chain. However, priority should be given to Attitude first, as it has the highest importance on Y’s PLB purchasing behavior with an unstandardized total effect = 0.257 and, respectively, a low-performance level in contrast to all other Y’s predecessor variables (Fig. 1, map1). This means that this variable has a longer marge of improvement while exerting greater importance over the PLB purchasing behavior. A one-unit increase in Attitude’s performance would increase the performance of the PLB’s actual purchasing behavior by 0.257 points. Therefore if we want to increase the purchasing odds of PLB within the occasional or the non-PLB purchasers’ population, we need to focus on improving the performance of aspects captured by the Attitude towards private labels, and then comes the Trust in the retail chain. The second IP map (Fig. 2) could add more details; PLB managers have to increase the performance captured by Attitude’s indicators, particularly the Atti3, Atti4, and Atti1 as showing some field of performance improvement, the Trust indicators, primarily the trust5, may follow (Fig. 2). As such, they are improving the “Quality/Price” ratio, and reducing the packaging elements at a tiny rate could be very useful. Moreover, managers have to implement actions in the market that increase trust in the retail chain.

Fig. 1. IPMA at latent variables level (unstandardized effects).

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Fig. 2. IPMA at indicators level (unstandardized effects).

5 Conclusion This research aims to identify the significant levers likely to convert non-buyers and occasional private-label buyers into regular buyers. Findings show that attitude mediates the relationship between antecedents and the actual purchase behavior of PLBs for their none and occasional buyers. Trust in the retail chain has a halo effect and positively affects the attitude toward the PLBs, which, in turn, influences their purchasing behavior. From the managerial point of view, to foster more willingness toward PLB among them, retailers have to work on improving the quality of their products while using more functional packaging that conveys a fair-trade stereotype in the mind of these consumers. Moreover, improving promotions such as using multichannel may contribute to raising their trust in the retail chain and ultimately render them less reluctant toward PLB. The limits of this work concern the size and nature of the sample, which is made up only of non-buyers and occasional buyers. Future research can, on the one hand, request a larger sample and, on the other hand, compare the predictors impacting the purchase of PLB by referring to the following three categories: regular buyers, occasional buyers, and non-buyers.

References Ailawadi, K.L., Neslin, S.A., Gedenk, K.: Pursuing the value-conscious consumer: Store brands versus national brand promotions. J. Mark. 65(1), 71–89 (2001) Ajzen, I.: The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 50(2), 179–211 (1991) Bauner, C., Jaenicke, E., Wang, E., Wu, P.C.: Couponing strategies in competition between a national brand and a private label product. J. Retail. 95(1), 57–66 (2019) Belaid, S., Lacœuilhe, J.: Les motivations d’achat et les leviers pour redynamiser l’offre des marques de distributeurs cœur de gamme. Décisions Mark. 2, 75–89 (2018) Bernard, J.C., Gifford, K.: Consumer willingness to pay premiums for non-GM and organic foods. Consum. Interes. Annu. 52, 343–354 (2006) Beylier, R.P., Messeghem, K., Fort, F.: Rôle des MDD de Terroir dans la Construction de la Légitimité des Distributeurs, le Cas Reflets de France. Décisions Mark. 66(2), 35–46 (2012)

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Burton, S., Lichtenstein, D.R., Netemeyer, R.G., Garretson, J.A.: A scale is measuring attitudes toward private label products and examines their psychological and behavioral correlates. J. Acad. Mark. Sci. 26(4), 293–306 (1998) Garbarino, E., Johnson, S.: The different roles of satisfaction, trust and commitment in customer relationships. J. Mark. 63(2), 70–87 (1999) Goldsmith, R.E., Flynn, L.R., Goldsmith, E., Stacey, E.C.: Consumer attitudes and loyalty towards private brands. Int. J. Consum. Stud. 34(3), 339–348 (2010) Hair, J., Risher, J.J., Sarstedt, M., Ringle, C.M.: When to use and how to report the results of PLS-SEM. Eur. Bus. Rev. 31(1), 2–24 (2019) Hair, J., Jr., Sarstedt, M., Hopkins, L., Kuppelwieser, G.V.: Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. Eur. Bus. Rev. 26(2), 106–121 (2014) Jiménez Torres, N.H., Martín Gutiérrez, S.S.: The purchase of foreign products: The role of firm’s country-of-origin reputation, consumer ethnocentrism, animosity, and trust. Documento de trabajo 13/07, Department of Economics and Business Administration, Universidad de Burgos (2007) Kacen, J.J., Lee, J.A.: The Influence of Culture on Consumer Impulsive Buying Behavior. J. Consum. Psychol. 12, 163–176 (2002) Kock, N.: Common method bias in PLS-SEM: A total collinearity assessment approach. Int. J. e-Collab. 11(4), 1–10 (2015) Lacœuilhe, J., Louis, D., Lombart, C.: Impacts of product, store, and retailer perceptions on consumers’ relationship to the terroir store brand. J. Retail. Consum. Serv. 39, 43–53 (2017) Liu, R.L., Sprott, D.E., Spangenberg, E.R., Czellar, S., Voss, K.E.: Consumer preference for national vs. private brands: The influence of brand engagement and self-concept threat. J. Retail. Consum. Serv. 41, 90–100 (2018) Martinelli, E., De Canio, F.: Are you purchasing veg private labels? A comparison between occasional and regular buyers. J. Retail. Consum. Serv. 63, 102748 (2021) Mieres, C.G., Martin, A.M.D., Gutiérrez, J.A.T.: Antecedents of the difference in perceived risk between store brands and national brands. Eur. J. Mark. 40(1/2), 61–82 (2006) Moore, M., Carpenter, J.M.: A decision tree approach to modeling the private label apparel consumer. Mark. Intell. Plan. 28(1), 59–69 (2010) Nielsen, C.F., Holtzapple, R., Di Piazza, A., Wistisen, T.N.: Status in 2021 and request for eamtime in 2022 for CERN NA63 (No. CERN-SPSC-2020–016) (2020) Nitzl, C.R., J. L., and G. Cepeda,: Mediation analysis in partial least squares path modeling: Helping researchers discuss more sophisticated models. Ind. Manag. Data Syst. 116(9), 1849– 1864 (2016) Olson, E.L.: Supplier inferences to enhance private label perceptions. J. Bus. Res. 65(1), 100–105 (2012) Richardson, P.S., Dick, A.S., Jain, A.K.: Extrinsic and intrinsic cue effects on perceptions of store brand quality. J. Mark. 58(4), 28–36 (1994) Ringle, C.M., Sarstedt, M.: Gain more insight from your PLS-SEM results: The importanceperformance map analysis. Ind. Manag. Data Syst. 116(9), 1865–1886 (2016) Ringle, C.M., Sarstedt, M., Mitchell, R., Gudergan, S.P.: Partial least squares structural equation modeling in HRM research. Int. J. Human Resour. Manage. 31(12), 1617–1643 (2020) Rubio, N., Villaseñor, N., Yagüe, M.J.: Creation of consumer loyalty and trust in the retailer through store brands: The moderating effect of choice of store brand name. J. Retail. Consum. Serv. 34, 358–368 (2017) Sarstedt, M.R., C. M. and J. F. Hair,: Partial least squares structural equation modeling. Handbook Mark. Res. 26(1), 1–40 (2017) Sethuraman, R., Gielens, K.: Determinants of store brand share. J. Retail. 90(2), 141–153 (2014)

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Does Retailer Activism Increase Consumers’ Perception of Private Label Brand Equity? Mario D’Arco(B) , Vittoria Marino, and Riccardo Resciniti Department of Law, Economics, Management and Quantitative Methods (DEMM), University of Sannio, Benevento, Italy {madarco,vittoria.marino,resciniti}@unisannio.it

Abstract. Consumers increasingly demand that brands take public stance on controversial and polarized issues. Assuming the perspective of marketing practitioners, brand activism is considered a relevant strategy to satisfy a request of this type. In the branding literature, activism has been prevalently investigated with regards to national brands (NBs). This research explores the world of private label (PL) brands. Particularly, it raises questions on whether consumers’ perception of PL brand equity could benefit from retailer activism. To this end, an explorative experiment is conducted. The experiment evaluates the presence (vs. absence) of activism attributes in three contexts: NB vs. premium PL brands vs. economy PL brands. These findings suggest that in the “activism attributes: present” condition participants indicated higher perceived brand equity for the premium PL brand. In the “activism attributes: absent” condition NBs were evaluated higher. Keywords: Private label brands · Consumer-brand identification · Controversial issues · Ethics · Purpose-driven retail

1 Introduction Even more people are demanding that business entities (i.e., companies, brands, CEOs, and retailers) take a public stance on controversial and polarized issues concerning the social, economic, political, legal, and environmental spheres (Hoppner and Vadakkepatt, 2019; Nalick et al., 2016; Rim et al., 2020; Sarkar and Kotler, 2018). Considering this new market trend, business entities around the world are increasingly engaging in activism strategies, such as brand activism (Moorman, 2020; Sarkar and Kotler, 2018; Vredenburg et al., 2020), CEO activism (Rumstadt and Kanbach, 2022), and corporate activism (Eilert and Nappier Cherup, 2020). According to the Edelman Trust Barometer Special Report (2021), consumers are more likely to buy those brands they fully trust and that address sociopolitical issues, create positive change in society, and contribute to make the world a better place. Hence, the activist role of companies, brands, CEOs, and retailers may generate new market opportunities (Gambetti and Biraghi, 2022) and positively affect consumer-based brand equity (Vredenburg et al., 2020). In the branding literature, activism has been prevalently investigated with regards to NBs, such as Ben & Jerry’s (Sibai et al., 2021), Patagonia (Mirzaei et al., 2022), Nike © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. C. Gázquez-Abad et al. (Eds.): NB&PL 2023, SPBE, pp. 23–30, 2023. https://doi.org/10.1007/978-3-031-32894-7_3

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(Moorman, 2020), and General Mills (Dauvergne, 2017), just to name a few. Our study position activism in the theoretical and practical debate on PL brands, namely, products thar are sold and marketed by a retailer under its own name or a brand name created by a retailer (Kumar and Steenkamp, 2007). Historically, PL brands, also called as store brands or private brands, utilize a pricequality value proposition (Abril and Rodriguez-Cánovas, 2016). However, as Gielens et al. (2021) pointed out, nowadays if PL brands do not want lose ground to NBs, they should be in line with current market trends and meet different customer needs, especially those of the younger generations, such as Millennials and Generation Z, who do not care only about price and product quality, but also about sustainability, ethics, and companies’ social commitment (Casalegno et al., 2022; Djafarova and Foots, 2022). Unlike NBs, PL brands are totally controlled by the retailer rather than the manufacturer. This means that retailers play a key role in creating PL brand equity (Abril and Rodriguez-Cánovas, 2016). Particularly, retailers take decisions about product positioning, suppliers and manufacturers, product and package design, pricing, shelf placement, and promotions (Morton and Zettelmeyer, 2004; Wu et al., 2021). Since PL brands are sold exclusively in their retailers it is worth exploring whether the presence of retailer attributes, such as engage in activism (hereafter retailer activism), may contribute to an increase of consumer-based PL brand equity. Research on the topic is scarce. Hence, to fill this gap we conducted an exploratory study using experimental design. This approach is suggested when theoretical frameworks are still under construction (Colaço, 2018). The remainder of the paper is structured as follows. First, we present the theoretical background. Second, a methods section is introduced. Third, we illustrate the study’s findings. The paper concludes with a discussion of the implications for theory and practice, as well as limitations and future research directions.

2 Theoretical Background Brand activism is defined as the “business efforts to promote, impede, or direct social, political, economic, and/or environmental reform or stasis with the desire to make improvements in society” (Sarkar and Kotler, 2018, p. 554). This marketing strategy is different from corporate social responsibility (CSR), because the “brand adopts a nonneutral stance on institutionally contested sociopolitical issues, to create social change and marketing success” (Vredenburg et al., 2020, p. 446). Borrowing from the conceptualization of brand activism (e.g., Moorman, 2020; Vredenburg et al., 2020), we define “retailer activism” as a retailer’s public statement end/or action taken to direct stakeholders’ attention on sociopolitical issues that are partisan in nature. When retailers engage in activism, they need to pick a side to keep the status quo or make changes in the world. For example, a retailer can decide to advocate for or against racial justice, LGBTQIA + rights, gun control, climate change, and access to safe abortion. Aldi’s “Pollinator Policy Statement” and Dick’s Sporting Goods’ decision to stop selling guns represent two concrete examples of retailer activism. Drawing from cue utilization theory (Burnkrant, 1978; Jacoby et al., 1971), retailer activism may serve as an extrinsic cue and signal an additional benefit. Extrinsic cues are not an inherent part of the product. Consequently, brand name, price, packaging, and retailer attributes,

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such as being an activist, are all extrinsic cues. Intrinsic cues constitute attributes that are part of the physical product, such as function, shape and size, or ingredients of a food. Consumers can utilize both intrinsic and extrinsic cues to make inferences about brands or products evaluations. Furthermore, both intrinsic and extrinsic cues participate in the process of consumer-based brand equity construction (Wu et al., 2021). Conceptually, brand equity is the “added value” that a given product receives from the brand name (Farquhar, 1989). In terms of consumer perspective, brand equity “occurs when the consumer is familiar with the brand and holds some favourable, strong and unique brand associations in memory” (Keller, 1993, p. 2). Brand equity construction is the result of the combination of marketing mix elements and accumulated marketing investments into the brand (Keller et al., 1998; Yoo and Donthu, 2001 For example, NBs employ advertising as one of the tools to create and preserve brand equity (Karray and Martín-Herrán 2009). On the contrary PL brands do not advertise much their products (Gielens et al., 2021). According to earlier research, PL brands were perceived as products with low brand equity (Abril and Rodriguez-Cánovas, 2016; Ailawadi et al., 2003). However, over the years PL brands have growth and reached “a large number of countries, sectors, and product categories” (Cuneo et al., 2012, p. 428). Furthermore, thanks to specific marketing programs, such as the introduction of premium PL, consumers do not perceive PL brands as the “poor cousins” of NBs anymore (Gielens et al., 2021; PLMA Report 2020). Reviewing the extant literature, it is possible to note that the effects of retailer activism on PL brand equity construction are still unexplored. In the context of PL, other retailers’ attributes have been investigated. Mejri and Bhatli (2014) explored consumer responses towards social effort of retailers, such as introduction of organic labels and fair-trade offer in PL. Specifically, they found a positive association between social quality of PL brands and consumers’ loyalty intentions towards the brand and the retailer. Bodur et al. (2016) examined how ethical attributes, such as environmental protection, human rights, animal welfare, and social issues, improve evaluation of PL brands especially when they are associated with a higher price point or offered by retailers with high reputation. Based on the gaps emerged from the extant literature, this study aims to answer the following questions: Do consumers still view NBs as the better choice over PL brands when they evaluate the presence of retailer purpose- and value-driven activism? Does retailer activism increase PL brand equity? Does PL brands that are associated with high price (but not low) benefit from retailer activism?

3 Methods This study adopts explorative experimentation, that is, a type of experiment that does not test hypotheses (Due, 2022). We opted for this method because there is not enough theory to generate predictions of interest to the researchers (Colaço, 2018). Hence, the main objective of explorative experimentation consists of investigating in depth a given phenomenon by relying on the theoretical background (i.e., “representations and claims about a topic” (Colaço, 2018, p. 3)) rather than a local theory concerning “the behavior of the particular objects being measured” (Franklin, 2005, 891). To sum explorative

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experiments are “not designed to evaluate theories, but nonetheless involve theoretical direction” (Colaço, 2018, p. 5). To investigate the phenomenon of retailer activism and answer the research questions, we conducted an experiment during mid-December 2022. Participants were recruited outside the Lidl located in Castel San Giorgio, Salerno, Italy. Lidl is a German international discount retailer chain. It offers both PL and NB products at affordable prices. To select the specific product for the experiment, we conducted a pretest. We asked participants (n = 100) to choose among multiple options their preferred product category sold at Lidl. The findings revealed that snacks (e.g., bar of chocolate, packet of cookies, and chips) are bought more frequently (20%), followed by dry goods (16%) (e.g., pasta, tea, sugar, and flour), and frozen goods (15%) (e.g., frozen vegetable, pizza, and ice creams). Considering the above results we selected chocolate bars as a product to create the experimental design. Particularly, the experiment consisted of a 2 (activism attributes: present vs. absent) × 3 (NB vs. Premium PL brand vs. Economy PL brand) between-subjects factorial design. Participants were randomly assigned to an experimental group. The experiment was framed as a purchase situation. Three chocolate brands with the same characteristics (i.e., milk chocolate) were used in the experiment: “Milka” represented the NB, “Deluxe” and “Fin Carré” constituted respectively the premium PL and the economy PL marketed by Lidl. We created a photo of the three products on a shelf. In each condition we presented the focal brand next to the others in order to simulate evaluations in a multi-brand retail context. In the “activism attributes: present” condition, we introduced the activism campaign performed by the retailer (e.g., Lidl gives support to millions of activists who are fighting to defend the environment) or the NB (e.g., Milka gives support to millions of activists who are fighting to defend the environment). We utilized consumer-based brand equity as the main dependent variable. Brand equity was measured on 7-point Likert scales (1 = “strongly disagree,” 7 = “strongly agree”) using four items adapted from Yoo and Donthu (2001) (e.g., It makes sense to buy this chocolate brand instead of any other brand, even if they are the same). Furthermore, a manipulation check was conducted to evaluate respondents’ comprehension of the activism action performed by the retailer or the NB. Specifically, the format of the manipulation check question was a check box question (e.g., What is the type of activism the retail X/the NB X engage in? Option 1 = Social activism; Option 2 = Economic activism; Option 3 = Environmental activism; Option 4 = Political activism).

4 Results A sample of 240 consumers (40 in each experimental condition) took part in the experiment (51.2% female and 48.8% male; age range 19–61, with an average of 33.38, standard deviation = 10.813) (see table 1). Cronbach’s Alpha was 0.939 for consumer-based brand equity measurement scale (four-items). Manipulation checks showed that only 1.3% (n = 3) of participants assigned to the condition “activism attributes: present” failed to respond correctly to the manipulation check. According to the results of the two-way ANOVA, the main effect of type of brand (NB vs. premium PL brand vs. economy PL brand) condition was significant (F(2, 234) = 28.089, P < 0.001, partial η2 = .194). Also, the main effect of activism

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Table 1. The characteristics of study population (n = 240). Category

Frequency

Percentage (%)

Education

Less than high school High school University graduate

12 142 86

5 59.2 35.8

Marital status

Single Married Others

39 138 63

16.3 57.5 26.3

Employment status

Formally employed Self employed Unemployed Students

128 46 17 49

53.3 19.2 7.1 20.4

Family annual income

e 19.000 or below e 20.000 – e 34.000 e 35.000 or above

97 104 39

40 43 16

Shopping frequency of Lidl customers

Several times a week Once a week Several times a month Once a month

51 110 74 5

21 46 31 2

Brand preference

National Brands Premium Private Labels Private Labels

75 97 68

31 40 28

attributes (present vs. absent) was significant (F(1, 234) = 11.738, p = .001, partial η2 = .049). Results also showed a significant interaction effect of type of brands and activism attributes (present vs. absent) (F(2, 234) = 7.887, P < 0.001, partial η2 = .063). Specifically, in the “activism attributes: present” condition participants indicated higher perceived brand equity for the premium PL brand (MNB = 4.963; Mpremium PL brand = 5.450; Meconomy PL brand = 3.937). In the “activism attributes: absent” condition NBs were evaluated higher. See Fig. 1 for more details.

Consumer-based brand equity 6 5

4,963

4,888

5,45 4,306

4

3,937

3,856

3 2 1 0

NB

Premium PL brand

Activism attributes: present

Economy PL brand

Activism attributes: absent

Fig. 1. Impact of brand type and activism attributes on consumer-based brand equity.

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5 Conclusions Nowadays, consumers expect business entities to take public stances on controversial issues that impact their lives. This research examined to what extent and under what circumstances PL brands benefit from retailer activism. Results suggest that when retailer activism is absent NBs continue to be perceived more positively than PL brands because of their established quality and high consumer-based brand equity. Retailer activism enhances evaluations of a PL brand only when it is associated with high priced PL (i.e., premium PL brands). This finding may appear contradictory because regular consumers of PL brands perceive price as a powerful PL brand-equity component (Wu et al., 2021). However, it is also true that a low price may have a negative influence on product quality perceptions (Beneke and Zimmerman, 2014). Against this backdrop, retailer activism may serve as a diagnostic cue of product quality and leads to a more positive response to the higher price of premium PL brands. Specifically, this attribute may help consumers when they do not dispose of strong brand associations or are dealing with unknown brands. From a managerial standpoint, the findings offer implications for retailers on how to improve PL brand-equity construction. Since PL positioning is influenced by retailer positioning (Kapferer, 2008), the activist personality of the retailer may have a positive effect on PL brands. Hence, retailers should adopt specific marketing strategies (e.g., in-store advertising, public relations, and social media campaigns) to inform consumers about their activist statement and actions. Our results reveal that retail activism specifically enhances the perception of premium PL. It follows that retailers should develop a brand portfolio that contains more premium PL than economy PL. This study has several limitations. First, the experimental design precluded the examination of moderators and mediators. Therefore, future research could explore consumer ideology effect or other variables. Second, we focused only on a specific product category, that is, chocolate. In the future we can test other products (e.g., orange juice, milk, hand soap, etc.,) to verify if results are the same.

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Psychographic Clusters of Private Label Consumers Morana Fuduri´c1(B) , Sandra Horvat1 , Vatroslav Škare1 , and Ákos Varga2 1 Faculty of Economics and Business, University of Zagreb, Zagreb, Croatia

{shorvat,mfuduric}@efzg.hr

2 Department of Digital Marketing, Corvinus University of Budapest, Budapest, Hungary

[email protected]

Abstract. This paper identifies private label prone segments that emerge based on implicit and explicit private label/manufacturer brand attitudes and brand preference. We describe these segments, measure the smart-shopper self-perception and value consciousness associated with segment membership, and quantify how these segments differ in their private label purchase intentions. We apply K-means cluster analysis to a database of 798 respondents in five emerging markets in the beauty care product category. Three consumer segments were identified and labeled as “Mindful”, “Indifferent” and “Skeptical”. Demographic characteristics of the three segments were also observed. Results indicate the highest potential of the “Mindful” segment for the retailers managing private labels, which also represent the largest cluster in the sample. Keywords: Private label · Manufacturer brand · Psychographic segmentation · Brand attitude · Smart-shopper · Value consciousness

1 Introduction Private labels (PL) remain an important strategy for retailers in different product categories, with their management continuously evolving (Gomez-Suarez et al., 2016). Gielens et al (2021) suggest that, despite their successful development, PLs should not strive to become like manufacturer brands (MB) but rather pursue a smart positioning approach, which would enable them to attract different consumer segments focused more on value and smart decisions (Gielens et al, 2021). Therefore, understanding PL prone consumers is of particular interest (Wu et al., 2021) since in-depth insight in the PL consumer behavior is necessary for adjusting marketing strategies for both retailers and manufacturers. While there is extant research on PL prone consumers, consumer attitudes towards PLs have changed over the years, partially due to the general development and penetration of PLs in different product categories and markets. In light of these developments, the key question is how can we segment consumers based on their attitudes and perceptions of PLs. In this paper we propose a novel approach in which we combine psychographic variables (value consciousness and smart shopper self-perception) with two different © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. C. Gázquez-Abad et al. (Eds.): NB&PL 2023, SPBE, pp. 31–37, 2023. https://doi.org/10.1007/978-3-031-32894-7_4

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attitudinal measures (explicit and implicit) and purchase intention. Selected psychographic variables are the reflection of increasing retailer focus on smart PLs as proposed by Gielens et al (2021). On the other hand, a combination of explicit and implicit attitude measures contributes to a better comprehension of consumer brand attitudes (Fuduri´c et al, 2022). More specifically, we aim to answer the following research questions: (RQ1) How can we segment PL consumers based on their implicit and explicit brand attitudes and preferences?; (RQ2) How PL prone segments differ in their smart-shopper self-perception, value consciousness and PL purchase intention?

2 Literature Review PLs are considered successful when consumers express a positive attitude towards them and consequently intention to purchase (Bao, Bao and Sheng, 2010). A positive relationship between explicit attitudes to PLs and their intentions or actual purchases has been confirmed by numerous empirical studies (Burton et al., 1998; Garretson, Fisher and Burton, 2002; Jin and Suh, 2005; Kwon, Lee and Kwon, 2008). (Burton et al. 1998) define attitude towards PLs as “a predisposition for a favorable or unfavorable response in relation to product appraisal, purchase appraisal and self-appraisal related to private label products”. In this respect, the attitude towards PLs constitutes a relatively durable construct that is broad enough to be generally used in different product categories. However, PL attitudes have typically been measured using explicit, self-reported measures that exhibit certain methodological shortcomings (Fuduri´c et al, 2022) so implicit attitudes are increasingly used as a way to accommodate these (McGuire and Beattie, 2019). Implicit attitudes have been defined as “actions or judgments that are under the control of automatically activated evaluation, without the performer’s awareness of that causation” (Greenwald et al, 1998: 1464). Furthermore, Fuduri´c et al (2022) argue that measuring implicit and explicit PL attitudes contributes to a better understanding of consumer behavior in the context of brand preference. In line with this finding, we included implicit and explicit attitudes as our key segmentation variables, as well as purchase intention as the main dependent variable in our research. To the best of our knowledge, implicit attitudes were not used in the research on segmentation of PL prone consumers. Research on PL prone consumers has shifted from socio-demographic to psychographic and behavioral characteristics (Martínez and Montaner, 2008). Psychographic variables can provide insights into consumer behavior that are not readily apparent from demographic data alone and provide more nuanced insights for PL positioning (Larson, 2018). In line with the new direction of PL positioning we examine value consciousness and smart shopper self-perception as psychographic variables that determine PL prone consumers (Brochado, Marques and Mendes 2015; Kumar and Chandra, 2019; Gielens et al, 2021). The smart-shopper self-perception is related to consumers’ need for an internal gratification as a result of price savings during the purchase (Reid, 2007). It is basically driven by ego and the psychological need for approval and a sense of achievement whose degree varies among consumers (Garretson, Fisher and Burton, 2002). According to Green Atkins and Kim (2012) smart shoppers are focused on expenditure minimization (time, money and energy) while obtaining hedonic and utilitarian value.

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Empirical research has shown that the smart-shopper self-perception positively affects PL attitudes (Burton et al., 1998; Manzur et al, 2011). (Burton et al. 1998) state that a positive attitude towards PLs suggests a careful customer who is proud of their ability to make smart purchase decisions. Value consciousness can be defined as the quality obtained for a certain price, which means that when making a purchase decision, consumers are not focused on quality in absolute form but in relation to the price of a particular brand (Jin and Suh, 2005). When making a purchase decision, such consumers compare the benefits of buying a product with the costs involved, indicating that the purchase decision is not impulsive but based on careful consideration of alternatives (Medina, Méndez and Rubio, 2004). When it comes to PLs, retailers increasingly communicate their high value, so it is not surprising that consumers’ value consciousness is often associated with buying or having positive attitudes toward PLs (Burton et al., 1998); Garretson, Fisher and Burton, 2002; Jin and Suh, 2005; Kwon, Lee and Kwon, 2008); Manzur et al, 2011) and PL purchase intention (Bao, Bao and Sheng, 2011).

3 Methodology The aim of this research is to identify consumer segments with similar attitudes and preferences towards PLs and MBs using cluster analysis, and determine whether these segments differ in their smart-shopper self-perception, value consciousness and PL purchase intention. We focus on beauty care products as they are frequently purchased and easily evaluated by consumers. Furthermore, research has shown that beauty care is one of the top three product categories with the highest growth in PL market shares across 18 European countries (PLMA, 2021). The study focuses on five countries in the Central and Eastern European (CEE) region, where PLs are less developed and underrepresented in research. The study compares Nivea (MB), to its PL counterpart, Balea. To measure implicit attitudes and preferences, we use the Implicit Association Test (IAT), which is widely used in social psychology to measure implicit biases (Greenwald, Nosek and Banaji, 2003). The IAT consisted of seven blocks, providing all combinations of target stimuli and attribute wordings. We followed a standard experimental protocol for the IAT, and participants completed the task consisting of 20–40 trials in each measurement block. We recorded participants’ response times in each measurement block, which were later used to calculate their preferences using the D-score algorithm. We also calculated the error rate following an analytical framework and set the IAT to drop participants with response times below 300 ms (Carpenter et al, 2019). Explicit attitudes towards PL and MB were measured using a 7-point, 4-item semantic differential scale, while Brand Preference was measured using a 5-point, 1-item scale (both adapted from Maison, Greenwald and Bruin, 2004). Smart-shopper self-perception was measured using a 5-point, 4-item scale adapted from Shukla, Banerjee and Adidam (2013). Value consciousness was measured using a 5-point, 7-item scale adapted from Bao, Bao and Sheng (2011). Finally, PL purchase intention was evaluated using a 5point, 3-item scale adapted from Netemeyer et al. (2004, in Calvo Porral and Lang, 2015). Scale reliability was evaluated using Cronbach’s alpha coefficient with values above 0.80 for all scales, which is above the recommendation for minimally acceptable

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reliability levels (Peterson, 1994). Data was collected using a general population quota sampling method based on age, gender, and country region. A total of 824 respondents from five countries (51.1% male, 20% Croatian, 19.3% Czech, 19.1% Hungarian, 23.1% Romanian, 18.6% Slovakian) participated in the study.

4 Results and Discussion The results indicate that the respondents both implicitly (M = 0.17, SD = 0.40) and explicitly have a more positive attitude toward MBs (M = 13.46, SD = 2.09) as opposed to PLs (M = 12.03, SD = 2.60). Relatedly, the perception of PLs is moderately positive (M = 17.96, SD = 3.76) with the respondents indicating a mild brand preference towards MBs as compared to PLs (M = 3.47, SD = 1,17). Finally, the respondents generally perceive themselves as value-conscious (M = 27.20, SD = 4.40) smart shoppers (M = 11.78, SD = 2.31). To identify customer segments based on implicit and explicit attitudes and perceptions toward MBs and PLs, we used a K-means cluster analysis with 10 iterations. We used the elbow method to determine the optimal number of clusters and ultimately accepted a three-cluster solution. The final cluster centers are presented in Fig. 1. Given the information in Fig. 1, we provide an interpretation of the clusters. Cluster 1, labeled the “Mindful”, represents the largest cluster, and has the highest potential for retailers managing PLs as they demonstrate positive attitudes towards both PL and MB and are most prone towards purchasing PL. Members of the “Mindful” cluster are dominantly women over 45 and with higher income levels who exhibit the highest level of value consciousness and smart shopper self-perception. Interestingly, this segment has the highest scores for MB attitude but only moderate score on implicit attitude measure indicating that their PL purchase intention could be the result of conscious and rational decision that PL offer higher value compared to MB. In line with changing consumer norms on what value means (Gielens et al 2021) PLs will have to step up their game in order to provide value these consumers perceive as adequate. The second cluster is labeled as “Indifferent”. Consumers in this cluster had less positive attitudes towards both PL and MB as well as a lower level of PL purchase intention compared to the “Mindful” cluster. It consists mainly of young men with average and above average income and the lowest level of value consciousness and smart shopper self-perception. Their explicit attitude levels towards PL and MB are quite similar (M = 10.85 vs. M = 10.48, respectively) but they exhibit the lowest level of implicit attitudes indicating they are implicitly more inclined to PL compared to other segments. The fact that they exhibit the lowest level of implicit attitudes and very similar scores on explicit attitude towards PL and MB indicates the potential to PL purchase if retailers manage to convince them that there are differences between the two types of brands. This is the youngest segment additionally strengthening its potential because it is more likely that their purchase preferences are still not firmly set. Finally, the third cluster labeled “Skeptical” had the lowest attitude toward PL and was least prone to purchasing PL. It consists mainly of men older than 45 with a moderate level of value consciousness and smart shopper selfperception compared to other segments. Their explicit PL attitude value is substantially lower than MB attitude and their implicit attitude score is the highest indicating strong

Psychographic Clusters of Private Label Consumers

35

preference for MB. Although they exhibit moderate levels of value consciousness and smart shopper self-perception they do not see PL as an optimal choice. One possible explanation could be that they put more emphasis on the quality aspect in the context of value so they believe that smart purchase is buying MBs on discount rather than PLs.

Cluster Mindful

Indifferent

Skeptical

ANOVA (F, p)

3

3

4

87.869, .000

.16

.11

.25

6.440, .002

Attitude Nivea

14.59

10.48

13.67

682.532, .000

Attitude Balea

13.89

10.85

8.98

792.656, .000

432

170

196

Age 18-34 (%) 35-45 (%) 45 and over (%)

23.20% 23.90% 52.90%

47.34% 20.12% 32.54%

23.47% 21.43% 55.10%

Gender Male (%) Female (%)

45.37% 54.63%

58.58% 41.42%

55.90% 44.10%

Income Up to 1.062 EUR Over 1.062 EUR

46.06% 53.94%

40.83% 59.17%

45.92% 54.08%

Smart Shopper SelfPerception (SSSP)

12.22

10.93

11.60

21.369, .000

Value Consciousness (VC)

27.85

26.10

26.73

11.727, .000

PL Purchase intent. (PL PI)

12.44

11.07

9.48

112.206, .000

Brand preference (BP) Implicit attitudes (DScore)

Cluster size Demographics

Related variables

Note 1: BP (Min=1, Max=5); DScore (Min= -1.41, Max=1.38), PL and MB Attitudes (Min=3, Max=15; SSSP (Min=3, Max=15); VC (Min=7, Max=35); PL PI (Min=3, Max=15) Note 2: Post Hoc tests conducted using the Tukey HSD indicate significant differences between clusters for all variables with the exception of Clusters 2 and 3 for Value Consciousness.

Fig. 1. Consumer clusters

36

M. Fuduri´c et al.

5 Conclusion We use a K-means cluster analysis on a sample of 798 respondents in the beauty care product category, and identify three segments labeled as “Mindful”, “Indifferent”, and “Skeptical”. This study sheds light on the importance of understanding the psychographic and behavioral factors that influence PL consumer behavior in emerging markets. It indicated that retailers should direct their marketing efforts to additionally position their PLs as brands that offer good value products and that are a smart purchase choice. The results have implications for retailers and manufacturers in terms of adjusting PL strategies to better cater to the needs of different segments. Future research could further investigate the identified segments and their behavior in different product categories and markets. Also it would be interesting to get a more in-depth understanding of the potential differences in the perception of smart-shopping and value concept between consumers.

References Bao, Y., Bao, Y., Sheng, S.: Motivating purchase of private brands: Effects of store image, product signatureness and quality variation. J. Bus. Res. 64, 220–226 (2011). https://doi.org/10.1016/ J.JBUSRES.2010.02.007 Brochado, A., Marques, S., Mendes, P.: Psychographic determinants of private-label adoption: a feasibility study in the Portuguese yogurt market. Tourism Manag. Stud. 11(1), 136–145 (2015) Burton, S., Lichtenstein, D.R., Netemeyer, R.G., Garretson, J.A.: A Scale for Measuring Attitude Toward Private Label Products and an Examination of Its Psychological and Behavioral Correlates. J. Acad. Mark. Sci. 26(4), 293–306 (1998). https://doi.org/10.1177/009207039826 4003 Calvo-Porral, C., Lang, M.: Private Labels: The role of Manufacturer Identification, Brand Loyalty and Image on Purchase Intention. Br. Food J. 17, 506–522 (2015). https://doi.org/10.1108/BFJ06-2014-0216 Carpenter, T.P., et al.: Survey-software implicit association tests: A methodological and empirical analysis. Behav. Res. Methods 51(5), 2194–2208 (2019). https://doi.org/10.3758/s13428-01901293-3 Fuduri´c, M., Varga, A., Horvat, S., Škare, V.: The ways we perceive: A comparative analysis of manufacturer brands and private labels using implicit and explicit measures. J. Bus. Res. 142, 221–241 (2022). https://doi.org/10.1016/j.jbusres.2021.12.033 Garretson, J.A., Fisher, D., Burton, S.: Antecedents of private label attitude and national brand promotion attitude: similarities and differences. J. Retail. 78, 91–99 (2002). https://doi.org/10. 1016/S0022-4359%2802%2900071-4 Gielens, K., et al.: The future of private labels: towards a smart private label strategy. J. Retail. 97(1), 99–115 (2021). https://doi.org/10.1016/j.jretai.2020.10.007 Gomez-Suarez, M., Quinones, M., Yagúe, M.J.: Store brand evaluative process in an international context. Int. J. Retail Distrib. Management 44(7), 754–771 (2016). https://doi.org/10.1108/ IJRDM-11-2015-0168 Green Atkins, K., Kim, Y.: Smart shopping: conceptualization and measurement. International Journal of Retail & Distribution Management 40(5), 360–375 (2012). https://doi.org/10.1108/ 09590551211222349

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Greenwald, A.G., McGhee, D.E., Schwartz, J.L.K.: Measuring individual differences in implicit cognition: The implicit association test. J. Pers. Soc. Psychol. 74(6), 1464–1480 (1998). https:// doi.org/10.1037/0022-3514.74.6.1464 Greenwald, A.G., Nosek, B.A., Banaji, M.R.: Understanding and using the implicit association test: I. An improved scoring algorithm. J. Pers. Soc. Psychol. 85(2), 197–216 (2003). https:// doi.org/10.1037/0022-3514.85.2.197 Jin, B., Suh, Y.G.: Integrating effect of consumer perception factors in predicting private brand purchase in a Korean discount store context. J. Consum. Mark. 22(2), 62–71 (2005). https:// doi.org/10.1108/07363760510589226 Kumar, S., Chandra, B.: Profiling consumers of private label brands in virtual retail environment - a cluster analytic approach. Int. J. Electron. Mark. Retail. 10(1), 26–44 (2019). https://doi. org/10.1504/IJEMR.2019.097073 Kwon, K.-N., Lee, M.-H., Kwon, Y.J.: The effect of perceived product characteristics on private brand purchase. J. Consum. Mark. 25(2), 105–114 (2008). https://doi.org/10.1108/073637608 10858846 Larson, R.B.: Profiling prospective private-label buyers. Int. Rev. Retail, Distrib. Consum. Res. 28(5), 516–530 (2018). https://doi.org/10.1080/09593969.2018.1525757 Maison, D., Greenwald, A.G., Bruin, R.H.: Predictive validity of the implicit association test in studies of brands, consumer attitudes, and behavior. J. Consum. Psychol. 14(4), 405–415 (2004). https://doi.org/10.1207/s15327663jcp1404_9 Manzur, E., Olavarrieta, S., Hidalgo, P., Farías, P., Uribe, R.: Store brand and national brand promotion attitudes antecedents. J. Bus. Res. 64(3), 286–291 (2011). https://doi.org/10.1016/ j.jbusres.2009.11.014 Martínez, E., Montaner, T.: Characterisation of Spanish store brand consumers. Int. J. Retail Distrib. Manage. 36(6), 477–493 (2008). https://doi.org/10.1108/09590550810873947 Medina, O., Méndez, J.L., Rubio, N.: Price-Quality and Market Share of Consumer Goods in Spain: Retail Brands and Manufacturer Brands. Ind. Rev. Retail Distrib. Consum. Res. 4(2), 199–222 (2004). https://doi.org/10.1080/0959396042000178197 Peterson, R.A.: A meta-analysis of Cronbach’s coefficient alpha. J. Consum. Res. 21(2), 381–391 (1994). https://doi.org/10.1086/209405 PLMA Private label today. Retrieved from https://www.plmainternational.com/ industrynews/private-label-today (2021) Reid, M.: Correlates of Grocery Store Brand Purchase Intent. Proceeding of the 36th EMAC Conference, Reykjavik, Island (2007) Shukla, P., Banerjee, M., Adidam, P.T.: The moderating influence of socio demographic factors on the relationship between consumer psychographics and the attitude towards private label brands. J. Consum. Behav. 12(6), 423–435 (2013). https://doi.org/10.1002/cb.1441 Wu, L., Yang, W., Wu, J.: Private label management: A literature review. J. Bus. Res. 125, 368–384 (2021). https://doi.org/10.1016/j.jbusres.2020.12.032

Consumer Behaviour

Blockchain-Enabled Banking Services and Customers’ Perceived Financial Well-Being: A Structural Nexus Maya F. Farah1(B) , Muhammad Naveed2 , and Shoaib Ali3 1 Adnan Kassar School of Business, Lebanese American University, Beirut, Lebanon

[email protected]

2 Department of Management Sciences, University of South Asia, Lahore, Pakistan

[email protected] 3 Air University School of Management, Air University, Islamabad, Pakistan

Abstract. Grounded in Transformative Service Research (TSR), this study aims to examine the mechanism by which blockchain-enabled banking determines customers perceived financial well-being (FW). We conclude that blockchain features augment information transparency, which in turn determines customers perceived financial well-being. Data was collected through a survey filled by 283 individuals having bank accounts. The contextual setting of the study was provided by commercial banks operating in Pakistan. The primary data was analyzed through PLSSEM to explore the direct and indirect relationships among blockchain features (efficiency, security, and regulatory compliance), perceived information transparency, and perceived financial well-being. The evidence points to the fact that: (1) Blockchain features are significant in determining customers’ financial wellbeing; (2) Information transparency mediates the relationship between these features and customers perceived financial well-being; hence, that (3) Bank managers who embrace the challenging task of improving the perceived financial well-being of their customers, should adopt blockchain technology to enhance information transparency, and accordingly to augment the customers’ financial well-being. Keywords: Blockchain · firm information transparency · perceived financial well-being

1 Introduction The emergence of Blockchain technology has been presaged as the next level of disruptive revolution that is believed to revitalize the banking and finance industry as it is expected to augment the efficiency and effectiveness of financial services. Blockchain is defined as the decentralized or distributed ledger, encompassing the technological infrastructure and protocols that enable recording, validation, and simultaneous access of data among multiple stakeholders (Frizzo-Barker et al. 2020). Blockchain stores financial data in blocks that can be shared between members. These can be added together in a chronological sequence that form a chain. Once shareholders share their confirmation, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. C. Gázquez-Abad et al. (Eds.): NB&PL 2023, SPBE, pp. 41–49, 2023. https://doi.org/10.1007/978-3-031-32894-7_5

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stored data cannot be changed. Stakeholders involved in a transaction act as nodes, and validation is performed through cryptography (Awad et al. 2018; Ammous 2018; Arsi et al. 2021; Bouri et al. 2021a; 2021b; 2022a; 2022b). Blockchain is the technology at the base of the cryptocurrency existence: it was created to support digital currencies without assisting in predicting the return of the latter (Shahzad et al. 2022b; Wang et al. 2022; Wen et al. 2022), nor guiding customers’ individual trading strategies (Shahzad et al. 2021a; 2022a). Blockchain-enabled banking is likely to provide stakeholders a transactional platform with greater security, transparency, speed, efficiency, and shared record, which can enhance customer trust. The effect of the COVID-19 pandemic on the price and trading volume of cryptocurrencies changed both by currency and geographical location (Naeem et al. 2021; Shahzad et al. 2021b; Dutta et al. 2022; Kumar et al. 2022). The increased transparency of a financial transaction sustains customers’ trust and predicts their attitude toward an entity (Naveed et al. 2021). In the economic transaction, trust remains a key element. The adoption of Blockchain appears to be essential for banks to foster their competitive position and sustain their performance and growth. Besides revitalizing the banking sector, the adoption of Blockchain will also transform customers’ well-being. Transformative service research (TSR) adheres to this concept and requires service providers to look after consumer well-being. The concept of consumer well-being remains an integral premise of societal marketing, which emphasizes the importance of meeting customers’ needs while also considering the well-being of society as a whole (Hoeffler and Keller 2002). Financial institutions in particular can demonstrate a societal marketing orientation by being transparent and ethical in their business practices (Ward and Lewandowska 2006). Furthermore, Blockchain-enabled banking is believed to provide a higher level of transparency and security for customers, while also reducing the risk of fraud and improving the efficiency of banking operations. There is a growing consensus that the ultimate goal of financial services is to maximize the individual shareholder wealth, which determines one’s financial well-being related to a person ability to meet current and ongoing financial obligations, while feeling secure about one’s financial future. The literature investigating the role of Blockchain and its transformative impact on customer well-being is still limited. Additionally, most of the past studies have been conducted in developed economies where the pace of technological adoption and regulatory compliance is divergent from developing economies. Banks act as a catalyst in stimulating economic growth in developing countries like Pakistan, which is now under the jurisdiction of the Financial Action Task Force (FATF) for encountering increasing number of financial scandals and facing rising challenges to sustain transparency. Therefore, to comply with the tightened international regulatory mechanism and safeguard customers’ well-being, as well as to ensure efficiency, security and transparency, the banking system needs to explore robust and effective technologies (Garg et al. 2021). Accordingly, the banking system of Pakistan has the potential to embrace Blockchainbased systems. Accordingly, this study aims to (1) explore Blockchain technology interventions, which are likely to improve an individual’s financial behavior and well-being in Pakistan, and (2) contribute towards the growing literature on financial services and their transformative role in shaping customers’ financial well-being.

Blockchain-Enabled Banking Services

43

2 Literature Review Recently, financial well-being has gained significant attention from regulators, managers, and academics: past studies examine various antecedents of financial well-being such as financial knowledge and behavior, consumer spending and self-control, credit card literary, bank information transparency and self-efficacy (Losada-Otalora and Alkire 2019), and firm information transparency and trust (Naveed et al. 2021). Research on financial well-being and the perceived feeling of being able to meet current and future financial obligations, remains limited. Blockchain, as a breakthrough in information transmission and data storage, might transform the existing model of financial services to a more efficient, convenient and transparent one. Despite the growing belief that the adoption of these technologies might have a potential transformative impact on consumer financial well-being, evidence with this regard remain scarce. Hence, this study aims to explore how Blockchain-enabled banking is perceived by the banking customer, and the extent to which it can magnify one’s level of perceived financial well-being by considering the societal marketing orientation. Blockchain-enabled banking and societal marketing orientation are mutually exclusive concepts that, when combined, can have a significant impact on the banking industry and society (Rain et al. 2022). Indeed, the use of Blockchain technology in banking can provide a higher level of transparency, security, and efficiency for customers (Hoeffler and Keller 2002). A societal marketing orientation in banking can ensure that financial institutions prioritize the needs and financial well-being of society. These two concepts can help create a banking industry that is not only more efficient and secure, but also more socially responsible and ethical (Ward and Lewandowska 2006). This can help to build a more sustainable and equitable society, where financial services are accessible to all and contribute to their financial well-being. 2.1 Blockchain-Enabled Security and Efficiency The adoption of this modern technological disruption by some banks is likely to offer them a competitive edge over competitors, as efficient and secure Blockchain-enabled banking services will allow customers to better manage their financial goals and objectives. However, till date, there are limited research that respond to the nexus between Blockchain enabled efficiency and security and customer perceived financial well-being. Transformative consumer research (TSR) overlooks the pivotal role of services in driving household as well as consumer well-being. It suggests that service providers should foster the welfare of the consumer to whom they provide their services (Anderson et al. 2013). Stimulated by the TSR agenda, the seminal study by Burgen et al. (2017) establishes the foundation to examine the overlooked concept of financial well-being. Extending this notion, the transformative role of Blockchain-enabled banking services would be pragmatic to determine the customer financial well-being. Evidence regarding the customer perceived benefits of Blockchain-enabled services and its transformative impact on customer well-being remains quasi-nonexistent. Therefore, this study aims to examine how Blockchain-enabled efficiency and the security of the banking services trigger customers’ financial well-being. H1 : Blockchain-enabled efficiency and security result in high level of FW.

44

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2.2 Blockchain-Enabled Regulatory Compliance The aftermath of the global financial crisis has imposed various regulatory reforms on the financial sector worldwide, therefore regulatory compliance remains the prime concern for banks to defend their legitimacy. Rules and regulations are most needed to safeguard the bank stakeholders’ interests, while also fostering the integrity and reputation of the financial system. Financial institutions are under increasing pressure to elevate their compliance as part of their operational strategy to sustain sound corporate reputation. Likewise, customers perceive banks that comply with regulations as safe to interact with. Moreover, regulatory compliance is essential for ensuring a transparent banking system, any pitfall in the banking sector directly affects the financial well-being of the society (Garg et al. 2021). Blockchain appears to have the fundamental capacity to integrate all financial processes, while enabling banks to comply with regulations in an effective manner. H2 : Blockchain-enabled regulatory compliance results in high level of FW. 2.3 Blockchain and Information Transparency Information transparency has been attributed to mitigate information asymmetry and build stakeholder trust by disclosing issues that are usually left in the dark. In the context of banking, information transparency is conceived as the perceived quality of information shared by banks with their shareholders. Recently, regulators have been increasingly asking for a more transparent information disclosure to safeguard the financial well-being of individuals against the devastating effect of misleading information. Consequently, banks have been integrating the element of transparency into their information disclosure. Accordingly, information transparency became performance evidence as well as an indicator to identify well-reputed banks. The increasing demand for greater transparency is based on the notion that reliable information is requisite to make sound resource allocation decisions (Netemeyer et al. 2018). Past studies document that based on informed and rational decision-making, customers thrive on their overall well-being (Brüggen et al. 2017). Despite increased attention to the analysis of transparency measures, limited attention has been devoted to how a household’s financial well-being is contingent to information transparency. The role of bank information transparency in determining the financial well-being remains slightly unfocused, and the mechanism through which the bank’s transparency provokes well-being is not well documented (Losada-Otálora and Alkire 2019). H3 : Information transparency mediates the relationship between efficiency and security, regulatory compliance and FW Figure 1 outlines the proposed theoretical model based on TSR theory:

Blockchain-Enabled Banking Services

45

Fig. 1. Theoretical Model

3 Methodology This study was based on a quantitative design, whereby data was collected through a survey. The proposed model was estimated through PLS-SEM. The sample included individuals with bank accounts in commercial banks located in Islamabad (Pakistan). A convenience sampling method was adopted for data collection. Out of 600 distributed questionnaires, only 283 were used for analysis after removing the missing values. Table 1 shows the detail characteristics of the sample selected. Table 1. Descriptive and Correlation Matrix Mean

S.D

Gdr

Age

Edu

Exp.

FW

ES

RC

Gdr

1.12

0.4

1

Age

2.04

0.8

0.03

1

Educ

1.87

0.7

0.05

0.19**

1

Exp.

1.76

0.8

0.01

0.05

0

1

FW

3.61

0.5

0.06

0.02

0.02

0

1

ES

2.98

0.7

0.03

0.01

– 0.04

– 0.12

0.38**

1

RC

3.43

0.7

0.02

0.06

– 0.02

– 0.01

0.54**

0.54**

1

IT

3.79

0.6

0.02

0.04

0.01

– 0.01

0.61**

0.40**

0.54**

IT

1

Note: ** shows the significance at 5%.

The study variables were assessed through scales adopted from past literature. Financial well-being was assessed based on the six-item scale by Gerrans et al. (2014). The perceived benefits of Blockchain banking named as efficiency and security and regulatory compliance were adopted from Garg et al. (2021). The mediating variable incorporated as perceived information transparency has been adopted from the explanatory variable, and the broker information transparency was measured through four items scale adapted from (Liu et al. 2015). The study also incorporated several control variables: gender, age, education and marital status affect their assessment of perceived financial well-being.

46

M. F. Farah et al.

4 Data Analysis 4.1 Measurement Model PLS-SEM were utilized for their appropriateness in estimating simultaneous causal relationships among variables. A three-step modelling was used to analyze the collected data, including the common factor analysis utilized to determine the number of latent variables, and the confirmatory factor analysis (CFA) employed to validate the measurement model and test the structural model. Validity was determined using convergent and discriminant validity parameters. The average variance extracted with outer loading were also applied to measure the convergent validity of the latent variables. To establish the convergent validity, the Average Variance Extracted (AVE) of the latent variable and outer factor of each item in the variable must be greater than 0.70 as argued by (Hair et al. 2017). The results for the AVE, factor loadings, composite reliability (CR), and Cronbach alpha are all presented in Table 2. Table 2. Descriptive statistics of measurement model Variable name (Scale source)

Items Factor loading Average Composite Cronbach’s α Variance Reliability (CR) Extracted (AVE)

Efficiency and Security (Garg et al. 2021)

ES1

0.795

ES2

0.870

ES3

0.825

ES4

0.870

ES5

0.845

FW1

0.886

FW2

0.899

FW3

0.820

FW4

0.853

FW5

0.892

FW6

0.841

Financial Well-being (Gerrans et al. 2014)

Information IT1 Transparency IT2 (Liu et al. 2015) IT3

0.888

IT4

0.907

0.708

0.924

0.897

0.749

0.947

0.933

0.813

0.945

0.923

0.915 0.895 (continued)

Blockchain-Enabled Banking Services

47

Table 2. (continued) Variable name (Scale source)

Items Factor loading Average Composite Cronbach’s α Variance Reliability (CR) Extracted (AVE)

Regulatory Compliance (Garg et al. 2021)

RC1

0.831

RC2

0.870

RC3

0.886

RC4

0.830

RC5

0.827

0.721

0.928

0.903

4.2 Structural Model The structural model was assessed only after establishing the reliability and validity of the constructs. All the hypotheses were supported expect for H1 according to which efficiency and security have an insignificant impact on the bank customers’ financial well-being. The findings confirm that the efficiency and security of the blockchain have an insignificant (significant) relationship with financial well-being (information transparency), where β = -0.046 (0.427) and p-value = 0.505 (0000). Regulatory compliance has a statistically significant relationship with information transparency and customers’ financial well-being with a path coefficient of 0.458 and 0.248, and p-values of 0.000 and 0.001 respectively, which supports the 2nd hypothesis. Table 3 summarizes the mediation analysis for this study. Table 3. Mediation analysis results Direct Effect 0.248*** RC IT FW -0.046 ES IT FW *** shows the significance at 1%

Indirect Effect 0.299*** 0.279***

Mediation Results Complementary mediation Indirect-Only mediation

5 Discussion, Conclusion, and Future Research Aligned with the proposed TSR research directions of Losada-Otálora (2019), this study was devoted to study the impact of new information technologies, hereby Blockchain, on the improvement of information transparency, and its ensuing influence on customers’ financial well-being. This study showed that banks might adopt Blockchain to uplift transparency, which in turn remain integral to improve the financial well-being of their customers. This expanded and validated the work of Brüggen (2017), who proposed technological interventions to uplift customer well-being. The findings of this study showed that regulatory compliance – as a potential feature of Blockchain technology – has a significant positive impact on customer financial well-being. Likewise, the indirect impact

48

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via information transparency also remains positively significant. The indirect impact of perceived efficiency and security as a potential feature of Blockchain technology has a significant positive impact on financial well-being. Nonetheless, the direct impact of efficiency and security on financial well-being remains insignificant. This study has a number of managerial, policy and societal implications. Financial institutions, including banks, policymakers and regulators can use the insights of this study to improve the financial well-being of their communities. Bank management and institutions that offer financial services can avail this objective by designing strategies with more transparent information. By adopting Blockchain technology, the banking sector of Pakistan can benefit from complying with stringent international regulations and enhancing information transparency in order to revitalize customers’ trust. Blockchain adoption will enable banks operating in Pakistan to gain a competitive edge by improving their efficiency and uplifting their customer’s financial well-being. Additionally, the implications of combining societal marketing orientation and Blockchain-enabled banking are significant, as it has the potential to transform the banking industry, while contributing to the financial well-being of society (Hoeffler and Keller 2002). The implications of this combination are far-reaching and have the potential to create a more sustainable, equitable, and socially responsible banking industry that prioritizes the needs and well-being of society as a whole. Future studies could examine the affective response of customers based on their affective assessment of the information transparency and could focus on the interaction of the cognitive and affective evaluation of the information in framing the perceived information transparency and financial well-being of customers.

References Ammous, S.: Can cryptocurrencies fulfil the functions of money? Q. Rev. Econ. Finance 70, 38–51 (2018) Anderson, L., Ostrom Williams, J.d., A.: Transformative service research: an agenda for the future. J. Bus. Res. 66(8), 1203–1210 (2013) Arsi, S., Guesmi, K., Bouri, E.: Herding behavior and liquidity in the cryptocurrency market. Asia-Pacific J. Oper. Res. 2140021 (2021) Awad, Y., El-Kassar, A.N., Kadri, T.: Rabin public-key cryptosystem in the domain of Gaussian Integers. In: 2018 International Conference on Computer and Applications (ICCA), pp. 1–340. IEEE, August 2018 Bouri, E., Gabauer, D., Gupta, R., Tiwari, A.: Volatility connectedness of major cryptocurrencies: the role of investor happiness. J. Behav. Exp. Finan. 30, 100463, June 2021a Bouri, E., Saeed, T., Vo, X., Roubaud, D.: Quantile connectedness in the cryptocurrency market. J. Int. Finan. Markets Inst. Money, 71, 101302, March 2021b Bouri, E., Kristoufek, L., Shahzad, S.J.: Microstructure noise and idiosyncratic volatility anomalies in cryptocurrencies. Annals Oper. Res. 1–27 (2022a) Bouri, E., Gupta, R., Vo, X.: Jumps in geopolitical risk and the cryptocurrency market: the singularity of Bitcoin. Defence Peace Econ. 33(2), 150–161 (2022) Brüggen, E., Hogreve, J., Lofgren, M.: Financial well-being: a conceptualization and research agenda. J. Bus. Res. 79, 228–237 (2017) Dutta, A., Bouri, E.: Outliers and time-varying jumps in the cryptocurrency markets. J. Risk Finan. Manag. 15(3), 128 (2022) Frizzo-Barker, J., Chow-White, P., Green, S.: Blockchain as a disruptive technology for business: a systematic review. Int. J. Inf. Manage. 51, 102029 (2020)

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Garg, P., Gupta, B., Modgil, S.: Measuring the perceived benefits of implementing Blockchain technology in the banking sector. Technol. Forecast. Soc. Change 163, 120407 (2021) Gerrans, P., Speelman, C., Campitelli, G.: The relationship between personal financial wellness and financial well-being: a structural equation modelling approach. J. Fam. Econ. Issues 35(2), 145–160 (2014) Hair, J.F., Hult, G.T.M., Ringle, C.M., Sarstedt, M., Thiele, K.O.: Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods. J. Acad. Mark. Sci. 45(5), 616–632 (2017). https://doi.org/10.1007/s11747-017-0517-x Hoeffler, S., Keller, K.L.: Building brand equity through corporate societal marketing. J. Public Policy Mark. 21(1), 78–89 (2002) Kumar, A., Bouri, E.: Connectedness among major cryptocurrencies in standard times and during the COVID-19 outbreak. J. Int. Finan. Markets Inst. Money 77, 101523101523, March 2022 Liu, Y., Eisingerich, A., Chun, H.: Service firm performance transparency: how, when, and why does it pay off? J. Serv. Res. 18(4), 451–467 (2015) Losada-Otálora, M., Alkire, L.: Investigating the transformative impact of bank transparency on consumers’ financial well-being. Int. J. Bank (2019) Naeem, M., Bouri, E., Peng, Z., Shahzad, S., Vo, X.: Asymmetric efficiency of cryptocurrencies during COVID19. Physica A 565, 125562 (2021) Naveed, M., Farah, M.F., Hasni, M.J.S.: The transformative role of firm information transparency in triggering retail investor’s perceived financial well-being. Int. J. Bank Market. 39(7), 1091– 1113 (2021) Netemeyer, R., Lynch, J.G., Jr.: How am I doing? perceived financial well-being, its potential antecedents, and its relation to overall well-being. J. Consum. Res. 45(1), 68–89 (2018) Rain, N.J.N., Roy, D., Molla, M.R., Al Mamun, A., Nayeem, M.A.I.: Analyzing the uses of societal marketing concept of bank: a study on a private bank. Amer. Int. J. Multi. Sci. Res. 12(1), 37–46 (2022) Shahzad, S., Bouri, E., … & Vo, X. (2021a). The pricing of bad contagion in cryptocurrencies: a four-factor pricing model. Finance Research Letters, Volume 41, July 2021, 101797 Shahzad, S.J.H., Bouri, E., Kang, S.H., Saeed, T.: Regime specific spillover across cryptocurrencies and the role of COVID-19. Finan. Innov. 7(1), 1–24 (2021). https://doi.org/10.1186/s40854020-00210-4 Shahzad, S., Bouri, E., Ahmad, T., Naeem, M.: Extreme tail network analysis of cryptocurrencies and trading strategies. Finan. Res. Lett. 44, January 2022a Shahzad, S., Anas, M., Bouri, E.: Price explosiveness in cryptocurrencies and Elon Musk’s tweets. Finan. Res. Lett. 102695 (2022b) Wang, Y., Wang, C., Cheng, F.: Can investors’ informed trading predict cryptocurrency returns? evidence from machine learning. Res. Int. Bus. Finan. 62, 101683, December 2022 Ward, S., Lewandowska, A.: Validation of a measure of societal marketing orientation. J. Pub. Affairs Int. J. 6(3–4), 241–255 (2006) Wen, Z., Bouri, E., Xu, Y., Zhao, Y.: Intraday return predictability in the cryptocurrency markets: momentum, reversal, or both. North Amer. J. Econ. Finan. 62, 101733, November 2022

How Are US Retailers Protecting Their Customer Data While Growing Their Ad Promotions Business? Darrell Bartholomew1(B) , Stephen Hampton1 , and Hunter Briegel2 1 School of Business Administration, Penn State Harrisburg, Harrisburg, USA

{deb62,stephen.hampton}@psu.edu 2 Attorney-at-Law, Chicago, USA

Abstract. As retail media networks continue to grow and the data exchanges between consumers and retailers are becoming more prevalent and complex, it becomes necessary to define and describe how retailers are managing their consumer data. We attempt to describe retailers’ walled gardens and how retailers are keeping these data exchanges secure. Keywords: Retail Media Networks · Walled-Gardens · Clean Rooms · Homomorphic Encryption

1 Introduction In November of 2022, the United States (US) Senate met to discuss the potential merger of two of the largest grocery store chains, Kroger (which serves 60 million consumers) and Albertsons (which serves 25 million consumers). Of major concern to the senators were issues of data privacy, data sharing between the retailer and its vendors, and personalization of ads to consumers on retail media networks (Hamstra 2022). This research seeks to address these concerns. The relationship between retailers and their consumers involves exchanges of assets including products, money, and perhaps often ignored information. Consumers look to retailers to provide them with the products and services that they desire in the assortment and quantities they need. Consumers accept ad information from retailers on the products and services to better inform their purchase. Retailers use information from their consumers to transact business with them along their customer journey. Due to the COVID-19 pandemic and increased competition in retailing in the global marketplace, retailers have become more Omnichannel in how they provide these exchanges of assets with their customers. Now retailers are expected to offer a broader assortment of customer interface options including in-store, online, curbside, home delivery, and others. Since large retailers are also growing their advertising promotions business by personalizing ads toward customers based on their past purchase behavior, how can they both increase advertising and keep customer data secure? The purpose of this research © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. C. Gázquez-Abad et al. (Eds.): NB&PL 2023, SPBE, pp. 50–56, 2023. https://doi.org/10.1007/978-3-031-32894-7_6

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is to look at current retailing practices to see how they are doing this using walled gardens, clean rooms, homomorphic encryption, and ad tech exchanges, by defining and describing each of these through illustrations and explanations from experts running these RMNs, we hope to answer these important questions and discuss their public policy implications. This research is exploratory and relies on exploratory methods such as secondary data from industry sources, and primary data from in-depth interviews with subject matter experts from retail media providers and third-party service platforms used in retail media networks (RMNs) promotional advertising campaigns. In-depth interviews were conducted with large retailers in the US ad third-party media platform companies who provide these demand-side platform services to retailers to understand the process of how walled gardens are used to retarget retail audiences using the retailer’s first-party shopper and loyalty card data.

2 Walled Gardens Walled gardens are created by retailers to isolate and protect their customer data. One of the first mentions of walled gardens is found in a patent by Microsoft which describes walled gardens as “walling off data within one’s network by keeping it secure using levels of privacy and or/security allowing the retailer access to the data but keeping it private from everyone else” (Kelly 2005). Walled gardens have long been used to protect consumer data on social networking sites (SNSs) behind walls by creating data silos around consumer’s personally identifiable information (PII) data and keeping this data about the people and their relationships within the platform itself and not sharing it outside the platform (Kärger and Siberski 2010).

3 Secure Data Exchanges The largest retailers in the US are now acting as media companies. According to the Boston Consulting Group, US ad promotions revenue for the retail media networks (RMN) business is approaching $100 billion in high-margin annual (BCG). BCG estimates that similar industries with lots of first-party data such as travel and tourism will also follow this trend (Wiener et al. 2021). This growth has largely been attributed to targeted personalized ads served on retailers’ owned and operated media (O&O) as well as placed programmatically on ad platforms of Google and Facebook. Several interviews were done with RMNs and third-party DSPs and providers of clean room technologies who helped to explain these platforms and exchanges. On the demand side ads and money flow in on the demand side. The advertiser creates the ad and places it on the demand side platform (DSP) from their ad server where bids are made in the exchange. On the supply side flow of ad slots from the publisher’s ad server to display ads on the O&O sites on the supply side platform (SSP) where ads are called in the exchange (Fig. 1). Most promotional advertising on O&O sites is promotional ads in the form of banner ads placed on websites by retailers and their brand partners (i.e. Consumer Packaged

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Fig. 1. Retail Media Ad Exchanges for Programmatic Advertising

Goods companies CPGs) through this bidding exchange process by targeting consumers with products that they currently purchase while they are shopping (LiveRamp 2022). Through data on-ramping retailers now know much more about their consumers by enriching their first-party data and can personalize their retail offerings (Bartholomew and Williamson 2022). 3.1 Clean Rooms In the past advertisers relied heavily on third-party cookies to collect user data. Cookies placed on a website for its own purposes are first-party cookies. They are often used to improve user experience by remembering user preferences and settings. Items added to online retail shopping carts, usernames, passwords, and language preferences are information that first-party cookies store. A cookie placed by any other site, such as an advertiser is a third-party cookie. Third-party cookies are used by advertisers to monitor user activity online. They track users across domains allowing for a more complete picture of user behavior than first-party cookies alone could offer. Recent legislation including CCPA, ePR, and GDPR have hampered the use of thirdparty cookies (Brodherson 2021). Additionally, the tech industry has joined the movement and limited third-party cookies’ effectiveness. Apple’s Safari and Mozilla’s Firefox now block 3rd party cookies by default. Users of these browsers have to actively opt-in to receive third-party cookies. This has left advertisers relegated to a coarse-grained advertising approach without enough information upon which to segment the market further and effectively target a more receptive audience. While the effect of the loss of third-party cookies and their corresponding data was widespread, the magnitude of its impact was not uniformly distributed across all organizations. Organizations with large sets of first-party data were still able to continue operations largely unharmed by these changes, while those without this luxury were left scrambling to fill the void (Marsten 2022). It is amid this seeming conundrum that new technologies have emerged as potential solutions to these problems. Data clean rooms are protected neutral spaces for data collaboration between two or more parties without the sharing of protected information. This digital space is modeled after physical clean rooms that are seen in research and

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manufacturing contexts to protect sensitive materials (Haggin 2022). In these contexts, a clean room provides a highly controlled environment without the fear of introducing contaminants. In data clean rooms the primary concern is the protection of private data while affording some access to other data that may allow for better segmentation, targeting, and measurement. Data clean rooms can provide aggregate and anonymized data that advertisers need to be focused and efficient while allowing retailers to leverage their data without losing control of it. Most data clean rooms operate in the cloud. Retailers encrypt and upload data to the clean room. Approved partners are granted access to portions of the available data. These partners may then upload data of their own to combine with the retailer data. The data from different clean rooms may not be combined, nor is there a standardization of the formats across different data clean room providers (Ostwal 2023). This means that each retailer-CPG interface is an idiosyncratic dialogue that does little to contribute to that greater systematic conversation across retailers. Even still some data and insights are far superior to none. 3.2 Homomorphic Encryption Homomorphic encryption, herein referred to as ‘HE’, is a nascent technology that enables computation on encrypted data. Similar to clean rooms employing trusted execution environments, HE claims to ensure data confidentiality in processing; this is a heretofore elusive endpoint that extends security measures taken while data is at rest or in transit. Contrasted with secure clean rooms, HE does not depend on specific hardware or attestation reports. Applied to a value chain scenario, such as programmatic advertising, these are important characteristics. Retailers’ exchange of first-party data with advertising partners motivates the adoption of information systems that can simultaneously shield proprietary information, achieve regulatory compliance and increase revenue. A second form of programmatic advertising uses real-time bidding (RTB) to award advertising placement via an auction ecosystem. This expands competition for advertising slots, benefiting the publisher, while brands can access the widest possible inventory with relative ease. The retailer, as a publisher, may transmit bid requests via multiple parties: an SSP, ad exchange, DSP, and possibly others, such as a DMP. In fact, it is commonplace for programmatic publishers to not even know the totality of parties their bid request will reach (Veale and Borgesius 2022). Legislation and technical changes have reduced the viability of cookies as a behavioral tracking method. European Union regulation has been shown to occasion a significant reduction in advertising effectiveness by complicating cookie-based techniques (Goldfarb and Tucker 2011). Google’s proposed deprecation of third-party cookies in the Chrome browser has simultaneously set off a global race to find innovative ways to profile users (Sloane 2022). Provisioning the RTB market with first-party publisher data is essential to maintaining the vitality of digital advertising. Despite prognostications of declining advertising utility, injections of new data have the potential to increase relevance to consumers. In some markets, cookie-based profiling was never particularly effective (AlSabeeh and Moghrabi 2017). Regardless, retailers are more than advertising businesses: they have complex and personal relationships with customers. Retail purchase history can be extremely revealing; for example, Kosher or Halal food preferences can indicate religion, ethnic foods are suggestive of nationality and over-the-counter

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medicine may indicate a health condition. Food liking has also been shown to have genetic associations (May-Wilson et al. 2022). Retailers have a social obligation to ensure ethical and privacy-sensitive personalization — their reputation is on the line. HE extends beyond other privacy-enhancing technologies to eliminate the disclosure of personal data. At the same time, HE doesn’t degrade the precision of information. There are assertions in the literature that HE can be used by a controller to anonymize data under the GDPR, a very high compliance bar. Consequently, data shared with a processor, who does not possess the decryption key, is argued to be outside the scope of GDPR (Spindler and Schmechel 2016, George et al. 2019). As the controller, the retailer has the exclusive right to determine how externally processed data is used; they are the only ones capable of decryption. Incidentally, this property enables consumers to effectively revoke consent across an unknown number of actors. A consumer simply needs to inform their retailer to stop processing data. The encrypted data, although theoretically retained by DSPs, is then rendered useless and indecipherable.

4 Implications and Discussion Through our research and interviews, we found that three critical steps must be undertaken by retailers who wish to engage in retail media promotional advertising. First retailers need to wall off their first-party data using a walled garden. Second, retailers must establish clean rooms or use HE to create a secure platform for accessing their data. Many retailers currently do not have the technology capabilities to do so. Retailers without this internal capability must partner with a third party to create a data management platform (DMP). Third parties including Epsilon/CitrusAd, LiveRamp’s Safe Haven, Snowflake, and Amazon Marketing Cloud are examples of third parties providing these secure exchange services through their DMPs. Two possible solutions are introduced using either clean rooms or homomorphic encryption. These solutions allow retailers to keep all of their PII behind their walled garden but allow for this data to be analyzed by the DMP in a private and secure access point. Third, the retailer media network is then able to anonymize consumer segments that are then generated and can be used to retarget these consumer segments with relevant ads using programmatic advertising. Discussing these important issues surrounding the privacy of data exchanges makes a contribution to the literature by bringing these important illustrations and definitions into the marketing and public policy literature that are relevant to what many marketers see as a fundamental shift in retail media (Wiener et al. 2021). Data clean rooms provide a solution for retailers wanting to leverage their valuable first-party data while remaining compliant with emerging regulations. Even though data clean rooms answer many questions for retailers and advertisers alike there are many questions yet unanswered. It seems that interoperability, scaling, and standardization are all issues concerning clean rooms that remain unsettled. That is to be expected with new technologies. While those issues are addressed it is important to remember that even despite these important issues remaining unresolved even in their current state, data clean rooms are providing considerable value and enabling consumers, retailers, and advertisers to benefit from their use. HE can be utilized to encrypt sensitive shopper data transmitted externally. First, the retailer will generate a private key secured within their walled garden environment. This key is not

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disclosed to downstream entities (e.g., DSP). Encrypted data can be operationalized as an extension of existing RTB protocols, for instance, OpenRTB, or per a novel arrangement between a future generation of HE-enabled SSPs and DSPs. Parties participating in RTB auctions can compute on the encrypted data. This could be used to precisely adjust a bid for advertising on a retail media network. Additionally, a brand, in conjunction with its DSP, may wish to personalize advertisements or promotional copy. The retailer will then use their key to decrypt the processed data. Once the result is back within the walled garden, it can be treated as plaintext. For example, the retailer can award an auction or personalize media. Consequently, although the retailer has realized the benefits of RTB advertising, it can continue to operate as a walled garden. Retailers will incur costs resulting from data privacy regulation. These encompass direct implementation expenses, potential loss of commercial opportunity, and compliance risk. Compliance risk arises from enforcement uncertainty and can forestall investment (Calomiris et al. 2020). The effects of GDPR are more pronounced on small businesses, who lack economies of scale in the deployment of technology and obtaining consented access to data (Chen et al. 2022). These costs are generally passed on to consumers in the form of higher prices or absorbed through lower profits. However, considering that retail media is a supplementary revenue stream, expenditure on enabling technology may have no detrimental impact. Associations, including the Interactive Advertising Bureau, can help ameliorate uncertainty by continuing privacy standardization and innovation. Government can assist small and medium-sized enterprises by offering bespoke guidance and regulatory process innovation grants. Governments can also lower the cost of technology by harmonizing data protection principles at the highest possible level, e.g., global and/or federal. Well-drafted rulemaking can provide predictability, thereby reducing compliance risk compared to piecemeal guidance. With this in mind, the United States would benefit from rapidly creating a national framework for data protection. Implementing safeguarding technology should not be viewed as a burdensome exercise; clean rooms and HE open new avenues for collaboration that benefit retailers, brands, and consumers. Walled gardens, clean rooms, and homomorphic encryption can meet the most demanding forward-looking legislative and commercial requirements that draw retailers to a walled garden model. Concurrently, it can facilitate dramatically expanded competition for ad placement and opportunities for revenue generation. It must be acknowledged that HE is computationally intensive. Thus, the state of the art may not be suitable for realtime auctions with strict performance requirements. Future works should empirically explore this possibility in depth.

References AlSabeeh, D.A., Moghrabi, I.A.R.: Programmatic advertisement and real time bidding utilization. In: Kar, A.K., et al. (eds.) I3E 2017. LNCS, vol. 10595, pp. 289–297. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68557-1_26 Bartholomew, D.E., Williamson, M.: Retail media networks. J. Retail. Consum. Serv. 69, 1–9, November 2022 Brodherson, M., Broitman, A., Macdonald, C., Royaux, S.: The demise of third-party cookies and identifiers (2021). Accessed 17 Sept 2021. https://www.mckinsey.com/business-functions/mar keting-and-sales/ourinsights/the-demise-of-third-party-cookies-and-identifiers

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Calomiris, C., Mamaysky, H., Yang, R.: Measuring the cost of regulation: a text-based approach (technical report). National Bureau of Economic Research (2020). https://doi.org/10.3386/ w26856 Chen, C., Frey, C.B., Presidente, G.: Privacy Regulation and Firm Performance: Estimating the GDPR Effect Globally. University of Oxford, Oxford Martin School (2022) George, D., Reutimann, K., Tamò-Larrieux, A.: GDPR bypass by design? transient processing of data under the GDPR. Int. Data Priv. Law 9(4), 285–298 (2019) Goldfarb, A., Tucker, C.E.: Privacy regulation and online advertising. Manage. Sci. 57(1), 57–71 (2011). https://doi.org/10.1287/mnsc.1100.1246 Haggin, P.: Advertisers Turn to ‘Clean Rooms’ to Keep Consumer Data Private (2022). Wall Street J. https://www.wsj.com/articles/advertisers-data-clean-rooms-11666038545 Hamstra, M.: Senate hearing highlights consumer privacy concerns. Supermarket News (2022). https://www.supermarketnews.com/online-retail/senate-hearing-highlights-consumer-pri vacy-concerns Kärger, P., Siberski, W.: Guarding a walled garden—semantic privacy preferences for the social web. In: Extended Semantic Web Conference. Springer (2010) Kelly, S.U., Cheng, L., Ryszard, K., Kott, R., Hughes, L., Portnoy, W.L.: Walled Garden. United States Patent Office (Ed.). Microsoft Technology LLC, US (2005) LiveRamp: The 5 Minute Guide to Retail Media Networks. LiveRamp Holdings, Inc. (2022) Marsten, E.: Not All Retail Media Loyalty Programs Are Created Equal (2022). Adweek: https://www.adweek.com/performance-marketing/not-all-retail-media-loyalty-programs-arecreated-equal May-Wilson, S., et al.: Large-scale GWAS of food liking reveals genetic determinants and genetic correlations with distinct neurophysiological traits. Nat. Commun. 13 (2022). https://doi.org/ 10.1038/s41467-022-30187-w Ostwal, T.: To Grow This Year, Data Clean Rooms Shoot for Interoperability (2023). Adweek:https://www.adweek.com/programmatic/to-grow-this-year-data-clean-rooms-shootfor-interoperability/ Sloane, G.: How ad tech execs plan for a cookieless future: meet the 12 industry leaders designing the next generation of programmatic advertising. Advertising Age 93(2) (2022) Spindler, G., Schmechel, P.: Personal data and encryption in the European general data protection regulation. J. Intellect. Property Inf. Technol. Elec. Commer. Law 7(2), 163–177 (2016) Veale, M., Borgesius, F.Z.: Adtech and real-time bidding under European data protection law. German Law J. 23(2), 226–256 (2022) Wiener, L., Kelman, L., Fisher, S., Abraham, M.: The $100 Billion Media Opportunity for Retailers.. Boston Consulting Group (2021)

Generations and Their Preferences for Loyalty Program Rewards in Supermarket Retailing Giada Salvietti(B) , Marco Ieva, and Cristina Ziliani University of Parma, Parma, Italy [email protected]

Abstract. When designing a loyalty program, a major decision is that of identifying which rewards and of which nature (monetary vs. non-monetary) to offer. Customers do not evenly react to the same rewards, a fact that draws retailers to identifying segments that share a common preference. The present study adopts a generational segmentation approach to identify differences in generational cohorts according to their attitude towards loyalty programs as well towards rewards offered within these programs, in the supermarket industry. Data were collected through a panel survey on 1,162 Italian consumers. Significant differences are found with reference to enrollment in a loyalty program and reasons not to enroll, and to preference for rewards. Implications for retailers and for future research are drawn. Keywords: Supermarket retailing · loyalty program · generational cohort theory · reward type

1 Introduction Designing a loyalty program is a major issue for retailers, especially in the supermaket sector, where such programs are pervasive (Bies et al. 2021) Among the various features that need to be defined, identifying which types of rewards to offer is key to the loyalty program’s success. The type of reward, in fact, has a direct effect on store loyalty (MeyerWaarden 2015) as well as retailer profitability (Bombaji and Dekimpe 2020). Although rewards can be categorized with different criteria (such as exclusive or non-exclusive, immediate or delayed, etc.), the most important distinction refers to their nature. In this sense, we distinguish between monetary (or “hard”) and non-monetary (or “soft”) rewards. The latter include all rewards “providing psychological, relational, emotional, and functional benefits” (Meyer-Waarden 2015, p. 23). Both types of rewards have been proven to positively impact customer satisfaction towards the loyalty program and the retailer, leading to long-term loyalty outcomes (Bridson et al. 2008). Moreover, despite more expensive, non-monetary rewards are usually offered, alongside monetary rewards, since they are unique and not easy to replicate by competitors (Chaabane and Volle 2010). Remarkably, customers may react differently to the same rewards offered, based on their own characteristics or motivations. As suggested by Haverila et al. (2022), retailers © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. C. Gázquez-Abad et al. (Eds.): NB&PL 2023, SPBE, pp. 57–64, 2023. https://doi.org/10.1007/978-3-031-32894-7_7

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should identify customer segments and assess whether and how they value different reward categories, with the aim of improving the loyalty program effectiveness. The present study adopts a generational perspective to investigate preferences for different types of rewards, as well as for loyalty programs overall, in the supermarket retailing context. According to the generational cohort theory (Inglehart 1977), it is possible to identify distinguished groups of people that are born in the same time period and share similar life experiences, especially during their formative years (Meriac et al. 2010). Within each group, similar values, attitudes and beliefs are formed, leading in turn to equally similar behaviors (Soares et al. 2017). Five generational cohorts have been identified in literature: Silent Generation, Baby Boomers, Generation X, Generation Y or Millennials, and Generation Z. In marketing, the study of generations and within-groups differences, is aimed at identifying how potential targets belonging to each group react to stimuli of different nature (Schewe and Meredith 2004;), with implications for their engagement and loyalty. Examples are social media advertising (Kamal et al. 2013), price promotions (Eastman et al. 2021), and stores attributes (Jackson et al. 2011). Only a few studies, however, have investigated so far the context of loyalty programs and generations, and typically with a focus on a specific generation (e.g. Tahal 2014, on Millennials). Moreover, the majority of these studies attain to the hospitality and tourism sector. To our knowledge, this is a first attempt to identify generational differences with respect to supermarket retailing loyalty programs and preference towards the rewards these programs offer. The present study aims, therefore, to investigate different generations’ preferences for monetary and non-monetary rewards in supermarket retailing programs.

2 Methodology In order to achieve this goal, a structured questionnaire was designed. Participants were instructed to answer about their behaviors as far as supermarket purchases are concerned. A first section focused on their preferences for retailers’ channels and touchpoints: participants were provided with a list of touchpoints and asked to indicate the frequency of use per each of them (7 points self-anchored scale, from “never” to “often”). We ensured that the list was extensive by integrating relevant papers on touchpoints (Wind and Hayes 2016; Herhausen et al. 2019) with industry practices. The second section focused on loyalty programs: participants were asked to indicate whether they were enrolled in their preferred supermarket retailer’s loyalty program, the length of relationship with that retailer and their program, and eventual enrollment in other loyalty programs from other industries. Engagement with the loyalty program was also measured, through the scale developed by Bruneau et al. (2018). We assessed internal consistency of the scale using Cronbach’s α (Nunnally 1994) and the factor loadings of each item (Gerbing and Anderson 1988). The third section of the questionnaire investigated preferences for monetary vs. non-monetary rewards. Participants were provided with a definition and examples of monetary and non-monetary rewards, and were asked to indicate their agreement with two items on a 7-points Likert scale (from “completely disagree” to “completely agree”). Also, they were presented with a list of 18 rewards, both monetary and non-monetary, drawn from industry practices, and they were asked to rank their top three preferences.

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Finally, demographic information concerning age, gender, education and affluency was retrieved from the panel. Data were analyzed using the one-way Analysis of Variance (ANOVA) and post-hoc evaluation of specific group differences. The homogeneity of variances has been verified with Levene’s Test, eventually leading to the adoption of the Welch modification when needed (Gastwirth et al. 2009). Significance was set at 0.05. Further differences among percentages for categorical variables were assessed using the Chi-Square Test.

3 Results Data were collected on a sample of Italian consumers, through a leading consumer panel research company. In order to ensure that the participants could offer reliable behavioral data, we asked them to refer to the supermarket retailer they had bought more frequently from in the past 6 months. To facilitate the task, consumers were provided with a list of all major Italian retailers operating in the sector. The sample was purposefully built to be representative of the Italian population. Data were collected in September 2022 by means of an online survey. After excluding unfinished questionnaires, 1,162 valid responses were obtained. Table 1 summarizes the sample’s characteristics. Table 1. Sample demographics Measure

Category

Frequency

Percentage

Gender

Male Female

489 673

42,1 57,9

Generation

Millennials Generation X Baby Boomers Silent Generation

232 363 463 104

20,0 31,2 39,8 9,0

Education

Middle-school degree High-school degree University degree

181 600 381

15,6 51,6 32,8

Income/Affluency

Low Below average Above average High

184 331 384 263

15,8 28,5 33,0 22,6

As for channels used by consumers for their supermarket shopping, we notice that the offline channel is prevalent. 83.6% of participants in the sample purchase their groceries exclusively offline, and 11.4% state they are buying mostly offline. Conversely, only 0.43% are buying groceries exclusively online and 1.72% mostly online. Finally, 2.84% of participants state they buy using offline and online channels without distinctions. Despite this prevalence of offline, we found that Millennials are more likely to purchase grocery products using online channels than Generation X (0.169, SE = 0.059,

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sign. 0.04), Baby Boomers (0.188, SE = 0.057, sign. 0.01), and Silent Generation consumers (0.199, SE = 0.069, sign. 0.041). Finally, Millennials and Generation X consumers have shown a higher frequency of use of the retailer’s loyalty program than Baby Boomers and Silent Generation. This is also shown in Table 2. This time, participants were asked to refer to all supermarket retailers they purchase from, rather than their preferred one as before, and to indicate in how many loyalty programs they are enrolled. Differences are significant (Chi-squared test = 137.204; df = 24). Table 2. Generations and enrollment in grocery loyalty programs 0 – not enrolled

1

2

3

4

5

6 or more

Millennials

11.1

15.1

24.9

18.7

14.2

1.8

14.2

Gen X

17.6

24.2

18.7

14.0

8.0

4.1

13.2

Baby boomers

36.4

31.7

32.0

15.2

4.1

2.8

5.5

Silent gen

38.5

26.0

16.3

9.6

4.8

0

4.8

We asked the respondents who were not enrolled to focus on their preferred loyalty program retailer, and to identify reasons for non-enrollment, as the expected to obtain some insight as far as the role rewards played on this respect. As it emerges from Table 3, significant differences are identified (Chi-squared test = 65.252; df = 12). Millennials display higher percentages of enrollment in loyalty programs, followed by Generation X. As for the first issue investigated – that is voluntarily choosing not to enroll –, Silent Generation consumers are those more aware of the loyalty program’s existence, but consciously choose not to enroll. Two sets of reasons emerge: on the one hand, there are issues related to the enrollment procedure (privacy concerns, too many communications from the retailer, procedure complicated or lengthy, lack of clarity of the program 48%); on the other, issues related to the program design and reward system (excessive effort in terms of time and additional payments needed to redeem rewards, rewards are not interesting or not valuable, difficulty in redeeming rewards after initial enrollment - 52%). The second issue investigated - that is being unsure whether the retailer has a loyalty program or not – is more common among Baby Boomers and Silent Generation, Finally, the third issue concerned retailers not having a loyalty program, which impacts on 21.2% of Generation X consumers as well as on other substantial percentages for other generations.

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Table 3. Generations and LP enrollment status Enrolled in LP

Not enrolled in LP

Not know if ret. has a LP

Ret. does not have a LP

Millennials

75.0

2.6

7.3

15.1

Gen X

63.6

6.1

9.1

21.2

Baby Boomers

54.2

8.0

20.7

17.1

Silent Gen.

49.0

13.5

20.2

17.3

Further questions about the loyalty program length of relationship, enjoyment and reward preferences were asked only to those participants who were enrolled in their preferred retailer’s loyalty program at time of survey. The results that follow refer to 707 individuals out of the sample of 1,162. When it comes to the length of relationship, it is shown that 28.4% of Millennials have been purchasing groceries from the same retailer for 5 to 10 years, and 25.9% of them for more than 10 years. This is exceeded by the other generations (respectively, 48.2% of Generation X, 47.7% of Baby Boomers and 49.0% of Silent Generations have been purchasing from the same retailer for more than 10 years.) Millennials, however, display the higher percentages for the other time ranges (less than 1 year, 1 to 3 years, and 3 to 5 years. Measuring the length of relationship with the retailer’s loyalty program leads to similar results. Millennials, therefore, appear as a relevant target for retailers (Table 4). We also tested the engagement with the loyalty program. Differences based on generations, however, were not significant (ANOVA, p = 0.22). Table 4. Generations, LoR retailer and LoR Loyalty Program LoR Ret.

10 years

Millennials

4.7

20.7

20.3

28.4

25.9

Gen X

3.9

14.6

13.5

19.8

48.2

Baby Boomers

4.1

11.9

14.0

22.2

47.7

Silent Gen

1.9

11.5

13.5

24.0

49.0

LoR LP

< 1 year

1 to 3 years

3 to 5 years

5 to 10 years

> 10 years

Millennials

13.2

28.2

13.2

29.9

15.5

Gen X

9.1

16.9

12.6

15.6

45.9

Baby Boomers

10.0

17.1

13.9

22.3

36.7

Silent Gen

10.6

20.2

13.2

21.6

34.4

As for rewards preference, Table 5 shows the scores obtained for monetary and nonmonetary rewards across the different Generations, expressed on a scale from 1 to 7. An

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overall preference for monetary rewards over non-monetary rewards is shown; nevertheless, this difference is lower for Generation X and Millennials. Following Welch test (p = 0.000), the preference for monetary rewards differs among generations. Specifically, according to Tamhane’s T2 post-hoc, Silent Generation consumers display higher preference for monetary rewards than Millennials, Generation X and Baby Boomers. Conversely, results for the preference for non-monetary rewards display non-significant differences (ANOVA, p = 0.47). Table 5. Generations and preference for reward types Monetary rewards

Non-monetary rewards

Diff.

Millennials

5.95

4.62

1.33

Gen X

5.83

4.60

1.23

Baby Boomers

5.87

4.49

1.38 2.19

Silent Gen.

6.45

4.26

Mean

6.02

4.49

Finally, as for the ranking of a variety of rewards, significant differences were identified with respect to free products / services, free shipping, and special deals only for members (Table 6). According to Tamhane’s T2 post-hoc: – Baby Boomers show higher preference for free products / services than Silent Generation consumers; – Millennials and Generation X show higher preference for free shipping than and Baby Boomers; – Silent Generation consumers show higher preference for special deals only for members than Millennials and Baby Boomers.

Table 6. Generations and reward preferences Sum of squares

Df

Mean squares

F

Sign.

7.658

4

1.985

2.168

0.04

Free shipping

8.314

4

2.269

3.079

0.02

Special deals

14.647

4

3.662

4.314

0.02

Free products

Free products

(I)

(J)

(I-J)

SE

Sign.

Baby B.

Silent Gen

0.342

.106 0.02 (continued)

Generations and Their Preferences for Loyalty Program Rewards

63

Table 6. (continued)

Free shipping Special deals

(I)

(J)

(I-J)

SE

Sign.

Millennials

Baby B.

0.158

.155

0.04

Gen X

Baby B.

0.158

.120

0.02

Silent Gen

Millennials

0.586

.185

0.02

Baby B.

0.534

.181

0.04

Besides the aforementioned rewards, differences were not significant for most items. ANOVA was not significant for: surprises from the retailer, prizes catalog, gift upon reaching tier, chances to test (not purchase) new products. The Welch test was not significant for: discounts on products/services, contests / sweepstakes, free samples, and free birthday gifts. Moreover, ANOVA could not be computed for six items (access to free classes, e.g. cooking or yoga; priority customer care services; exclusive or early access to products, events or services; paid subscriptions with more benefit or exclusive services; membership to exclusive communities; and recognition on social media), since at least one group for each of them had variance equal to zero.

4 Conclusions and Implications Differences have been identified, between generations, with reference to supermarket loyalty program enrollment and rewards preferences. Specifically, a gap has been identified between Millennials and Generation X consumers on the one side, and Baby Boomers and Silent Generation on the other, where the first two cohorts show the highest percentage of enrollment. Also, given that Millennials and Generation X use more online channels and touchpoints for their grocery shopping retailers might consider working towards integrating and promoting their loyalty program through their website and mobile app. As regards rewards, no significant differences have been found among generations for non-monetary rewards. However, Silent Generation consumers have shown a higher preference for monetary rewards than the other cohorts. Since Millennials and Generation X have the highest length of relationship with the retailer, retailers interested in integrating non-monetary rewards in their programs might consider offering such alternatives later in the relationship with the customer. A final remark concerns the geographical context in which the study has been developed. On the one hand, results are tied to supermarket retailing practices in Italy; it could be interesting to run the study in the same industry in other countries. On the other hand, preferences of the Italian generational cohorts might be different, from a cultural point-of-view, from those of foreign consumers, although belonging to the same generation. Other cultures might place different value on monetary rewards and non-monetary ones. Also, the economic and financial situation of the country – think if the high inflation rates that may currently attract consumers to the savings provided by monetary rewards - might have had an impact on the overall predominant preference for monetary rewards expressed by Italian consumers.

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References Bies, S.M., Bronnenberg, B.J., Gijsbrechts, E.: How push messaging impacts consumer spending and reward redemption in store-loyalty programs. Int. J. Res. Mark. 38(4), 877–899 (2021) Bombaij, N.J., Dekimpe, M.G.: When do loyalty programs work? the moderating role of design, retailer-strategy, and country characteristics. Int. J. Res. Mark. 37(1), 175–195 (2020) Bridson, K., Evans, J., Hickman, M.: Assessing the relationship between loyalty program attributes, store satisfaction and store loyalty. J. Retail. Consum. Serv. 15(5), 364–374 (2008) Bruneau, V., Swaen, V., Zidda, P.: Are loyalty program members really engaged? measuring customer engagement with loyalty programs. J. Bus. Res. 91, 144–158 (2018) Chaabane, A.M., Volle, P.: Perceived benefits of loyalty programs: scale development and implications for relational strategies. J. Bus. Res. 63(1), 32–37 (2010) Eastman, J.K., Iyer, R., Eastman, K.L., Gordon-Wilson, S., Modi, P.: Reaching the price conscious consumer: the impact of personality, generational cohort and social media use. J. Consum. Behav. 20(4), 898–912 (2021) Gastwirth, J.L., Gel, Y.R., Miao, W.: The impact of Levene’s test of equality of variances on statistical theory and practice. Stat. Sci. 24(3), 343–360 (2009) Gerbing, D.W., Anderson, J.C.: An updated paradigm for scale development incorporating unidimensionality and its assessment. J. Mark. Res. 25, 186 (1988) Haverila, M.J., Haverila, K., McLaughlin, C., Tran, H.: The impact of tangible and intangible rewards on online loyalty program, brand engagement, and attitudinal loyalty. J. Market. Anal. 10(1), 64–81 (2022) Herhausen, D., Kleinlercher, K., Verhoef, P.C., Emrich, O., Rudolph, T.: Loyalty formation for different customer journey segments. J. Retail. 95(3), 9–29 (2019) Inglehart, R.: The Silent Revolution: Changing Values and Political Styles Among Western Publics. Princeton University Press, Princeton, NJ (1977) Jackson, V., Stoel, L., Brantley, A.: Mall attributes and shopping value: differences by gender and generational cohort. J. Retail. Consum. Serv. 18(1), 1–9 (2011) Kamal, S., Chu, S.C., Pedram, M.: Materialism, attitudes, and social media usage and their impact on purchase intention of luxury fashion goods among American and Arab young generations. J. Interact. Advert. 13(1), 27–40 (2013) Meyer-Waarden, L.: Effects of loyalty program rewards on store loyalty. J. Retail. Consum. Serv. 24, 22–32 (2015) Meriac, J.P., Woehr, D.J., Banister, C.: Generational differences in work ethic: an examination of measurement equivalence across three cohorts. J. Bus. Psychol. 25, 315–324 (2010) Nunnally, J.C.: Psychometric Theory 3E. Tata McGraw-Hill Education (1994) Schewe, C.D., Meredith, G.: Segmenting global markets by generational cohorts: determining motivations by age. J. Consum. Behav. Int. Res. Rev. 4(1), 51–63 (2004) Soares, R.R., Zhang, T.T., Proença, J.F., Kandampully, J.: Why are Generation Y consumers the most likely to complain and repurchase? J. Serv. Manag. 28(3), 520–540 (2017) Tahal, R.: Loyalty programs in e-commerce and their perception by the young adult internet population. Cent. Eur. Bus. Rev. 3(2), 7–13 (2014) Wind, Y.J., Hays, C.F.: Research implications of the “beyond advertising” paradigm: a model and roadmap for creating value through all media and non-media touchpoints. J. Advert. Res. 56(2), 142–158 (2016)

Investigating the Combinations of Target Products and Gifts: Metal Accounting Perspective Yi-Mu Chen(B) , Allen Chen, and I.-Hsuan Yang Department of Business Administration, I-Shou University, Kaohsiung, Taiwan [email protected]

Abstract. This study explores consumers’ perceived risk of the combination of the main product (tangible product/intangible service) and gift (free product/service upgrade). Metaling accounting theory is applied as a moderator between product-free gift combinations and perceived risk. The experimental design method is adopted to create four different situations to collect data, and a total of 152 valid samples are collected. Analysis of variance (ANOVA) was used for data analysis and hypothesis testing. The results show that product categories significantly differ in consumers’ perceived risk. The perceived risk of purchasing tangible products is less than the perceived risk of purchasing intangible services. Moreover, customers perceived the lowest risk while purchasing the combination of tangible products with free-gift; and the highest risk while purchasing the combination of intangible services with service upgrades. The moderating effect, consumers see the target product and gift in one or two accounts is not significant in this research. Relevant marketing suggestions from this research are provided. Keywords: Mental accounting · perceived risk · gift · upgrade

1 Introduction The advantage of price promotion is that in addition to providing monetary discount savings, it also shortens the time of buyers’ search and decision-making (Compeau and Grewal, 1998). Many substantive studies have found that price promotions can reduce consumers’ perceived risk and increase their willingness to purchase. However, the price promotion itself has also been criticized in part. These criticisms illustrate that consumers may doubt the authenticity of the price promotion and thus destroy the perceived quality. Therefore, some alternatives have emerged, such as non-price promotions with gifts. Consumers often perceive promotions as having a high value. The price reduction will reduce the money customer pay; that is, the sacrifices they make are reduced (Raghubir and Kim, 1999). Intangible services carry greater perceived risk than tangible products. This study will explore whether consumers’ perceived risk is different when the two types of promotions, gifts and upgrades, are paired with tangible products and intangible services. In addition, © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. C. Gázquez-Abad et al. (Eds.): NB&PL 2023, SPBE, pp. 65–69, 2023. https://doi.org/10.1007/978-3-031-32894-7_8

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how consumers identify the relationship between the main product and the gift, whether the two are one or two different accounts, is discussed in the study. The aims of this study are stated in the followings: (1) To understand the impact of different promotion types on consumers’ perceived risk. (2) Discuss the impact of target products (tangible/intangible) and promotions (gifts/upgraded services) on consumers’ perceived risk. (3) Discuss the moderating role of mental accounting.

2 Literature Review Perceived risk refers to the fact that consumers cannot predict whether the purchase result is correct when they make a purchase. Therefore, the uncertainty of the purchase result is implied in the consumer’s purchase decision (Bauer, 1960). The asymmetry of information between the sellers and buyers causes uncertainty in purchasing phase, and that is the customer’s risk. Jacoby and Kaplan (1972) proposed five dimensions of perceived risk, financial, performance, physical, psychological, social, and overall risk. Moreover, each risk dimension can be regarded as the expectation of future cost, and this future cost is the perceived value of the product relative to money at the time of purchase (Sweeney, Soutar, and Johnson, 1999). Products are tangible entities that can be seen and touched. On the other hand, services are intangible, heterogeneous, non-storable, and indivisible (Lovelock, 1996). Because of the characteristic of services, it is difficult to evaluate service quality (Parasuraman, Berry, and Zeithaml, 1991). Therefore, purchasing intangible services with higher risk than purchasing tangible products (H1). Providing gifts is a common promotion tool. In a consuming service setting, nonprice promotion, upgrade, might be a common promotion tool. The difference between the two is that price promotions have an economic goal (i.e., lowering prices), while nonprice promotions have both emotional and behavioral goals. Therefore, this study will explore the types of gifts (gifts/upgrade services) under the main product and compare perceived risks. It is reasonable that the target product’s price is higher than the gift price. Consumers should consider the target product as one of the main reasons for purchasing and then consider the value of the gift under the product when making a purchase decision. Gifts and upgrade services are free and provided by sellers to create a pleasant purchase situation and can reduce consumers’ perceived risks. Grove, Pickett, and Laband (1995) illustrated that service marketers should provide more factual information to reduce consumers’ perceived risk. Therefore, consumers perceive less risk when the purchasing combination is tangible/intangible target products with intangible/tangible gifts (H2). Mental accounting attempts to describe how an individual sorts money into different accounts, and the principle would affect his/her consumption and purchase decisionmaking behavior (Thaler, 1985). Thaler and Johnson (1990) confirmed that the happy editing principle would affect an individual’s decision-making and preferences; moreover, the principle assists in calculating the gain and loss in the account. Consumers are believed to decompose the situational events at the time of purchase into single or multiple events according to the principles; the gains and losses are determined to be separated or integrated (Thaler and Johnson, 1990). Therefore, we applied mental accounting to

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discuss the different combinations of target products and gifts. That consumers purchase the package of the combination of targeting products/services and gift/upgrades divided into one/two accounts, each corresponding to different purposes. In this research, we included how consumers identify the purchasing combination as one or two different accounts as a moderator to explore the relationship between the purchasing package (the combination of targeted products and gifts) and perceived risk (H3).

3 Methodology Four scenarios, 2(target product: tangible/intangible) X 2(gift: tangible/intangible), are created. A pilot test is conducted to select products. Flash drives (tangible products) and watching movies (intangible services) are designated as targeted products to control the price. The gifts would be a strap charm and a 7-day free antivirus trial software for flash drives; a 10% off coupon and a complimentary drink for watching movies. The measurement of perceived risk is adapted from Jacoby and Kaplan (1972). Forty questionnaires of each scenario are distributed, and 152 valid questionnaires are received. The participants were 18–25 years college students; 44.7% were men, and 58.7% with an average monthly allowance of less than NT$4,000. Furthermore, deal proneness was measured by each group. Table 1 shows that intangible target services with upgrades have higher deal proneness than the other groups. Table 1. Comparison of deal proneness Product/combination

Mean

S.D.

F-value 13.18

Flash drives + strap charm (P + P)

4.05

1.72

Flash drives + 7-day free antivirus trial software (P + S)

4.74

1.88

Watching movies + free drink (S + P)

4.16

1.84

Watching movies + 10% off coupon (S + S)

6.42

1.98

Note: P is product; S is service; the dependent variable is deal proneness

4 Results This study used one-way ANOVA to test the perceived risks in different product types, purchasing combinations, and consumer numbers. The research results are shown in Table 1. The results showed that the perceived risk of the flash drive would be significantly less than the perceived risk of watching movies (F-value = 236.35). They illustrated that consumers perceived less risk in tangible products than intangible services. Moreover, the perceived risk of combining a tangible target product with an intangible service would be significantly less than other combinations (F-value = 106.88). Furthermore, we included consumers’ perceived account numbers in the analysis. The results illustrated that the perceived risk of the combination of a tangible target

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product with an intangible service and the consumers’ perceived target product and gift in the same account would be significantly less than other combinations (F-value = 46.67). Table 2 shows a detailed comparison. Table 2. Testing results Product/combination

Mean

S.D.

F-value

Flash Drives

1.83

.48

236.35

Watching movies

3.59

.87

Flash drives + strap charm (P + P)

1.66

.43

Flash drives + 7-day free antivirus trial software (P + S)

2.02

.47

Watching movies + free drink (S + P)

3.97

.76

Watching movies + 10% off coupon (S + S)

3.20

.79

Flash drives + strap charm (P + P), 1 account

1.40

.32

Flash drives + strap charm (P + P), 2 account

1.74

.43

Flash drives + 7-day free antivirus trial software (P + S), 1 account

2.06

.37

Flash drives + 7-day free antivirus trial software (P + S), 2 account

2.00

.52

Watching movies + free drink (S + P), 1 account

4.07

.76

Watching movies + free drink (S + P), 2 account

3.72

.73

Watching movies + 10% off coupon (S + S), 1 account

3.29

.78

Watching movies + 10% off coupon (S + S), 2 account

3.14

.83

106.88

46.67

Note: P is product; S is service; the dependent variable is perceived risk

5 Conclusion Price promotions may bring a negative message to consumers, such as the poor quality of the target product. Providing non-price promotion strategies might be an alternative. In this study, we found that gift and upgrade promotions similarly affect purchasing tangible and intangible services. Drawing on the mental accounting perspective, consumers perceived less risk when they regarded the targeted intangible product and gift as two different accounts. The results of this study show that the perceived risk is lower when the main product and the complementary gift are of the same product type (e.g., they are both tangible products or both intangible services). This may be because consumers use the same attribute to evaluate the target product and gift. Accordingly, we suggest marketers choose the same type of gift as the target product in promotions to reduce perceived risk. The perceived risk is lower when the consumer identifies the main product and the gift as two different accounts. Therefore, we recommend that marketers choose gifts that differ from the main product or emphasize the differences when conducting advertising campaigns. This study integrates the discussion of the product categories

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of main products and complimentary gifts and the mental account theory to explore promotional campaigns, which can fill the gap in the promotional literature and marketing practices. Acknowledgments. The authors would like to thank the Ministry of Science and Technology of the Republic of China, Taiwan, for financially supporting this research under Contract No. MOST 110-2635-H-214-001.

References Bauer, R.A.: Consumer behavior as risk taking. In: Proceedings of the 43rd National Conference of the American Marketing Association. American Marketing Association, Chicago (1960) Compeau, L.D., Grewal, D.: Comparative price advertising: an integrative review. J. Public Policy Mark. 17, 257–273 (1998) Grove, S.J., Pickett, G.M., Laband, D.N.: An empirical examination of factual information content among service advertisements. Serv. Ind. J. 15(2), 203–215 (1995) Jacoby, J., Kaplan, L.B.: The component of perceived risk. In: Venkatesan, M. (ed.) Proceedings of 3rd Annual Conference. Association for Consumer Research, Urbana (1972) Lovelock, C.H., Yip, G.S.: Developing global strategies for service businesses. Calif. Manage. Rev. 38(2), 64–86 (1996) Parasuraman, A., Berry, L.L., Zeithaml, V.A.: Understanding customer expectations of service. Sloan Manag. Rev. 32(3), 39–48 (1991) Raghubir, P., Kim, C.: When do price promotion affect pretrial brand evaluations? J. Consum. Res. 36(2), 211–222 (1999) Sweeney, J.C., Soutar, G.N., Johnson, L.W.: The role of perceived risk in the quality-value relationship: a study in a retail environment. J. Retail. 75(1), 77–105 (1999) Thaler, R.: Mental accounting and consumer choice. Mark. Sci. 4(3), 199–214 (1985) Thaler, R.H., Johnson, E.J.: Gambling with the house money and trying to break even: the effects of prior outcomes on risky choice. Manage. Sci. 36(6), 643–660 (1990)

The Examination of Social and Service Relational Aspects on Customers’ Retention Zahy Ramadan(B) , Maya F. Farah, and Salwa Bekdache Adnan Kassar School of Business, Lebanese American University, Beirut, Lebanon [email protected]

Abstract. Consumers are increasingly joining social networking sites (SNS) where they build online relationships with friends and brands they engage with. While SNS related studies are growing, the Middle East region still lacks a proper understanding relating to SNS effects on customer retention within a service environment. This study aims to develop the understanding pertaining to SNS relational factors and the ensuing consequence of such bonding on the social platform’s advertising value, consumers’ proneness in comparing insurance premiums and the effect on retention. An internet-based survey was filled by 297 respondents on Facebook, targeting individuals who already use an insurance company’s services. The model measured the overall experience on Facebook while integrating socialization with friends, relationship with the social network itself, SNS ad value, premium comparison, and retention and customer service. This study is amongst the first to examine the effects of relational aspects, from a peer-to-peer and peerto-SNS perspectives, on the social ad value and the ensuing influence on price comparison propensity and the effects on customer retention in the region. Keywords: Facebook · Social networking sites · Insurance · Retention · Social advertising

1 Introduction Digital platforms have been growing at an accelerated rate, changing drastically companies’ business models through creating a network of users and producers in a web of interconnected ecosystems built mainly on customers’ interaction experience (Lythreatis et al., 2021). This interaction had a major effect on social media platforms. Throughout the past decades, and with the development of technology, consumers shifted their attention to online social networking sites (SNS) (Farah et al., 2022). In today’s highly digitized world, consumers are joining and creating communities in order to build online relationships (Al Shehhi et al., 2019). SNS users are growing their virtual connections even without face-to-face interactions that would have generated through offline encounters. Accordingly, SNS have taken a fundamental role in consumers’ life and became part of their collective social journey. Facebook (FB), the largest SNS with more than 2.96 billion active users worldwide, is built on a monetization model that helps brands become social entities that can interact with consumers (Ramadan et al., 2018b). The © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. C. Gázquez-Abad et al. (Eds.): NB&PL 2023, SPBE, pp. 70–79, 2023. https://doi.org/10.1007/978-3-031-32894-7_9

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platform enables the setup of brand pages to provide a social presence for brands aiming to engage with their audience through relevant and valuable content. Thus, media sharing platforms such as FB built brand communities and opportunity for marketers and users to share relevant needed information. Indeed, customers are increasingly becoming active participants and co-creators of content when they interact online. While SNS related studies are increasingly growing in the world, the Middle-East region still lacks a proper understanding relating to SNS effects on customer retention in a service setting. Accordingly, this research aims to compensate the literature gap relating to this region by examining the social and relational aspects of SNS on customer retention in a service related industry.

2 Literature Review The development of the conceptual framework incorporates the extant literature relating to online based relationships, social bonding and SNS related experiences (Akoury, 2020). Customers’ relationships with the social platform and the effect on the apparent perceived value of ads on the social network (hereby FB) are also integrated within the discussed framework (Ramadan et al., 2018a; Mrad & Cui, 2018; 2020). In a social networking site setting, friend likability is considered to be a key social factor that influences positively similarity with friends (Vallor, 2012). This bonding relates with a scheme of self-presentation that leads to an increase in similarity within a community (Shane-Simpson et al., 2020). Through an insurance sector setting, this study incorporates price sensitivity to the research framework. Insurance firms can place their ads on FB to promote their offerings. As insurance policies need customers to subscribe to them in the reasonably long run, retention is considered as a key business driver in this industry (Saleh et al., 2018a; 2018b). Many factors can affect customer retention, of which is price (premium) (Polo et al., 2011) and service quality (Boulding et al., 1993). 2.1 Consumer-Consumer Social Relations on the SNS In the sociology literature, similarity is considered as a “consciousness of kind” identified as “a state of consciousness in which any being, whether high or low in the scale of life, recognizes another conscious being as of like kind with itself ” (Giddings, 1896, p. 17). To know if individuals are like-minded, the “definition of the other” has to be made, which takes into consideration the person’s conduct that explains his/her behavior and personality. In a digital framework, commitment displays the concept of reciprocity alongside the sense of belonging to a group (Dholakia et al., 2004). FB empowers such friendships via reciprocity, empathy, and self-knowledge (Tóth, 2022). Therefore, FB friends tend to be like-minded individuals who are similar with regard to numerous socio demographic, and behavioral aspects. This study hypothesizes that: H1. The higher the peer-peer likability on the SNS, the stronger the feeling of similarity. In the social-psychology literature, liking is perceived as an attitude of an individual based on his/her emotional state involving personal bonding and sentimental and behavioral values (Tout et al., 2019). In the service sector, liking is considered as an ability

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to be comfortable with the other within a business relationship context. When people have a high level of likability, they are prone to have emotional dispositions toward the relationship as online friendships are built and supported in the same way as in a wider society (Zeeni et al., 2018; Thomas et al., 2019; Zeeni et al., 2021). Online friendships in a SNS environment promote similar feelings as closeness and intimacy can be maintained and further developed, affecting the SNS itself with the same emotional attachment (Ramadan et al., 2018). H2. The higher peer-to-peer likability, the higher the affective feelings toward the social platform. 2.2 Consumer–SNS Relationship Social media applications have allowed customers to network with business organizations, taking an active role in co-creating their experiences (Alnakhli et al., 2021). Under the perception of the social information processing theory (Farchakh et al., 2022), people are able to have stronger relationships online with others than in person (Engle-Warnick et al., 2020) due to the presence of an attractive image from both senders and receivers. This strong orientation on communication ensues with a high level of similarity. A stronger emotional bond is created between friends when a high level of peer-to-peer liking is formed alongside favorable emotions and feelings. Personal relational attachments are at the core of SNS related behaviors, which can increase the level of close emotions with others while socializing on the SNS (Awwad et al., 2018). Individuals express their feelings within the SNSs while maintaining and developing this bond. The strong emotions and trust bonds develop accordingly when the relationship is reliable (Islambouli et al., 2020) and when emotional feeling are well associated (Ramadan, 2018). H3. Similarity with friends leads to a higher attachment to the SNS (hereby FB) Users share their opinions, thoughts and feelings within the SNSs and can influence each other. When users socialize on FB, they can assess and share their opinions (Barakat et al., 2021) within their socially connected environment. Different studies demonstrated that when consumers recognized that advertising includes important information related to their needs, they react accordingly. Such reactions are affected by the SNSs consumers’ experiences and their connection with the social platform (Hayes et al., 2016). Consumers’ perception of the ad value is considered to be high when the ad has the ability to offer pertinent and practical information (Ramadan et al., 2018). Therefore: H4. Affective attachment towards FB leads to a higher perceived SNS ad value Pricing and advertising are effective marketing tactics used to improve firms’ revenues. Advertising can have direct and indirect effects on overall sales (Antounian et al., 2021). One of the indirect effects of advertising is price sensitivity. The effect of ads on price was studied in prior research whereby advertising was classified as being price oriented or non-price. Advertising offers relevant information to customers, that enhance

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both its quality perception and price, leading to a higher price sensitivity. We hypothesize that: H5. The higher the perceived value of insurance ads on FB, the higher the propensity to compare prices (hereby premiums). 2.3 Customer Retention In insurance companies, customer retention is based upon renewals and the length of time a recurring customer uses the company’s services. Consumers who preserve insurance policies with one company for a long time may be influenced by the bundling of services or by aspects such as loyalty and retention (Polo et al., 2011). Price is considered to be a prime determinant of such related customer behaviors (Dabbous & Tarhini, 2019). Bolton and Lemon (1999) considered a firm’s pricing structure to be depending on competitors’ pricing strategies and the relative value of the accessible services (Haj Yousef et al., 2019; Maalouf et al., 2020; Haj Youssef & Teng, 2021). Moreover, higher price perceptions lead to an enhanced efficacy of purchases, which would drive retention. This shows that price fairness influences consumer retention. Keaveney (1995) showed that high prices decrease retention. The price effect on consumer retention was mainly successful in the later phases of market liberalization (Bouri et al., 2022). H6. Propensity to compare premiums decreases customers’ retention with current insurance company. In the insurance market, customer service is a prime component in the retention of customers. In mature markets, customer satisfaction and the ensuing customer retention are considered to be highly important as companies look at the latter as a high return on investment surpassing new customers’ acquisitions (Abosedra & Sita, 2018; Azam et al., 2021). Prior studies took into consideration the link between service quality, customer satisfaction and retention and showed that service quality is the precursor of consumer satisfaction (Cronin et al., 2000). All in all, service can be a good support to retain customers (Yunis et al., 2018; Youssef et al., 2020). Service quality can be a significant antecedent for renewal rates. Furthermore, the changing behaviors of customers can actually be the result of service failure (Keaveney, 1995). The following is hypothesized: H7. The higher the perception of the current insurance company’s customer service, the higher the customers’ retention.

3 Methods The Middle-East region is short on studies pertaining to SNS social and relational effects on customer retention in a service setting, particularly the insurance sector. Indeed, there is a dire need to understand the effects of these relational aspects from a peer-to-peer and peer-SNS perspectives, when it comes to the value of social ads and their effect on price assessment within the context of this particular region. Accordingly, this study

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focuses on FB as it is the dominant platform in the Middle East. The objective of this study is to link users’ power of friendship within the SNS with their affective attachment to FB and its ad value. A survey was shared through a web link to active individuals on the SNS who use an insurance company’s services. The respondents aged between 25 and 56 years, with a gender split of 46% female and 54% male. Sixty-seven percent of sample were married individuals; and 92% had at least a university bachelor degree. Seventy-four percent of the respondents have used the same insurance company for the past 3 years and 62% have expressed their openness to try the services of new insurance providers. Participants were asked to re-share it with their network fitting the set criteria (snowballing sampling). The questionnaire enclosed three key sections. Part 1 enclosed questions regarding the respondents’ usage pattern of FB. Part 2 integrated the proposed constructs in the conceptual model, while part 3 included demographics related questions. The total number of filled questionnaires reached 297 entries, which were analysed using SPSS 20 and LISREL 8.8.

4 Results 4.1 Analysis and Constructs Validation Cronbach’s coefficients came to be greater than 0.70 for all constructs, reflecting a good internal consistency. Construct validity was tested using the average variance extracted (AVE) whereby a value of .50 or greater is considered as a sign for good validity (Fornell & Larcker, 1981). The AVE scores ranged between .60 and .84, indicating sufficient construct validity. Discriminant validity was tested through an exploratory factor analysis. All items loaded as expected with no cross-loading, providing support for discriminant validity. Confirmatory factor analysis was conducted to test the validity and items’ appropriateness for testing the hypotheses. IFI, CFI, GFI, and NFI were used in addition to the χ2 statistic to measure the suggested model’s fit to the data (Jöreskog and Sörbom, 2001): the measures pointed to a good fit level. χ2 was significant (χ 2 = 344 (149), P = 0.000). The model also had good fit indices: NFI = 0.954, IFI = 0.973, CFI = 0.973, GFI = 0.900 and RMSEA = 0.0641. 4.2 Model Estimation and Research Findings The estimation of the model shows a good fit with χ 2 = 416(162), P-Value = 0.00, NFI = 0.944, IFI = 0.965, CFI = 0.965, RMSEA = 0.0713, GFI = 0.879. The links between the constructs were significant, except the one from propensity to compare premiums to retention (H6) (see Fig. 1). The results display good support for the suggested model, where all hypotheses were supported except one. As hypothesized, friend liking has a positive influence on similarity with friends (H1: β = .702, p < .001). Friend liking was significant as expected on SNS affect (H2: β = .344, p < .001). Similarity with friends was also significant on SNS affect (H3: β = .319, p < .001). SNS affect also had a positive significant effect on SNS ad value (H4: β = .619, p < .001). SNS ad value was significant on propensity to compare premiums (H4: β = .236, p < .001). Nonetheless,

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propensity to compare premiums was not significant on retention (H6: β = .0309, not supported). Finally, customer service had a significant positive influence on retention (H7: β = .548, p < .001).

*significant at the p < 0.001 level Fig. 1. Model Estimation

5 Discussion, Implications and Future Research From a scholarly point of view, this study fills a major gap in the literature pertaining to the understanding of social interactions and bonding within an SNS on service-based firms and their customer retention (Itani et al., 2020). The model incorporated diverse key relational areas such as peer-peer and peer-SNS relationships, SNS ad value, premium comparison, retention and customer service. The findings first show that peer-peer likability has a positive effect on friend similarity, supporting the prior argument that friend likability increases connection between peers (Guerreiro et al., 2022) as well as the development of additional friendships. This type of effect between the two components has a direct consequence on friends’ similarities. In parallel, the social information the SNS provides creates chances to develop interpersonal relationships that could uphold emotional feelings (Kouatli, 2018). The bonding and similarity of members’ identity that develop between the SNS members seem to spillover to the social platform itself. The findings also show that a close peer to peer and peer to SNS relationship would influence the social platform’s ad perception. Such reactions are influenced by the SNSs customers’ experiences and their connection with the social platform (Hayes et al., 2016). Accordingly, social ads have the ability to offer pertinent, practical and precious information when customers’ perception of publicity value is considered to be high (Ramadan et al., 2018). The study however did not confirm the hypothesis linking premium comparison to customer retention. Indeed, while premium comparison has a directional negative relationship with customer retention, it is yet to be significant in the Middle-East and in the insurance industry where price sensitivity does not seem to be a determining factor on retaining clients. Nonetheless, customer service demonstrated a positive effect on retention in this study. This output was associated with different prior studies, which took into consideration the relation among service quality, consumer satisfaction and retention (Cronin et al., 2000).

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From a managerial perspective, the study provides insurance companies with a better understanding on relational aspects that are based upon social media platforms and are bound to potentially affect customer retention (Hamadeh et al., 2020). The findings show that FB is a key social facilitator in developing peer to peer and peer to SNS bonding and relationships, which would ultimately push customers to compare prices through a higher ad value perception. While this study provides a working direction for companies to focus more on social ads in order to avoid high propensities in premium comparison, the latter is yet to influence the retention of customers in a similar way that customer service does. Hence, managers are more active in integrating social networks as part of their integrated marketing communications (Mahdi et al., 2022). They have turned their thought to questions regarding the return on investment of social media (Itani et al., 2019a; Itani et al., 2019b; Itani, 2021). Customer retention is a crucial success factor for insurance companies as retention practices allow companies to address any strain being placed on cross-selling opportunities. Insurance companies are encouraged to assess which customers would be more inclined to purchase multiple products. Policy bundling is a tool available to insurance organizations and customers to aid in customer retention. It is important for a firm to create a strategy to ensure that consumers’ needs are known and met (Nieroda et al., 2019). Furthermore, the ability to maintain customers or clients is vital to a company’s success as it is costlier to acquire a client than to retain one (El-Khalil & Kassar, 2018; El-Khalil & Mezher, 2020; Khabsa et al., 2020). Past clients are often most likely to be future customers and good referrals. When clients share their experience about their insurance companies through the different stages of the buying process, SNSs can have major influences (Ramadan & Farah, 2020). This allotment can reflect on the buying process, product portfolio, price and customer service. This study is amongst the first to examine the effects of relational aspects, from a peer-to-peer and peer-SNS perspectives, on the social ad value and the ensuing influence on price comparison propensity in the Middle-East region. It measures the underlying end effect of such relationship and customer service on retention. The paper is not without limitations: as the research focuses on a specific region, a given service sector (insurance), and a particular SNS (FB), future research could study other markets alongside different service sectors given different SNS.

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Branding

Brand Attitude and Frugality as a Lifestyle: Evidence from Coffee Shop Customers. María Villavicencio1(B) and Walesska Schlesinger2 1 Universidad Católica de Cuenca, Cuenca, Ecuador [email protected] 2 Institute of International Economics, University of Valencia, Valencia, Spain [email protected]

Abstract. Recent years have witnessed a phenomenal change in the quantity and pattern of consumption, frugality has often been associated with resource-saving behavior that contributes to sustainable consumption. Thus, this study investigates consumer frugality and its relationship with brand attitude and purchase intention for green foodservice in an emerging market. A sample of 327 customers was taken from an icon ecological coffee shop in Ecuador. The positive effect on the relationship between frugality and green purchase intention and the mediating role of brand attitude in this relationship were empirically demonstrated. The results suggest that green marketing strategies in emerging markets should consider consumer beliefs and values as they have an impact on purchase intention. Furthermore, the importance of brand attitude in guiding sustainable behavioral intention. Keywords: Brand attitude · green purchase intention · green purchase behavior · frugal lifestyle

1 Introduction The existing literature describes frugality as a lifestyle trait, reflecting discipline and savings in the acquisition of products or services and ingenuity in using them. The frugal consumer feels good about saving money and resources, he is positive and optimistic (Young, 1996; Lastovicka et al., 1999). In this line, frugality may become a global mega trend, driven by the need of austerity and moderate lifestyles, thus arising the need for economic and environmentally responsible products and services in the postpandemic era (Manta et al., 2021). The current scenario for emerging economies, is not encouraging. The consequences of the COVID 19 pandemic can be seen in the historical inflation and economic decline worldwide. The possible effects of the pandemic on sustainability, in the context of changing consumption patterns, is a topic of study that attracts attention. Despite extensive research on the sustainable consumer in the European and North American context, this study has been neglected for several years in developing economies, even more so in the case of Latin American countries (Biswas & Roy, 2015). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. C. Gázquez-Abad et al. (Eds.): NB&PL 2023, SPBE, pp. 83–88, 2023. https://doi.org/10.1007/978-3-031-32894-7_10

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The research scenario for this study is Ecuador, considered an emerging economy according to its social and economic indicators. The study population consisted of customers of the Sweet & Coffee shop; a company with sustained success in the Ecuadorian market in its 22 years of experience and that has adopted green practices. It currently has 100 coffee shops nationwide and 1,200 employees. Additionally, it has been recognized as an icon brand in the country by the Marketing Hall of Fame Ecuador (Alvarado, 2020). Currently, the brand has four social responsibility projects, among them, the project “Our Pact with the Planet” aims to apply the “3Rs”: recycle, reduce and reuse. For this purpose, they use eco-friendly packaging made with recycled and biodegradable material to consequently reduce CO2 emissions. Furthermore, they raise awareness in society about the responsible use of disposable utensils (Chávez, 2020). In this context, the purpose of the present article is to examine the effect of frugality as a lifestyle on consumers’ sustainability consumption and thus analyze the relationship between frugality, brand attitude and purchase intention in the context of green catering. According to Theory of Planned Behavior (TBP) approach, attitude is considered vital to predict an individual’s behavior (Ajzen, 1991). According to Kuzniar et al. (2021), the connection between attitudes and behaviors is a complex process; thus, attitude is a key concept in consumer behavior research as it is attributed with the final decision on purchase decisions. The values and beliefs are formed according to the main attributes of the catering service, the associated benefits and by brand-related message judgments (Jeong et al., 2014; Liu et al., 2020; Hwang et al., 2021). Thus, when customers feel connected to the brand, they can show a strong attitude towards it (Foroudi et al., 2021). In the same way, Park (2020), mentions that the behavior and the intention of choice may vary depending on the brand attitude. The literature shows that as environmental awareness increases worldwide, performing environmentally friendly practices and building a green image could represent a competitive advantage (Mourad, 2012). Likewise, other studies have seen attitude as a function of the prominent beliefs of consumers about a product or service and evaluative judgment about how good or bad the brand is. Researchers have explained that brand attitude implies cognitive and affective measures, and these are linked to the consumer’s intention (Liu et al., 2020). In this way, the attitude towards the brand has both a direct and indirect impact on purchasing intention the products / services by consumers (Salehzadeh et al., 2021). In this sense, this study contributes to the theory of consumer behavior by measuring attitudes towards a brand, frugality and its influence on green purchase intention. According to the literature review, the following hypotheses are proposed: H1 proposes that frugality positively and significantly influences the green purchase intention; H2 that frugality positively and significantly influences brand attitude; H3 proposes that brand attitude positively and significantly influences the green purchase intention; H4 proposes that the effect of frugality on the green purchase intention is mediated by brand attitude.

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2 Data and Method The information was collected through a self-administered questionnaire, the convenience sample consisted of 327 customers of the ecological coffee shop Sweet & Coffee. The sample was represented by 55.7% of women, most of the participants are between 20 and 29 years old and have a university education; up to 75% of the participants reported earnings of less than $ 880; in addition, 41% of the respondents purchase 1 or 2 times a month and 27% purchase more than 3 times a month. The measurement of frugality was done by using the scale proposed by Evers et al. (2018) with eight items. Brand attitude was measured using the three items of the Qi & Ploeger’s (2019) scale. For measuring purchasing intention, the scale proposed by Kim et al. (2013) was applied. The model was tested by applying a partial least squares structural equation modelling (PLS-SEM) analysis because it is a method that reaches high statistical power and is widely applied in many disciplines of social sciences, including marketing management (Hair et al., 2019). It also allowed to perform the analysis from a predictive approach. The data analysis was carried out in two steps: the first, analyzing the measurement model, and the second, testing the structural relationships between the latent variables.

3 Results During the initial estimation, the reliability of the indicators of the various scales was tested. Discriminant validity was tested by comparing the square root of the AVE and the correlations with its factors, as well as the HTMT-ratios method (Henseler et al., 2014). Parameter significance estimation (bootstrapping) was performed with 5,000 subsamples and the analysis shows that H1, H2, H3, H4 have been supported. In reference to the analysis of the indirect effect, we proceeded with the calculation of the variance explained (VAF) resulting in 53.45% indicating a case of partial mediation (Nitz et al., 2016). This shows empirically that the brand attitude has both a direct and indirect impact on purchase intention (Salehzadeh et al., 2021) (Fig. 1).

Fig. 1. Proposed structural model

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4 Conclusions This study developed a suitable theoretical model that might be able to explain frugality as an antecedent variable of green purchasing intention. The empirical analysis of the model proved that brand attitude has a partial mediating role in the relationship between frugality and green purchasing intention (H4). Attitude, a variable that has a theoretical basis in the TPB, and that allows to understand the intention of consumers towards a behavior that respects the environment (Chen & Tung, 2014), resulted to be a relevant construct in the proposed model. The acceptance of this hypothesis is of great relevance in the field of green marketing, since attitude has been considered a good predictor of the purchase intention of green restoration services. 4.1 Implications for Practice, Limitations and Future Research Directions The research provided a unique viewpoint: consumer decisions can be partially explained by customer psychological factors such as frugality. It has also proposed a model that considers customers’ attitudes toward a green restaurant, with the notion that brand attitude is an important construct for understanding behavioral intentions (Ajzeny Fishbein, 2000; Fishbein and Ajzen, 1975; Jeong et al., 2014). Therefore, the study suggests that, in order to improve consumers’ purchase intention towards green foodservice, based on certain values related to sustainable consumption, favorable brand attitudes should first be created through experiential marketing programs and green advertising appeals (Liu et al., 2020). Thus, marketers should consider in their analysis, personal behavioral beliefs to attract consumers’ favorable attitude. Furthermore, the consideration of the condition of customer or not as moderate variable may be an interesting option for future research. The results of this study are important, but it does have some limitations which offer several interesting lines for future research. Among them, it is relevant to mention the size of the sample and the fact that it was applied to one country and to a specific service and brand. This aspect must be considered when generalizing the results, however, previous studies have also focused only on one brand (Jeong et al., 2014). Additionally, future research could include variables such as brand preference, green image perception and purchase behavior in the model. As the existence of a partial mediation in the model has been validated, a variety of constructs that could also positively mediate the relationship can come out (Hayes, 2018).

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The Mediating Role of Self-image Congruence and Perceived Product Quality on the Relationship Between Brand Personality and Brand Equity in the Belgian Beer Market Johan Hellemans(B) , Kim Willems, and Malaika Brengman Department of Business – Marketing & Consumer Behavior Cluster, Imec-SMIT, Vrije Universiteit Brussel, Brussels, Belgium [email protected]

Abstract. This study highlights the relationship between brand personality (BP) and customer-based brand equity (CBBE) regarding brand commitment and preference by examining the mediating role of self-image congruence and perceived product quality in the beer market. Thus, we make some significant contributions to the existing literature on BP by applying the BP concept to beer brands and by including the mediating role of self-image congruence, which has been limitedly addressed in previous studies. Further, the impact of BP is contrasted with perceived product quality, which offers brand managers insight into what kind of associations they must pursue. Data were collected from 322 student legal-age beer drinkers (age = 21.3, SD = 1.5, Female = 45%), who evaluated 2 out of 4 leading Belgian national beer brands, yielding 644 brand observations. Our PLS path model results show that the impact of BP factors on brand equity in the beer market is fully mediated by self-image congruence and perceived product quality. Both self-image congruence and perceived product quality significantly affect brand equity. The BP dimension activity reveals the most substantial total effect, given its impact on self-image congruence and perceived product quality. Also, the BP factor responsibility influences brand equity. The message is clear. For a national beer brand to stay relevant amongst young adults, it must convey a dynamic, active, and innovative BP while paying attention to its perceived quality. Other potential routes are to communicate facets of responsibility. Keywords: Brand personality · Perceived product quality · Self-image congruence · Beer · Brand equity

1 Introduction The brewing industry is witnessing fast-changing consumer preferences, which pushes beer companies to revise their strategies (Andrzejewska, 2013). Also, in Belgium, industry data shows a steady decline in beer consumption and a shift from core lager pilsner to more specialty beer. Consumer preferences are steered by utilitarian appeal and social © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. C. Gázquez-Abad et al. (Eds.): NB&PL 2023, SPBE, pp. 89–99, 2023. https://doi.org/10.1007/978-3-031-32894-7_11

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and expressive values (Aaker, 1997; Fournier, 1998). In mature markets like beer, intangible brand associations like brand personality (BP) can offer a competitive advantage (Eisend & Stokburger-Sauer, 2013). BP is an essential tool to differentiate a brand in the market and drive consumer preference (Aaker & Fournier, 1995; Valette-Florence, Guizani, & Merunka, 2011).

2 Objectives The purpose of the current study is to examine the mediating role of self-image congruence in the relationship between the BP of four national Belgian beer brands and their brand equity in terms of brand commitment and preference. This paper offers several unique contributions to the field of BP; it contrasts BP in a concurrent model with perceived product quality. Second, the mediating role of self-congruence between BP and brand equity is examined. Finally, it is one of the first papers that applies the BP concept using a systematic BP measurement in the beer market. For brand managers, it offers insight into potential routes for beer brands to differentiate themselves and increase brand equity.

3 Literature Review 3.1 Brand Equity While there has yet to be a consensus on the definition and dimensions of brand equity, the dimensions of brand awareness, perceived quality, brand associations, and brand loyalty proposed by Aaker (1997) are widely accepted. Within a consumer-based brand perspective (CBBE), the brand is seen as an abstract notion that resides only within the consumer’s mind, formed by its associative memory network (Keller, 1993). Within this view, the source of any brand equity lies intrinsically in the brand-knowledge structures of consumers that impact the response to a brand favourably. Furthermore, intangible associations, like BP, are seen as part of such brand memory networks (Christodoulides & De Chernatony, 2010; Keller, 1993; Koll & von Wallpach, 2014). Brand equity can be built following two conceptual routes, either based on associations linked to the product and are more tangible or based on associations with no inherent product value and are more intangible in nature (Bhat & Reddy, 1998). It follows that the fundamental task of any brand manager is to understand and manage the set of associations around their brand as an essential component of brand equity (Aaker, 1997). 3.2 Brand Personality Initially set on the academic research agenda in the mid-90s by Aaker (1997), the concept of BP and its impact has gained wide popularity amongst scholars. Aaker (1997) defines BP formally as “the set of human characteristics associated with a brand.“ A significant body of research examined the antecedents and consequences of BP. Nonetheless, research in this area still needs to be completed (Eisend & Stokburger-Sauer, 2013; Radler, 2018; Saeed, Burki, Dahlstrom, & Zameer, 2022). Most BP research has been

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deployed in a limited number of product categories, concentrated in a few countries (Saeed, Burki, Dahlstrom, & Zameer, 2022). So, despite this interest, very little research has systematically focused on the role of BP in the beer industry. A quick review of the literature on WOS, Scopus, and Dimensions searching on (“brand personality” and beer) reveals only 4 English studies of interest. Andrzejewska (2013) reports on a successful campaign case of the Czech beer Brand Budweiser on the Polish market, illustrating how the brand persona, expressed by a hero, allowed the brand to easily convey the character of the brand. A qualitative study by Seimiene and Kamarauskaite (2014) indicates that marketing mix elements like packaging design, advertisements, names, and the typical brand user probably impacted the beer brands’ personalities. Another qualitative research reports on two projective personification techniques of celebrity mood boards and job associations with four beer brands to highlight differences between the brands under investigation. In a subsequent exercise, personality attributes related to six underlying personality dimensions were associated with each constructed projective profile. A clear profile emerged with all brands showing highest association with competence and excitement as beer’s most important personality dimensions. However, differences between brands in the interpretation of the projective outcomes did not lead to differences between brands on the BP level (Hofstede, van Hoof, Walenberg, & de Jong, 2007). Lastly, big data analytics of transactional Taiwanese retail data showed that consumers purchase brands according to their personality traits. Co-occurrences were analysed to determine beer purchase patterns and generate a perceptual mapping of 74 beer brands that yielded five distinctive groups of brands, each associated with a brand personality factor related to the country-of-origin traits, to predict potential customer lifetime value (CLV). In this analysis, consumers within the group of personality traits related to “peacefulness” and “openness” showed higher CLV (Chiang & Yang, 2018). While reviews of the literature show a positive effect of BP on a vast array of brandrelated outcomes related to brand equity (Saeed, Burki, Dahlstrom, & Zameer, 2022), BP is seldom contrasted against other dimensions of brand equity. Research by Yoo, Donthu, and Lee (2000) investigating 3 product categories; athletic shoes, camera film, and color television sets, shows that both perceived product quality and brand imagery impact brand equity in a significant way. However, the impact might differ according to the specific industry. Results from the fast fashion industry show no effect of both on brand loyalty. In a concurrent model with other factors, perceived quality and BP are not seen as decisive purchase factors among young people in this market (Su & Chang, 2018). In contradiction, investigating the same industry, the perceived quality did have a direct effect, while the effect of BP was indirectly mediated via perceived quality in a sample of adults (Su, 2016). Brand personality theory leans heavily towards self-congruence theory from social psychology (Radler, 2018). Previous empirical work in this area indicates that the fit between perceived brand image and a consumer’s self-concept can also yield a range of positive consumer and marketing outcomes like brand commitment and brand preferences (Eisend & Stokburger-Sauer, 2013; Aguirre-Rodriguez, Bosnjak, & Sirgy, 2012; Sirgy, 2014, Sirgy, Lee, & Yu, 2017). Research in this area models self-image

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congruence as a mediator of BP and brand equity-related outcomes (Matzler, Strobl, Stokburger-Sauer, Bobovnicky, & Bauer, 2016; Usakli & Baloglu, 2011). 3.3 Conceptual Model and Hypotheses Based on these findings, we applied a similar measurement model as Matzler, Strobl, Stokburger-Sauer, Bobovnicky, and Bauer (2016) and Usakli and Baloglu (2011) in a beer context. We hypothesize that (H1) brand equity for beer brands is positively influenced by self-image congruence (Matzler, Strobl, Stokburger-Sauer, Bobovnicky, & Bauer, 2016; Usakli & Baloglu, 2011). Given the work of Yoo, Donthu, and Lee (2000), Su and Chang (2018), and Su (2016), we developed the following hypotheses: (H2) BP positively impacts brand equity, (H3) perceived product quality positively impacts brand equity, (H4) BP positively impacts perceived product quality, (H5) self-image congruence positively impacts perceived product quality, (H6) BP impacts self-image congruence (H7) perceived product quality mediates the relationship between BP and brand equity. Finally, following Matzler, Strobl, Stokburger-Sauer, Bobovnicky, and Bauer (2016), we hypothesize that (H8) self-image congruence mediates the relationship between BP via actual self-image congruence (Fig. 1).

Fig. 1. Conceptual model

4 Methodology 4.1 Sampling An online self-completed questionnaire that guaranteed anonymity was administered to a Belgian university population (18 to 24 years old) in the context of a market research course. In total, 322 respondents passed the age and beer usage filter of drinking beer at least once (age = 21.3, SD = 1.5, Female = 45%). Among those, 25% of respondents said they drink beer almost every day (4 to 7 times per week or more), 60% reported drinking beer 1 to 3 times a week, while 15% drink less frequently (1 to 3 times a

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month). Men were almost three times more likely than women to drink beer almost every day (35% versus 12%). There was no significant correlation between general beer consumption frequency and any of the variables of interest, except for BEQ, which showed a weak positive correlation (r = .10). Brand use instead was included as a covariate in the analyses as a dummy, given it correlated with all endogenous variables and beer frequency. For this exercise, each of the 322 respondents was randomly assigned two beer brands out of a total of four selected national beer brands, resulting in 644 observations in total. We chose the following beer brands because they represent a diverse range of popular Belgian beer types and are considered the leading national brands within their respective subcategories. The selected brands and their number of users in our sample include Jupiler, used by 79% (lager); Duvel, used by 49% (strong blond); Hoegaarden, used by 14% (white beer); and Leffe, used by 13% (abbey beer), which together account for over 50% of the market share by volume, according to reliable personal sources. As a stimulus, each respondent got the beer brand’s logo with or without the accompanying glass. This manipulation was not of interest to the current investigation, but we controlled for its effect in our proposed model. 4.2 Measures The following 7-point scales were deployed to measure the necessary constructs to test our hypotheses. Participants rated the following four items measuring brand selfimage congruency with their actual self (SCA) to reflect a self-maintenance motive (adapted from Sirgy et al., 1997), anchored as not at all/completely: (1) is consistent with how I see myself; (2) reflects who I am; (3) is very similar to myself, and (4) is used by people similar to me. The brand equity (BEQ) measures were adapted from Wang (2002) and Yi and Jeon (2003) and rated on a Likert agreement scale: via (1) I consider myself a loyal user of this beer brand; (2) if asked, I would say good things about this beer brand; (3) I feel a strong connection to this beer brand; (4) I like this brand more than other beer brands; (5) I have a strong preference for this beer brand; (6) I would definitely recommend this beer brand to friends. The 12-item BP scale of Geuens, Weijters, and De Wulf’s (2009) was used, anchored as not at all/completely, including the following dimensions and their respective items: responsibility with (1) down-to-earth, (2) stable, and (3) responsible; activity with (4) active, (5) dynamic, and (6) innovative; aggressiveness with (7) aggressive, and (8) bold; simplicity with (9) ordinary, and (10) simple; and finally emotionality with (11) sentimental, and (12) romantic. Perceived product quality (PPQ) was measured by generic abstract quality items to reflect the consumer’s judgment about a product’s overall excellence or superiority in a similar vein as Yoo, Donthu, and Lee (2000) and Su (2018) adapted to a beer context: (1) overall, I would say that this beer brand has outstanding quality; (2) the beer of this brand has a very good quality; (3) this beer brand is well made; (4) this beer brand is brewed with craftsmanship; (5) this beer brand is brewed with quality ingredients; (6) this beer brand has an excellent taste.

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4.3 Measurement Model SmartPLS-software validated the measurement and structural model (Ringle, Wende, & Becker, 2015). The model with the original five factors of Geuens, Weijters, and De Wulf (2009) shows adequate reliability and discriminant validity. All latent factors’ composite reliabilities scored above 0.8, and all items have significant (p < .05) factor loadings above .75, except for down-to-earth with a factor loading of .62 on responsibility. Further analysis showed convergent, discriminant validity by the respective standards of Fornell and Larker (1981) and Henseler, Ringle, and Sarstedt (2015). Multicollinearity was assessed, showing no common method variance problems (Kock, 2015).

5 Results We ran a bootstrap resampling method with 5000 resamples to estimate the structural model. The path coefficients and significance levels are shown in Fig. 2. Our model explains 72% of the variance in brand equity (R2 Adj. = .72, p < .05). For completeness, the R2 adjusted values of the other endogenous variables are also reported in Fig. 2. We further controlled for the brand’s usage, the stimulus manipulation, and the brands themselves by dummy variables (not shown on the graph). We added the average scores of the summed items, SD, and correlations for descriptive reasons in Table 1. The results reported are further summarized in Table 2, showing the total and indirect effects of interest and significance levels and the bias-corrected confidence intervals. Table 1. Mean, SD and correlations between measures

responsibility (1)

Mean

SD

1

4.00

1.0

1

2

3

4

5

6

7

activity (2)

3.88

1.0

.33*

1

simplicity (3)

4.27

1.2

.36*

−.10*

1

emotionality (4)

3.56

1.2

.42*

.34*

−.02

1

aggressiveness (5)

3.65

1.2

.02

.48*

−.06

.11*

1

SCA (6)

4.88

1.1

.29*

.46*

.00

.29*

.29*

1

PPQ (7)

3.13

1.3

.19*

.38*

−.15*

.16*

.14*

.47*

1

BEQ (8)

3.99

1.5

.17*

.39*

−.06

.19*

.22*

.69*

.72*

* p < .05

As shown in Table 2, most of the hypotheses are supported, showing that both self-image congruence (SCA) (b = .54, t = 15.38, p < .05, [.47, .61]) and perceived product quality (PPQ) (b = .50, t = 17.16, p < .05, [.44, .56]) have a significant positive total effect on brand equity supporting hypothesis H1 and H3. Brand equity is further positively impacted by BP factor activity (b = .22, t = 4.84, p < .05, [.14, .32]), and responsibility (b = .14, t = 3.68, p < .05, [.06, .21]) supporting hypothesis H3. Also, in

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Fig. 2. Path diagram (b, p-value)

relation to perceived product quality we find some support for hypothesis 4 depending on the BP-facet. While BP dimensions activity (b = .26, t = 4.74, p < .05, [.15, .37]) and responsibility (b = .21, t = 5.3, p < .05, [.12, .28]) show a significant positive total effect, simplicity (b = -.15, t = 3.17, p < .05, [−.24, −.05]) shows a significant total negative effect on perceived product quality. In support of hypothesis 5, actual selfimage congruence (SCA) is significantly positively related to perceived product quality (b = .31, t = 7.5, p < .05, [.24, .40]). Hypothesis 6 is supported with actual self-image congruence being significantly positively impacted by activity (b = .25, t = 5.84, p < .05, [.17, .34]), responsibility (b = .16, t = 4.05, p < .05, [.08, .23]), emotionality (b = .15, t = 3.87, p < .05, [.07, .22]), and aggressiveness (b = 0.10, t = 2.39, p < .05, [.01, .18]). The hypothesized mediation effect of perceived product quality (H7) between BP and brand equity is supported for activity (b = .09, t = 3.33, p < .05, [.04, .15]) and responsibility (b = .08, t = 3.97, p < .05, [.04, .12]) with a significant positive indirect effect shown, and a non-significant direct path. The hypothesized mediation effect of actual self-image congruence between BP and brand equity (H8) is supported for activity (b = .10, t = 5.22, p < .05, [.06, .14]), responsibility (b = .06, t = 3.71, p < .05, [.03, .09]), emotionality (b = .06, t = 3.7, p < .05, [.03, .09]) and aggressiveness (b = .04, t = 2.32, p < .05, [.01, .07]). In relationship to our control variables (not shown) we could observe a positive effect of usage on all three endogenous variables. The manipulation of stimulus did not show any effect.

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J. Hellemans et al. Table 2. Total and specific indirect effects of interest

TOTAL EFFECTS

B (M)

T

P

95%RI

SCA - > BEQ

0.54

15.38

0.00

0.47

0.61

BEQ - > BEQ

0.50

17.16

0.00

0.44

0.56

ACTIVITY - > BEQ

0.22

4.84

0.00

0.14

0.32

RESPONSIBILITY- > BEQ

0.14

3.68

0.00

0.06

0.21

EMOTIONALITY - > BEQ

0.06

1.66

0.10

−0.02

0.13

AGRESSIVENESS - > BEQ

0.03

0.66

0.51

−0.06

0.11

−0.08

1.83

0.07

−0.15

0.01

SCA - > PPQ

0.31

7.50

0.00

0.24

0.40

ACTIVITY - > PPQ

0.26

4.74

0.00

0.15

0.37

RESPONSIBILITY - > PPQ

0.21

5.30

0.00

0.12

0.28

EMOTIONALITY - > PPQ

−0.01

0.25

0.80

−0.09

0.07

AGRESSIVENESS - > PPQ

−0.03

0.66

0.51

−0.14

0.06

SIMPLICITY - > PPQ

−0.15

3.17

0.00

−0.24

−0.05

ACTIVITY - > SCA

0.25

5.84

0.00

0.17

0.34

RESPONSIBILITY - > SCA

0.16

4.05

0.00

0.08

0.23

EMOTIONALITY - > SCA

0.15

3.87

0.00

0.07

0.22

SIMPLICITY - > BEQ

AGRESSIVENESS - > SCA

0.10

2.39

0.02

0.01

0.18

−0.04

0.93

0.35

−0.12

0.04

ACTIVITY - > BEQ

0.23

5.80

0.00

0.15

0.31

ACTIVITY - > SCA - > BEQ

0.10

5.22

0.00

0.06

0.14

ACTIVITY - > PPQ - > BEQ

0.09

3.33

0.00

0.04

0.15

RESPONSIBILITY - > BEQ

0.17

5.73

0.00

0.11

0.22

RESPONSIBILITY - > SCA - > BEQ

0.06

3.71

0.00

0.03

0.09

RESPONSIBILITY - > PPQ - > BEQ

0.08

3.97

0.00

0.04

0.12

EMOTIONALITY - > BEQ

0.05

1.76

0.08

−0.01

0.11

EMOTIONALITY - > SCA - > BEQ

0.06

3.70

0.00

0.03

0.09

EMOTIONALITY - > PPQ - > BEQ

−0.03

1.49

0.14

−0.07

0.01

AGRESSIVENESS - > BEQ

0.02

0.64

0.52

−0.05

0.09

AGRESSIVENESS - > SCA - > BEQ

0.04

2.32

0.02

0.01

0.07

SIMPLICITY - > SCA SPECIFIC INDIRECT EFFECTS

AGRESSIVENESS - > PPQ - > BEQ

−0.03

1.34

0.18

−0.08

0.01

SIMPLICITY - > BEQ

−0.09

2.73

0.01

−0.15

−0.02

SIMPLICITY - > SCA - > BEQ

−0.02

0.92

0.36

−0.05

0.02

SIMPLICITY - > PPQ - > BEQ

−0.07

3.02

0.00

−0.11

−0.02

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6 Conclusions This study is one of the first to focus on brand personality in the beer industry, within the Belgian market. Both perceived product quality and brand personality are important factors in achieving brand equity for national beer brands. The study found that brand personality can be a key driver of brand equity, especially when it aligns with consumers’ personalities. The dimensions of brand personality that had the greatest impact on brand equity were activity and responsibility, which were fully mediated by self-image congruence and perceived brand quality. National beer brands targeting young adults should aim to convey a dynamic, active, and innovative brand personality while maintaining their perceived quality. Brand managers can also pursue responsibility as an additional route to enhance brand equity. Beer brands often emphasize product quality through historical associations, brewing processes and ingredients, packaging cues, and country of origin associations. Brand managers are well advised to consider how these product attributes also impact brand personality. Most prominent sources of achieving an active personality would be the use of endorsers (e.g., master brewer), brand logo, and advertising style (Maehle & Supphelen, 2011; Seimiene and Kamarauskaite, 2014). In addition to marketing communication, Lara-Rodríguez, Rojas-Contreras, and Duque Oliva (2019) suggest a distinct visual identity (e.g., bottle, colours, logo) and brand experience (e.g., glass, sponsorship, brand extensions, on trade locations). The sample for this study was limited to a student population using a cross-sectional approach. Our findings might not be generalizable to all beer drinkers and brands. Future research can confirm whether the relationships found in the current study apply to other consumers, cultures, and brands. Such research could also investigate context factors, like specific occasions of consumption, as moderators of the relations found. We expect the relations to be stronger in a more conspicuous context related to outdoor drinking than in-home drinking. Future research could also validate the suggested sources of BP proposed for beer brands.

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Sustainable Brands and Retail: A Bibliometric Analysis Emili Vizuete-Luciano(B) , Miguel Guillén-Pujadas, David Alaminos, María Luisa Solé-Moro, and Ana María Argila-Irurita Business Department, University of Barcelona, Barcelona, Spain {evizuetel,miguel.guillen,alaminos,mlsolesolel,aargila}@ub.edu

Abstract. The sustainable development of the economy has become a priority at the international level and brands and consumers cannot remain oblivious to this paradigm shift. This subject has been gaining relevance among the scientific community, hence the importance of its study and the need to have an updated record of the work carried out at an academic level. Consequently, the aim of this paper is to perform a bibliometric analysis of publications on sustainable brands and sustainability retail to know the current state of scientific production. To this end, 3,228 research papers published between 1995 and 2022 have been analyzed, based on the results of the Web of Science Core Collection (WoS), identifying publications and co-authorships among the best-known authors, as well as the countries with the highest production of articles, citations, the most prominent academic institutions, the most influential journals, and the co-occurrence of keywords. The results obtained show that there is a high scientific production, which is expected to continue increasing. In addition, future lines of research contemplate the study of other factors such as the supply chain, as well as consumer satisfaction and the development of other lines of research regarding more sustainable consumption in the long term. Keywords: Sustainable Brands · Sustainability Retail · Bibliometrics · Web of Science · Citations

1 Introduction In recent years, sustainability has become a well-known term among businesses, consumers and citizens of the Earth. Sustainability seeks to protect the planet after the serious effects of economic development in the 20th and 21st centuries, which have been observed with some concern; it seeks to curb climate change and social divergences that have seriously endangered the resources that will be available to future generations. To think about sustainability is to think that things can be done in a different way, more conscious of the elements that surround us. It is possible to generate economic growth and development while caring for the planet, trying to avoid the generation of greenhouse gases and taking measures to counteract the devastating effects of climate change. The concept of sustainability that we are so aware of today is a term that appears in the so-called Brundtland Report, entitled “Our Common Future”, published by the © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. C. Gázquez-Abad et al. (Eds.): NB&PL 2023, SPBE, pp. 100–115, 2023. https://doi.org/10.1007/978-3-031-32894-7_12

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United Nations in 1987, which warns of the risks of continuing to promote the growth of countries in the excessive consumption of resources, trying to present solutions to the serious problems we are facing. It will not be until 2010, with the celebration of the Millennium Summit, that the Millennium Development Goals (MDGs) to be achieved by 2015 will be agreed upon. At the end of this period, the agreements evolved into what we know today as the 2030 Agenda, which is presented as a strategic plan for the development of people and the planet and was signed by 193 Member States, in which, for its purpose, the achievement of 17 Sustainable Development Goals is observed, in which we can find 169 goals that can be grouped into three major themes: the economy, social development and the environment. In 2023, at the global level, it is the 2030 Agenda that marks the development followed by countries and companies with the aim of achieving sustainable development. For this reason, we will study different aspects to assess the impact of citations in scientific publications through the different factors that we have observed. With all this, it will be possible to determine the aspects that have already been extensively studied by the scientific community and to identify the factors that have a potential for future development. Since scientific mapping and stakeholder analysis are complementary studies, we will use them together to approach all these lines of study in the most accurate way. We can affirm that our study allows us to reliably observe the most prominent scientific aspects as well as the most relevant authors who have helped to develop the topic of sustainable brands and sustainability retail (Van Eck and Waltman, 2007; Chen et al., 2017). Several Journals have produced additional issues, special articles and letters to highlight an event related to the journal itself or its field of study (Baier-Fuentes et al. 2021). Bibliometric analyses are very important at these key moments because they allow us to determine the current state of the art and encompass everything that has happened in that period of time. In this case, the relevance of sustainability as such, we can affirm that we are at a turning point, where there is room for reflection and analysis. The rest of the paper is structured as follows. Section 2 shows the formal aspects of the methodology used to develop this paper, Sect. 3 presents the main results of the factor performance analysis and scientific mapping of the literature published in recent years on sustainable brands and sustainability retail topics. Finally, we will find Sect. 4, where a description of the most noteworthy aspects of the study, future lines of research and the conclusions of the analysis carried out will be made.

2 Methodology In bibliometric analysis, different methodologies are used such as quantitative analysis and performance analysis as well as scientific mapping through the use of different applications (Noyons et al. 1999; Cobo et al. 2011). The objective of this type of analysis is to have a broad and clear vision of the contributions that researchers have made in this field. This technique has had extensive development among different researchers who have made their contributions (GuillénPujadas et al. 2022).

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In the paper that we develop, we will apply both quantitative analysis and scientific mapping in the records obtained on the different topics that we used: sustainable brands and sustainability retail (Fig. 1).

Asse mbli ng

Identification Domain: Sustainable brands & sustainability retail Research questions:1. What are the most current research trends, the most influential articles that have been published, and the most contributing journals in the area of sustainable brands and sustainability retail? 2. What is the intellectual framework of the research at the moment? 3a. What are the topics related to our research area? 3b. What are the lines of research that present the greatest potential? Source quality: Web of Science (WoS) Source type: Journals

Acquisition Search period: 1990 to 2022 Search keywords: " Sustainable brands " and " sustainability retail " Total number of publications: n = 4,374

Arr angi ng

Purification Filtered language: English (4,249) n = 4,249 Filtered document type: Articles (3,228) n = 3,228

Asse ssin g

Evaluation Analysis method: Bibliometric Analysis; Namely: Co-Citation Analysis, Bibliographic Coupling, Cooccurrence Analysis Agenda proposal method: Present the current trends of the research and gaps, and areas for future research

Reporting Reporting conventions: Figures, tables, graphs, words Limitations: Data from the WoS Database, Language of the data Source of support: No funding

Fig. 1. Procedure of the study based on the SPAR-4-SLR Protocol.

Currently there is no bibliometric review in this area of research, with this study we intend to analyze the reality in which they are found. To do this, we posed the following research questions: • Question 1. What are the most current research trends, the most influential articles that have been published, and the most contributing journals in the area of sustainable brands and sustainability retail? • Question 2. What is the intellectual framework of the research at the moment? • Question 3. a. What are the topics related to our research area? b. What are the lines of research that present the greatest potential?

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We will build our bibliometric analysis from the research questions we have asked ourselves. One of the main advantages of bibliometric analysis is the possibility of analyzing unstructured data and trying to graphically unite them through maps, observing the evolution that has occurred (Baier-Fuentes et al. 2021). The purpose of the research carried out will be to obtain relevant information for researchers and users who want to deepen their knowledge of sustainable brands and sustainability retail to make contributions in those lines that interest them the most and even the development of new lines of research in the field. Area (Donthu et al. 2021). To carry out our analysis we will apply the SPAR-4-SLR protocol (Paul et al. 2021; Vizuete-Luciano et al. 2022); after studying and analyzing the bibliometric data obtained on sustainable brands and sustainability retail. To carry out our study, we turned to the Web of Science (WoS), which belongs to Clarivate Analytics. Our decision is based on the fact that it includes different databases of special international relevance, which will allow us to obtain higher quality information. WoS comprises databases and bibliographical references from 1900 to the present. In the bibliometric analysis, we used as reference indicators the number of publications, the number of citations, the total number of articles and the h-index; a single measure that contemplates the number of publications and the number of citations (Hirsch, 2005). Thanks to the h index we can determine the number of studies out of a totality (N) that have received at least h citations. To obtain a greater level of detail in our work, we carry out scientific mapping, with which we seek to represent the existing intellectual connections between the authors who interact in a certain topic of knowledge (Small 1997; Cobo et al. 2011; Gaviria et al. 2019; Vizuete-Luciano et al. 2023). In our study we have used the VoSviewer software (Van Eck and Waltman 2010), which allows us to visualize the results obtained by using indicators such as: • The bibliographic link; occurs when two articles cite the same third article (Kessler, 1963). • Co-citation indicates the most cited documents and is observed when two articles receive a citation from the same third document (Small, 1973). • Co-keywords, they indicate the most important keywords in the documents and we use them to analyze the structure of the different concepts that correspond to our research (Callon et al., 1983). Finally, we highlight the results obtained in the performance analysis and the scientific mapping that we have carried out give a current, but retrospective vision, so we can observe relevant differences over time, since it is possible the future appearance of new and better indicators that allow analysis of the most recent publications.

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3 Results 3.1 Bibliometric Performance Analysis 3.1.1 Publications and Citation Structure The first publications on sustainable brands and sustainability retail were made in 1995. Although it was not until 2008 when the number of publications increased exponentially until today, where in 2022 it reached 692 publications. Between the period 95–08 the number of publications is stagnant around 2–10 publications per year. In 2015, the barrier of 100 annual publications was overcome to reach 150, and two years later, in 2017, 200 were exceeded. Having exponential growth from 2012 to today. In Fig. 2 we can see the variation in the number of publications over the years.

Fig. 2. Number of papers published per year

In Fig. 2 we can see how the turning point of publications regarding sustainable brands and sustainable retail began in 2008, in view of the change in perception that companies and consumers have about the environment around them and that the time has come to be aware of the fragility of planet earth and the unfeasibility of continuing to do things as they were done to date, which led brands and companies to devise other strategies focused on the future growth of the planet and thus capture the attention of the customer in a more effective way. Subsequently, from 2015 onwards, the publication of academic articles in reference to the use of sustainability as a business strategy grows rapidly, which makes this topic a line of research on the rise year after year, reaching in 2022, the last year of the series, the highest number of publications with 692 articles. 3.1.2 Influential Papers As we have already mentioned, the number of articles referring to the concepts studied has not stopped growing. These publications have been cited by different authors in

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prestigious journals such as the Strategic Management Journal, Journal of Retailing, Journal of Marketing, and the Journal of Consumer Research, among others. In order to advance in the line of research, following the guidelines set by the most influential authors, we have reviewed the publications by the most relevant authors. In this sense, in Table 1, we present the 15 most cited articles in reference to sustainable brands and sustainability retail and that have been compiled by the Web of Science Core Collection. The indicators that we have used are the title of the publication, the authors, the year of publication, the total number of citations (TC) and the mean number of citations per year (C/Y). First up is the article “How much does industry matter, really?” whose authors are McGahan, A.M., and Porter, M.E., published in 1997, with a total of 766 citations; This article analyses the factors that influence business results with the adoption of certain business strategies; which has made it a work of great relevance for the scientific community. Subsequently, in 2013, the article by Gleim et al. was published, entitled “Against the Green: A Multi-method Examination of the Barriers to Green Consumption”, with 384 citations, whose objective was to analyze the barriers that affect consumers of organic products when found in the traditional point of sale. We found other contributions of great scientific interest such as “Carbon emission reduction decisions in the retail-/dual-channel supply chain with consumers’ preference” by Ji et al., or “Is Eco-Friendly Unmanly? The Green-Feminine Stereotype and Its Effect on Sustainable Consumption” by Brough et al., which have a high average number of citations between years. 3.1.3 Leading Authors Table 2 shows the 15 authors with the highest number of publications on sustainable brands and sustainability retail. In this table we show the name of the authors, the organization they are part of, the total number of publications (TP) for these topics, the total number of citations (TC), the calculated H index, the citation ratio per paper (TC/TP) and a paper counter according to the total number of citations it has depending on whether there are more than 100 citations, 50 or more than 10. We observe that the author with the greatest number of publications is Kim, J., from Korea University, who reaches 12. On the other hand, we highlight other authors whom with a smaller number of publications have a greater number of citations; Choi, T.M., from Hong Kong Polytechnic University, presents the highest number of citations with 257 and the highest citations per paper ratio, which is 32.13. In order of citations, we would highlight Ko, E., from Yonsei University and Kim, K.H., from Changwon National University with 245 and 218 citations, respectively. This last author being the one with the highest H-index. Finally, although the main authors have different origins; the vast majority belong to Asia and Europe. 3.1.4 The Most Productive and Influential Institutions Different universities and organizations study the potential of sustainability for brands and businesses, since they consider this line of research as a line with high potential. For this reason, in Table 3 institutions from all over the world appear. In this Table we

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E. Vizuete-Luciano et al. Table 1. Top 15 most cited papers on Sustainable Brands and Retail topics

Rank

Title

Author/s

Year

TC

C/Y

1

How much does industry matter, really?

McGahan, A.M.; Porter, M. E

1997

766

28,37

2

Against the Green: A Multi-method Examination of the Barriers to Green Consumption

Gleim, M. R.; Smith, J. S.; Andrews, D.; Cronin, J.; Joseph, Jr

2013

384

34,91

3

Is Market Orientation a Source of Sustainable Competitive Advantage or Simply the Cost of Competing?

Kumar, V.; Jones, E.; Venkatesan, R.; Leone, R. P

2011

347

26,69

4

The impact of supply chain structure on the use of supplier socially responsible practices

Awaysheh, A.; Klassen, 2010 R. D

297

21,21

5

Towards a sustainable de Brito, M. P.; 2008 fashion retail supply chain Carbone, V.; Blanquart, in Europe: Organization and C. M performance

294

18,38

6

The Relationship Between Sustainable Supply Chain Management, Stakeholder Pressure and Corporate Sustainability Performance

Wolf, J

2014

261

26,10

7

Environmental sustainability in fashion supply chains: An exploratory case-based research

Caniato, F.; Caridi, M.; Crippa, L.; Moretto, A

2012

256

21,33

8

Carbon emission reduction decisions in the retail-/dual-channel supply chain with consumers’ preference

Ji, J.; Zhang, Z.; Yang, L

2017

250

35,71

9

Is Eco-Friendly Unmanly? Brough, A. R.; Wilkie, The Green-Feminine J. E. B.; Ma, J.; Isaac, Stereotype and Its Effect on M. S.; Gal, D Sustainable Consumption

2016

247

30,88

10

The Persistence of Dekimpe, M. G.; Marketing Effects On Sales Hanssens, D. M

1995

245

8,45 (continued)

Sustainable Brands and Retail: A Bibliometric Analysis

107

Table 1. (continued) Rank

Title

11

Author/s

Year

TC

C/Y

Building stronger brands McWilliam, G through online communities

2000

234

9,75

12

Green Claims and Message Frames: How Green New Products Change Brand Attitude

Olsen, M. C.; Slotegraaf, R. J.; Chandukala, S. R

2014

217

21,70

13

Willingness to pay for organic products: Differences between virtue and vice foods

van Doorn, J.; Verhoef, P. C

2011

217

16,69

14

Theory of planned behavior Liobikiene, G.; approach to understand the Mandravickaite, J.; green purchasing behavior Bernatoniene, J in the EU: A cross-cultural study

2016

211

26,38

15

Late mover advantage: How Shankar, V.; Carpenter, 1998 innovative late entrants G. S.; Krishnamurthi, L outsell pioneers

211

8,12

present the ranking of the 15 institutions that have carried out the most research over the years. The indicators that we present are: the total number of papers (TP), the total citations (TC), the h index of the Institution (H) and the ratio of total citations to total articles (TC/TP). As in the previous table, we also include a counter to consider the number of citations greater than 100, 50 or 10. We end this Table with the evaluations of the institutions from the Academic Ranking of World Universities (ARWU) and the Quacquarelli Symonds World University Ranking (QS), which will allow us to have an approximation of the research effort carried out by these organizations. In the first position, we find the Wageningen University of Netherland, which has a total of 41 publications, accumulating more than 900 citations and with 4 having more than 100 citations. In second place we have the Hong Kong Polytechnic University with 33 publications and 795 citations, which has two publications with more than 100 citations and 19 with more than 10. Regarding the citation ratio per article, Aarhus University of Denmark stands out in first place. With a value of 44.12 with 17 publications that present a high number of citations 750; in second place the Delft University of Technology of the Netherlands with a ratio of 42.50 and in third place the University of London with a value of 41.87. In the Table we observe that 66.67% of the main institutions originate from Europe, 20% are from Asia and where the US and Australia contribute an organization. On the other hand, it is worth noting when looking at the column of the Academic Ranking of World Universities that the best highlighted is the University of London in position 17,

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E. Vizuete-Luciano et al. Table 2. Top 15 Leading authors on Sustainable Brands and Retail topics

Rank

Authors

Organization

TP

TC

H.index

TC/TP

≥100

≥50

≥ 10

1

Kim, J.

Korea University

12

120

5

10,00

0

0

4

2

Gil-Saura, I.

University of Valencia

10

71

4

7,10

0

0

2

3

Kim, S.

Hong Kong Polytechnic University

10

79

4

7,90

0

0

3

4

Ruiz-Molina, M.E. University of Valencia

10

39

4

3,90

0

0

1

5

Kim, K. H.

Changwon National University

9

218

9

24,22

0

0

8

6

Ko, E.

Yonsei University

9

245

7

27,22

0

2

5

7

Kumar, A.

London Metropolitan University

9

116

4

12,89

0

1

1

8

Amatulli, C.

Universita degli Studi di Bari Aldo Moro

8

168

5

21,00

0

2

2

9

Choi, T. M.

Hong Kong Polytechnic University

8

257

6

32,13

1

0

5

10

Dabija, D. C.

Babes Bolyai University from Cluj

8

158

6

19,75

0

0

5

11

De Angelis, M.

Luiss Guido Carli University

8

168

5

21,00

0

2

2

12

Strahle, J.

Reutlingen Univ

8

41

3

5,13

0

0

1

13

Wang, Y.

Chongqing Jiaotong University

8

85

4

10,63

0

0

3

14

Han, H.

Sejong University

7

89

5

12,71

0

0

4

15

Sharma, M.

Birla Institute of Technology Mesra

7

80

5

11,43

0

0

4

followed by the University of North Carolina in 29. Similarly, we highlight that 5 are within the top 100 of the most relevant in the world. Regarding the ranking provided by Quacquarelli Symonds World University Ranking, we found 1 in the top 10 and 9 in the top 100. By way of differences between both rankings, we highlight that the University of London occupies 6th place in the QS while in the ARWU it is the 17th although it is the best classified in both cases; The same is not true of the University of North Carolina, which in the QS is out of the Top 100 while in the ARWU it occupies position 29.

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Table 3. The most productive and influential institutions Rank

Organization

Country

TP

TC

H

TC/TP

≥ 100

≥ 50

≥ 10

ARWU

QS

1

Wageningen University & Research

Netherlands

41

939

13

22,90

4

0

14

101–150

124

2

Hong Kong Polytechnic University

Hong Kong

33

795

16

24,09

2

0

19

151–200

65

3

University of London

United Kingdom

30

558

8

41,87

1

2

5

17

6

4

Yonsei University

South Korea

24

552

14

23,00

0

3

11

201–300

73

5

Lund University

Sweden

22

652

14

29,64

1

2

13

151–200

95

6

University of Queensland

Australia

22

218

8

9,91

0

1

5

51

50

7

University of Bucharest

Romania

21

98

6

4,67

0

0

4

N/A

1001–1200

8

University of Manchester

United Kingdom

20

465

13

23,25

0

2

13

35

28

9

University of Leeds

United Kingdom

19

284

8

14,95

0

1

6

101–150

86

10

University Of United North Carolina States

19

276

8

38,63

1

0

5

29

102

11

Delft University of Technology

Netherlands

18

765

12

42,50

3

0

9

151–200

61

12

Kyung Hee University

South Korea

18

152

8

8,44

0

0

8

401–500

270

13

The University United of Sheffield Kingdom

18

378

11

21,00

2

0

9

101–150

96

14

University of Valencia

Spain

18

171

5

9,50

0

1

2

301–400

571–580

15

Aarhus University

Denmark

17

750

10

44,12

3

3

5

71

161

3.1.5 The Most Productive and Influential Countries Next, in Table 4, we study the most productive and influential countries, since they are the ones that have made the greatest number of publications on the topic of sustainable brands or sustainability retail. The country of reference is where the authors of the papers worked from. We present the data in Table 4 and use different indicators, as we have done in Table 3, although we have incorporated others, such as the total population of the country, according to World Bank data, in addition to the ratio between the total number of articles and population (TP/POP) and the ratio between total citations and population (TC/POP). In Table 4 we show the production of 15 most relevant countries, however, there are many who publish on this topic. In Table 4 we observe that the country with the highest production

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of publications is the United States, with a total of 541 articles and an H index of 54, followed by China with 411 articles and an H index of 34; with England in third place with 356 articles and an H index of 44. If we analyze, the countries with a higher ratio of total publications per person. In this group it stands out that Germany has the highest ratio, which with 152 publications stands out for its production of articles per inhabitant; the same as Australia, which has 202 articles. Regarding the relationship of citations by population, we can highlight the Netherlands in third place with 128 articles and an h index of 30. Table 4. The most productive and influential countries Rank Country

TP

TC

H

TC/TP Population 331.893,74

TP/POP 1.630,04

TC/POP

1

United States 541 12.410 54 22,94

2

China

411 5.317

34 12,94

3

England

356 6.612

44 18,57

55.997,20

6.357,46 118.077,33

4

Italy

214 3.640

31 17,01

59.066,22

3.623,05

5

Australia

202 3.897

34 19,29

25.739,26

7.847,93 151.402,95

6

Spain

190 2.561

26 13,48

47.326,69

4.014,65

7

India

168 1.977

24 11,77

8

South Korea

157 1.822

23 11,61

1.412.360,00 291,00

1.393.409,03 120,57 213.993,44

1.888,62

37.391,49 3.764,62 61.625,75 54.113,23 1.418,82 21.917,67

9

Germany

152 3.119

28 20,52

83.129,29

8.669,17 177.889,06

10

Netherlands

128 3.434

30 26,83

17.533,40

5.349,43 143.515,07

11

Taiwan

102 1.046

19 10,25

23.927,80

3.057,61

31.355,46

12

Sweden

101 2.344

27 23,21

33.359,42

3.027,63

70.265,01

13

Canada

96

25 25,17

38.246,11

2.510,06

63.169,82

14

France

90

1.752

22 19,47

67.499,34

1.333,35

25.955,81

15

South Africa

64

705

15 11,02

59.392,26

1.077,58

11.870,23

2.416

3.2 Science Mapping To carry out our bibliometric study we have opted for the use of scientific mapping. This methodology will allow us to graphically observe the different connections between the different publications that researchers have made. This type of analysis contributes to enrich the quantitative analysis that we have carried out in the previous section with a more visual methodology. In this section we show the different scientific mappings that we have carried out and that will allow us to find the connections between the different authors mentioned above, the most influential and active journals as well as the most used keywords. To do this, we have used the VOSwiewer program, which allows us to perform the analysis by citations, co-citations, co-occurrences, and bibliographic coupling.

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Performing the analysis of citations and co-citations we can observe the relationships between the different papers, publications and authors that have been cited by more than one paper together. The co-citation mapping will show us the data of the cited articles and their connections; Visually, we will be able to observe the relationships between the different papers depending on whether they have been cited together on different occasions. The co-occurrence map will allow us to observe the terms that have been related more frequently. And in the case of keywords, it will indicate the existing relationship if we find the same keywords in different articles. To carry out our study, we will first proceed to develop the Journals co-citation map, where we observe those publications with the greatest influence in the field of sustainable brands and sustainability retail and which present a greater number of citations. With the resulting graph, we can see the publications that will have the most relevant papers. We have carried out the mapping establishing that the minimum number of citations was 721 times, which shows us the 25 most co-cited journals. In Fig. 3, we see the main journals. We observe three clusters that we can differentiate by colors. The green block corresponds to sustainability publications, the red block to marketing and the blue block to retail. In the first block we highlight the magazines Journal Cleaner Production, Sustainability-Basel and the Journal Business Ethics among others. In the red cluster we find relevant journals such as the Journal of Marketing, the Journal of Consumer Research and the Journal of Business Research. Finally, in the blue cluster, we highlight the Journal retailing consumer services and the Journal of Retailing.

Fig. 3. Co-citation of Journals

In Fig. 4 we can show the keywords that have been used mainly in papers related to sustainable brands and sustainability retail. This mapping allows us to observe the

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different themes and future lines of research. In the keyword co-occurrence map we find the 50 most relevant keywords, having at least 70 occurrences. At the epicenter of our research we find sustainability, being the most relevant word in terms of importance. Next, we find impact, performance, model and consumption; variables that are related to the theme that we have analyzed. The mapping that shows us the co-occurrence of keywords shows us three clusters of topics, in green it is more related to sustainability and sustainable business management, where we can also observe future lines of research: the supply chain, design, policy. In the red cluster, we find the keywords most related to consumers, such as: consumption, products, quality, behavior and brand. In this area, future lines of research are sustainable consumption, willingness-to-pay and consumer attitudes. In the blue cluster we find marketing or management concepts such as the model, impact, satisfaction, and social media. In this cluster we observe that satisfaction, the study of purchase intention and the analysis of loyalty are having a great impact among researchers.

Fig. 4. Co-occurrence of keywords

To finish our analysis in Fig. 5, we show the coupling between countries in which we show the 25 most relevant countries. We can see three well-differentiated clusters, although the members of the green cluster call our attention. In this, the most relevant country is Peoples R China, which appears with the Asian countries and which are joined by the Europeans Spain, France and Romania. In the red cluster we find that the most relevant country is England, while in the blue cluster we find the USA and Australia.

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Fig. 5. Bibliographic coupling of publishing countries

4 Conclusions In recent years, we observed the consequences of human activity and climate change on the planet. As a result, we have become aware of the need to implement limits to pollution, deforestation…, if we want to live in a healthy and sustainable way for many years on Earth. Sustainability is essential to apply in all our decisions, as we pursue a common goal; the survival of our planet. Business sustainability is linked to the possibility of continuing to carry out economic activities in the future, considering environmental, economic and social factors or criteria. It has become a priority for society and therefore for companies and their managers. As a result, consumers have developed an ecological awareness and are increasingly demanding products and services from companies that are committed to the sustainable development of society. This is not a passing fad; it is a change in trend that is here to stay, as evidenced by the growing demand for ecological and sustainable products and the development of similar concepts such as the circular economy with the aim of avoiding the useless waste of resources. A sustainable company is one that is responsible for the planet that will be left to future generations; for this reason, companies seek to promote sustainable development, on the one hand protecting nature and on the other hand trying to curb the excessive consumption of resources that have taken thousands and thousands of years to be formed. Noting the importance of sustainability, we have proceeded to investigate the keywords sustainable brands and sustainability retail in published academic articles in order to establish the current situation and to observe the future lines of research in a topic that is so relevant worldwide. For this reason, we have combined quantitative methodologies

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with scientific mapping. In this way we can give relevance to the most relevant authors, institutions, countries and Journals. For the development of our study, we have started from a significant sample obtained from the Web of Science Core Collection database. We have obtained a total of 3,228 papers of great relevance through the topic’s sustainable brands and sustainability retail. These articles have been published between 1995 and 2022, where we have observed the growing interest they have aroused among members of the scientific community, exponentially increasing the number of publications and citations over the years. We have observed that these papers have been published by a large number of renowned authors, institutions and countries, which shows the high degree of impact that the subject has, from an academic and social point of view. We can highlight the production of articles from different countries, with the United States, China and England being the countries with the highest number of publications and citations. We have also observed the relevance of institutions of high international prestige, which are in the most prominent positions of the most prestigious university rankings such as ARWU and QS. To conclude our study, thanks to the scientific mapping developed through the VOSViewer program, analyzing the existing relationships between articles, we have been able to observe relationships of special relevance. We highlight the activity observed in prestigious journals such as: Journal of Marketing, Journal Cleaner Production, Journal of Consumer Research, Journal of Retailing, among others. Finally, we have analyzed the main Keywords used in the different academic articles published and we can determine that those factors most studied are: Sustainability, Impact, Management, Performance and Consumption… And those topics that represent future lines of research for the Scientific Community. 4.1 Futures Lines of Research One of the main contributions that we have been able to make thanks to the bibliometric analysis carried out is to be able to determine the future lines of research that can be observed in the field of sustainable brands and sustainability retail. Figure 4 shows the potential of supply chain management in a sustainability environment, since we can see the problems that arise in many cities with the transportation of online purchases. Secondly, we highlight the potential of studying consumer satisfaction in such a changing and uncertain environment as the one in which we find ourselves. We also note the development that is being made around sustainable consumption, as undoubtedly more and more consumers are becoming aware of the current problems of the products they buy and this is leading them to act and take part in the solution. Finally, we could highlight the potential of product design, as we observe that it is a fundamental characteristic that must be developed in the future if we want to develop strong and sustainable growth over time.

References Baier-Fuentes, H., Merigó, J.M., Miranda, L., Martínez-López, F.J.: Strategic planning research through fifty years of long-range planning: a bibliometric overview. Strategic Manage. 26(1), 3–25 (2021). https://doi.org/10.5937/StraMan2101003B

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Callon, M., Courtial, J.P., Turner, W.A., Bauin, S.: From translations to problematic networks: An introduction to co-word analysis. Soc. Sci. Inf. 22(2), 191–235 (1983). https://doi.org/10. 1177/053901883022002003 Cobo, M.J., López, A., Herrera, E., Herrera, F.: Science mapping software tools: Review, analysis, and cooperative study among tools. J. Am. Soc. Inform. Sci. Technol. 62, 1382–1402 (2011). https://doi.org/10.1002/asi.21525 Colgrazier, W.: Sustainable development agenda: 2030. Science, 349(6252), 1048–1050 (2015). https://doi.org/10.1126/science.aad2333 Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., Lim, M.L.: How to conduct a bibliometric analysis: an overview and guidelines. J. Bus. Res. 133, 285–296 (2021). https://doi.org/10. 1016/j.jbusres.2021.04.070 Gaviria, M., Merigó, J.M., Baier, H.: Knowledge management: a global examination based on bibliometric analysis. Technol. Forecast. Soc. Change 140, 194–220 (2019). https://doi.org/ https://doi.org/10.1016/j.techfore.2018.07.006 Guillén-Pujadas, M., E. Vizuete-Luciano, F. Vila-Márquez, and M.L. Solé-Moro (2022). “Investigación bibliométrica sobre la belleza y los consumidores.” Cuadernos del CIMBAGE, 2(24), 36-50 Hirsch, J.E.: An index to quantify an individual’s scientific output. Proc. Natl. Acad. Sci. U.S.A. 102, 16569–16572 (2005) Kessler, M.: Bibliographic coupling between scientific papers. JASIST J. Assoc. Inform. Sci. Technol. 14(1), 10–25 (1963). https://doi.org/10.1002/asi.5090140103 Li, Y.B., Zhu, X.F.: The 2030 Agenda for sustainable development and china’s belt and road initiative in Latin America and the Caribbean. Sustainability 11(8) (2019). https://doi.org/ https://doi.org/10.3390/su11082297 Merigó, J.M., Pedrycz, W., Weber, R., de la Sotta, C.: Fifty years of information sciences: a bibliometric overview. Inf. Sci. 432, 245–268 (2018). https://doi.org/10.1016/j.ins.2017.11.054 Noyons, E.C., Moed, H.F., Luwel, M.: Combining mapping and citation analysis for evaluative bibliometric purposes: a bibliometric study. J. Am. Soc. Inf. Sci. 50(2), 115–131 (1999). https:// doi.org/10.1002/(SICI)1097-4571(1999)50:2%3C115::AID-ASI3%3E3.0.CO;2-J Papuzinski, A.: The Enlightenment assumptions of the brundtland report. Problemy Ekorozwoju 13(1), 7–14 (2018) Paul, J., W.M. Lim, A.O’Cass, A.W. Hao, and S. Bresciani, S.: Scientific procedures and rationales for systematic literature reviews (SPAR-4-SLR). Int. J. Consumer Stud. 45(4), O1–O16. https:// doi.org/10.1111/ijcs.12695 (2021) Small, H.: Co-citation in the scientific literature: a new measure of the relationship between two documents. J. Am. Soc. Inf. Sci. 24, 265–269 (1973). https://doi.org/10.1002/asi.4630240406 Small, H.: Update on science mapping: creating large document spaces. Scientometrics 38(2), 275–293 (1997). https://doi.org/10.1007/BF02457414 Van Eck, N., Waltman, L.: Encuesta de software: VOSviewer, un programa informático para el mapeo bibliométrico. Cienciometría 84(2), 523–538 (2010). https://doi.org/10.1007/s11192009-0146-3 Vizuete-Luciano, E., Guezel, O., Merigó, J.M.: Bibliometric research of the Pay-What-You-Want Topic. J. Revenue Pricing Manag. (2022). https://doi.org/10.1057/s41272-022-00414-6 Vizuete-Luciano, E., Guillén-Pujadas, M., Alaminos, D., Merigó-Lindahl, J.M.: Taxi and urban mobility studies: a bibliometric analysis. Transport Policy, 133, 144–155 (2023). https://doi. org/10.1016/j.tranpol.2023.01.013

Online Context and COVID-19

Shift in National Brand and Private Label Shares with Households Commencing Online Grocery Shopping Philipp Brüggemann(B) and Carsten D. Schultz FernUniversität in Hagen, Hagen, Germany {philipp.brueggemann,philipp.brueggemann}@fernuni-hagen.de

Abstract. Online Grocery Shopping (OGS) is a relatively new sales channel for retailers and manufacturers that is changing retailing considerably. Despite this, little is known about whether or how consumers’ behavior changes when they start using OGS. We fill this research gap by measuring the share of national brands (NBs) of 173 households before and after starting OGS. Based on the literature, we derive the hypothesis that the share of NBs increases when households start using OGS, while the share of private labels (PL) consequently decreases. We use the difference-in-differences (DiD) method for our empirical analysis and test our hypothesis using extensive GfK household panel data. Our results show that the share of NBs increases significantly when a household commences OGS. Consequently, the market share of PLs decreases with start using OGS. These results help retailers and manufacturers to better understand how OGS can change consumer behavior. For manufacturers, this is a positive signal as it seems to strengthen the share of their NBs. For retailers, this is a sign of the relevance of NBs of online grocery shoppers. Keywords: Consumer behavior · e-commerce · National brands · Online grocery shopping · Private labels · Retail

1 Introduction Online grocery shopping (OGS) was already studied two decades ago as a possible new form of grocery shopping (Morganosky and Cude 2000; Ramus and Nielsen 2005). Since then, some other research projects have addressed this topic. Driven by ongoing digitalization and due to the COVID-19 pandemic, the availability and use of OGS is noticeably increasing in recent years, as is the corresponding research (Grashuis et al. 2020; Pantano et al. 2020; Prabowo and Hindarwati 2020; Al-Hawari et al. 2021; Guthrie et al. 2021; Brüggemann and Olbrich 2022). Consistent with this, current statistics forecast an average annual increase in OGS of about 10% through 2027 (Statista 2022a). Despite this fundamental change for retailers, brand managers, and consumers, it is still unclear whether or how consumer behavior will change in terms of competition between national brands (NBs) and private labels (PLs) when they start using OGS. This is enormously relevant for retailers and manufacturers, as they need to detect changes in © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. C. Gázquez-Abad et al. (Eds.): NB&PL 2023, SPBE, pp. 119–126, 2023. https://doi.org/10.1007/978-3-031-32894-7_13

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consumer behavior caused by this new way of grocery shopping as quickly as possible and react accordingly. Only if involved parties know how consumers’ behavior changes when they start OGS they are able to react adequately, e.g., by changing the assortment or using online and offline channels. To analyze changes in consumer behavior, we use value-based shares of NBs (and implicitly PLs) exemplarily. In retail research, NBs and PLs have been investigated in numerous publications as key target variables to analyze competition between retailers and manufacturers (e.g., Hoch 1996; Steenkamp et al. 2010; Brüggemann et al. 2020; Gielens et al. 2021; Dawes 2022). However, there is no research analyzing consumers’ shares of NBs before and after they stared using OGS. We address this research gap by empirically comparing the purchases of 173 households before and after their OGS start using a difference-in-differences (DiD) regression analysis. Consequently, we answer the following research question: (How) does consumers’ share of NBs change when they start using OGS?

2 Theoretical Background and Hypothesis Development For this research, particularly OGS and competition between manufacturers’ NBs and retailers’ PLs are relevant. While an extensive research body exists on competition between NBs and PLs (e.g., Hoch 1996; Quelch and Harding 1996; Ailawadi and Harlam 2004; Pauwels and Srinivasan 2004; Ailawadi et al. 2008; Sethuraman 2009; Steenkamp et al. 2010; Cuneo et al. 2019; Gielens et al. 2021; Dawes 2022), OGS is less focused until the beginning of the COVID-19 pandemic. Since then, the use of OGS has increased significantly (Statista 2022a; Statista 2022b; Brüggemann and Olbrich 2022). Consistently, research on OGS has also increased, especially since the COVID-19 pandemic (Bauerová and Zapletalová 2020; Li et al 2020; Al-Hawari et al 2021; Baarsma and Groenewegen 2021; Jensen et al. 2021; Gruntkowski and Martinez 2022; Ermecke et al. 2023). However, it is not yet known how OGS will evolve in the long term and whether there will be a changes in consumer behavior as a result of additionally using the online channel. To observe a change in consumer behavior, we use the well-researched field of competition between NBs and PLs, which is highly relevant in retailing. In this paper, we use the value-based share of NBs per household as an indicator for a change in behavior after the respective household has started with the OGS. Brüggemann and Pauwels (2022) found that the market share of NBs in online channels is higher than in brick-and-mortar stores. Furthermore, the authors find that also-online grocery shoppers have a lower price consciousness and a higher brand preference than offline-only grocery shoppers. Though, the authors do not analyze whether a household’s behavior changes when it begins OGS. Still, the authors’ results suggest that households that begin OGS are predisposed to purchase NBs and ultimately purchase proportionately more NBs after tapping into the new online channel. Therefore, we expect that the share of NBs overall, i.e. offline and online, will increase as a result of starting with OGS. With this line of reasoning in mind, we expect the following: H1: After households start using OGS, they buy offline and online proportionately more NBs (and less PL).

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3 Empirical Analysis 3.1 Data, Operationalization, and Methodological Details For the empirical analysis, we use GfK household panel data from 2016–2020 regarding the four product groups hair shampoo, laundry detergent, chocolate bars and coffee.1 The data includes per household the following purchase information: product prices, product quantity, whether the product is a NB or a PL, whether the product was purchased offline or online. First, we separate the households between also-online and offline-only consumers. For each year, we identify households that shopped exclusively in brick-and-mortar stores and assign these households to the offline-only group. A household is assigned to the also-online group in a given year if it made at least one online purchase in that year. In a further step, we identified the first online purchase as the start of OGS for each also-online purchasing household in order to investigate a change in behavior. It should be noted that we only considered households that started to purchase groceries online after the first six months and before the last six months of the observation period. In addition, we only considered households that had made at least one online purchase per year since their OGS start, as well as at least ten online purchases in total. Through this operationalization, we obtain 173 households that started and continued using OGS during the observation period. For each household, we form a control group of offline-only shoppers in order to identify effects that influence both groups (offline-only and also-online) equally during the observation period. Figure 1 illustrates the operationalization of the treatment group (also-online shoppers) and the control group (offline-only shoppers). For the empirical analysis, we use the DiD method. For this purpose, we calculate the value-based share of NBs before and after starting with OGS (treatment group) for each household that started with OGS in the observation period. To calculate the value-based shares of NBs per household, we use both online and offline purchases to measure changes in behavior across channels rather than an increase in shares of NBs online. According to Brüggemann and Pauwels (2022), a higher share of NBs can be expected online. However, we are particularly interested in whether the total share (online and offline) of NBs changes as a result of the start with OGS. In doing so, we focus our empirical analysis on changes on consumer behavior in terms of shares of NBs both online and offline. To compare the shares of NBs for each also-online purchasing household with those of offline-only purchasing households, we calculate a comparative value of offline-only purchasing households. Here, too, we differentiate between the periods before and after the start with OGS of the also-online purchasing household. For each also-online purchasing household, we take the date of the start with OGS and calculate the average share of NBs of offline-only purchasing households before and after this date. Following this procedure, we calculate 173 times the average share of NBs (from offline-only shoppers) before and after each cutoff date of the corresponding also-online households (see Fig. 1). In addition to the DiD effect, we also add a binary variable on COVID-19 pandemic to the linear regression. This variable takes the value 1 if a household started OGS during the pandemic (as of March 2020). We included this 1 The source of the data is GfK Consumer Panels & Services.

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binary variable to control for a possible influence of COVID-19 pandemic on our DiD results. 2016 Treatmen t group (n=173)

2017

2021

2020

Also-online households

Average of Offline-only households

Control group Ö

Ö First six month excluded

OGS start

Periods before/after OGS start

Last six month excluded

Fig. 1. Operationalization of the empirical analysis

Formula (I) describes the underlying linear DiD regression. share of NBsh,t,e = γo + treatmenth ∗ γ1 + eventh ∗ γ2 + treatmenth ∗ eventg ∗ γ3 + COVID − 19h ∗ γ4 + ε

(1)

where shareofNBsh,t,e

Share of NBs in all purchases of the underlying product groups observed, = differentiated according to treatment and control group and according to the two periods before and after the OGS start (note: for the control group, average values were calculated as baseline values before and after the OGS starting dates),

h =

Household (n = 173),

t

Binary variable for differentiation by treatment and control group = (control = 1; treatment = 0),

e

Binary variable for differentiation according to the period before and = after the start with OGS (before = 0; after = 1),

treatment h

Binary variable to differentiate between treatment and control group, =

event h

binary variable to differentiate the periods before and after the start of the = OGS,

COVID-19h

binary variable to control for an effect of the COVID-19 pandemic on = shares of NBs,

γ0 γk ε

constant, = =

regression coefficients (k = 1, …, 4), error term

=

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3.2 Empirical Results Table 1 provides stepwise empirical results of our DiD regression. In a first step, Model I contains the results of the DiD regression. In a second step (Model II), we additionally provide our results regarding the binary variable on the COVID-19 pandemic. The Event row reports the results from differencing between the periods before and after starting with OGS. The row Treatment contains the differentiation between also-online and offline-only buying households. The interaction effect of Event and Treatment is shown in the DiD row. Our empirical results show a significant positive effect on the share of NBs since consumers start to shop groceries online. Thus, households that start using OGS bought – online and offline – proportionately more NBs (and less PLs). At this point, we would like to emphasize that this linear regression can only explain 4.3% of the total variance. Although this seems small, it still indicates a significant increase (decrease) in the share of NBs (PLs) purchased by households as a result of starting with OGS. That there are other influencing factors (e.g., prices, assortment, household attitudes) is undisputed. Nevertheless, this analysis shows that households that start with OGS show an increasing (decreasing) share of NBs (PLs) online and offline. Thus, our results support hypothesis H1. Model II further shows that the increase in the share of NBs due to the start of OGS is not due to the COVID-19 pandemic. Table 1. Empirical results. Model I

Model II

Event

.053 (.018)

.053 (.018)

Treatment

.081 (.027)

.081 (.027)

DiD

.132 (.050)*

.132 (.050)*

Constant

(.744)***

(.743)***

COVID-19

.014 (.008)

Observations

691

691

R2

.047

.048

Adjusted R2

.043

.042

Residual std. Error

.161 (df = 688)

.161 (df = 688)

F statistic

11.392*** (df = 3; 688)

8.568*** (df = 3; 688)

Dependent variable: value-based share of NBs per household

4 Conclusion With this research we demonstrate that the value-based share of NBs of grocery shoppers increases when consumers start shopping groceries online. Using the example of shares of NBs, we show that consumers’ behavior changes (offline and offline) after they start

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using OGS. Households that start using OGS may be more willing to buy NBs. These can be addressed, for example, with individual offers or appropriate assortment selection, especially in OGS applications. For retailers that offer both brick-and-mortar stores and OGS, this result is particularly relevant, as we demonstrate an increase (decrease) in the share of NBs (PLs) both online and offline with our empirical study. These retailers can increasingly offer NBs to households using OGS online and offline, e.g. via special offers in the OGS application or personalized coupons for brick-and-mortar stores. Brüggemann and Olbrich (2022) conclude, based on high fluctuation among OGS customers in Germany, that OGS providers have so far hardly managed to retain their customers in the long term. With the knowledge from this study, OGS providers can address their new customers in a more targeted manner and thus retain them better. Finally, the results of this study are a positive signal for manufacturers, as the start of OGS by households seems to strengthen the share of NBs of these consumers. For retailers, this is a sign of the high relevance of NBs in OGS. However, this study also has some limitations that open avenues for further research. First, our empirical analysis is based on four product groups. Even though these product groups reflect different parts of the shopping basket and, thus, varying purchasing behavior, the four product groups do not completely capture the entire shopping behavior. Therefore, we encourage researchers to replicate our analysis considering the entire shopping basket. Second, in this paper we focus on the share of NBs to reveal change in consumer behavior. For this purpose, further research should consider additional variables, such as share of organic or fair trade products, demographics as well as households’ attitudes (e.g., price consciousness, brand preference, sustainability, convenience). Third, in our data from 2016 to 2020, we were able to identify 173 households that started using OGS during the observation period. Recent statistics show that OGS adoption has continued to increase since 2020, and projections show that it is likely to continue to increase in the coming years (Statista 2022a ; Statista (2022b) ). Therefore, the findings here should be further explored with additional data in future research. Fourth, the results show relatively low coefficients of determination. While the effect found due to the start of the OGS on the share of NBs is significant, we can only explain a small proportion of the variance with the model used here. Further research should therefore add more variables to this basic model, such as prices, special offers, brand variety. Fifth, we compare individual households that start OGS with average values of offline-only households. In order to validate our results, in further research the households starting with OGS can additionally be compared with individual offline-only households instead of using an aggregated baseline.

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Bauerová, R., Zapletalová, Š.: Customers’ Shopping Behaviour in OGS – Changes Caused by COVID-19. In: 16th Annual International Bata Conference for Ph. D. Students and Young Researchers, pp. 34–48 (2020) Brüggemann, P., Olbrich, R.: The impact of COVID-19 pandemic restrictions on offline and online grocery shopping: New normal or old habits? Electronic Commerce Research, in press Brüggemann, P., Olbrich, R., Schultz, C.D.: Competition Between National Brands and Private Labels: Determinants of the Market Share of National Brands. In: Martinez-Lopez, F.J., Gázquez-Abad, J.C., Breugelmans, E. (eds.) NB&PL 2020. SPBE, pp. 39–49. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-47764-6_5 Brüggemann, P., Pauwels, K.: Consumers’ Attitudes and Purchases in Online Versus Offline Grocery Shopping. In: Martinez-Lopez, F.J., Gázquez-Abad, J.C., Ieva, M. (eds.) Advances in National Brand and Private Label Marketing – Ninth International Conference, pp. 39–46. Springer International Publishing, Cham (2022) Cuneo, A., Milberg, S.J., del Carmen Alarcon-del-Amo, M., Lopez-Belbeze, P.: Private label and manufacturer brand choice in a new competitive reality: Strategic directions and the future of brands. Eur. Manag. J. 37(1), 117–128 (2019) Dawes, J.: Factors that influence manufacturer and store brand behavioral loyalty. J. Retail. Consum. Serv. 68, 103020 (2022) Ermecke, K., Olbrich, R., Brüggemann, P.: They Don’t Do What They Say – The Attitude-Behavior Gap in Online and Offline Grocery Shopping for Organic Products, in: Martinez-Lopez, F.J. (Ed .), Advances in Digital Marketing and eCommerce – Fourth International Conference, Springer International Publishing: Cham 2023, in press https://doi.org/10.1007/978-3-031-31836-8_9 Gielens, K., et al.: The future of private labels: towards a smart private label strategy. J. Retail. 97(1), 99–115 (2021) Grashuis, J., Skevas, T., Segovia, M.S.: Grocery shopping preferences during the COVID-19 pandemic. Sustainability 12(13), 5369 (2020) Gruntkowski, L.M., Martinez, L.F.: Online grocery shopping in germany: Assessing the impact of COVID-19. J. Theor. Appl. Electron. Commer. Res. 17(3), 984–1002 (2022) Guthrie, C., Fosso-Wamba, S., Arnaud, J.B.: Online consumer resilience during a pandemic: An exploratory study of e-commerce behavior before, during and after a COVID-19 lockdown. J. Retail. Consum. Serv. 61, 102570 (2021) Hoch, S.J.: 1996: How Should National Brands Think about Private Labels? MIT Sloan Manag. Rev. 37(2), 89–102 (1996) Jensen, K.L., Yenerall, J., Chen, X., Yu, T.E.: US Consumers’ Online Shopping Behaviors and Intentions During and After the COVID-19 Pandemic. J. Agric. Appl. Econ. 53(3), 416–434 (2021) Li, J., Hallsworth, A.G., Coca-Stefaniak, J.A.: Changing grocery shopping behaviours among Chinese consumers at the outset of the COVID-19 outbreak. Tijdschr. Econ. Soc. Geogr. 111(3), 574–583 (2020) Morganosky, M.A., Cude, B.J.: Consumer response to online grocery shopping. International Journal of Retail & Distribution Management 28(1), 17–26 (2000) Mortimer, G., Fazal e Hasan, S., Andrews, L., Martin, J.: Online grocery shopping: the impact of shopping frequency on perceived risk. Int. Rev. Retail, Distrib. Consum. Res., 26(2), 202–223 (2016) Pantano, E., Pizzi, G., Scarpi, D., Dennis, C.: Competing during a pandemic? Retailers’ ups and downs during the COVID-19 outbreak. J. Bus. Res. 116, 209–213 (2020) Pauwels, K., Srinivasan, S.: Who benefits from store brand entry? Mark. Sci. 23(3), 364–390 (2004) Prabowo, H., Hindarwati, E.N.: Online grocery shopping adoption: a systematic literature review. In: 2020 International Conference on Information Management and Technology (ICIMTech), pp. 40–45. IEEE (2020)

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Online Booking Versus Personalised Service in the Context of a Sports Retailer: A Qualitative Approach to Golf Courses María Del Mar Martín-García, José Luis Ruiz-Real, Juan Carlos Gázquez-Abad(B) , and Juan Uribe-Toril University of Almería, Almería, Spain [email protected]

Abstract. Golf clubs face the challenge of maximising revenue without losing the trust of their members. Reservation management as a tool to increase revenue is hampered by difficulties in getting members to book online. The aim of this research is to explore the profile of the golf club members to identify the barriers to online booking, their implications for revenue management and the role of relationship marketing. Six in-depth interviews were conducted with golf club managers in Andalusia (Spain). The results show that the barriers to online booking are closely related to the customer profile of the golf club members. A relevant implication of the study is the importance of staff in the relationship with members and relational marketing as an effective marketing tool with this customer. Keywords: Golf club · Online booking · Reservation management · Relationship marketing · Revenue maximisation

1 Introduction The techniques employed in European golf clubs for revenue management and reservation management is an area that has been under-researched. In some types of nontraditional businesses, such as golf, bookings need to be managed at a greater level of detail (Noone et al., 2019). Time-based capacity management raises the complexity of revenue and reservation management practices. The availability of the golf course, its capacity management, depends on the seasonal climate, available sunshine hours, and some variables that cannot always be controlled such as weather or pace of play. As Bondrea et al 2014 point out, pricing in the golf course sector requires a high level of specialisation. The handling of re-bookings is an important revenue management tool and managers turn to software to facilitate its control to maximise revenue. Thus, some research has developed models for allocating demand to available tee times (e.g., Rasekh and Li, 2011; Kimes and Schruben, 2002). However, while in the hotel or airline industries price segmentation is widely used to maximise revenue, in the golf course sector it is still under development (Pekgün et al., 2014; Li, 2014). This may be due to the special character of the golf course sector © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. C. Gázquez-Abad et al. (Eds.): NB&PL 2023, SPBE, pp. 127–133, 2023. https://doi.org/10.1007/978-3-031-32894-7_14

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(Bondrea et al 2014). Variables that are not always controllable, such as the pace of play or the weather, require a level of complexity that makes it difficult to manage reservations and maximise revenue. Thus, some research reveals that slower pace of play can lead to reduced revenue (Kimes and Schruben, 2002), as well as being one of the main customer complaints (Licata and Tiger, 2010). Golf club revenue in Europe comes mainly from members and green fees (Huth and Kurscheidt, 2019). In regions where golf tourism is encountered, clubs receive income from local players as membership and green fees and from golf tourism. In the autumn and winter season, which coincides with the peak golf tourism season, the management of bookings is more complex. In addition to the reduction of sunshine hours, and therefore capacity, there is an increase in demand due to the reception of this tourism. Managing member bookings in high season involves high operational costs. Online booking by this customer would reduce these costs and help streamline booking management, maximising revenue and optimising staff time. Managers try to get their members to book online, challenging the difficult balance already pointed out by McMahon-Beattie et al. (2002) between re-relating with customers and maximising revenue. However, they encounter barriers such as the lack of custom and personalised service required by this type of customer. Identifying these barriers to online booking would help to find marketing strategies to increase online green fee booking among golf club members. This study explores the profile of the golf club member and subscriber in a golf tourism destination, the barriers to online booking and their implications for revenue management, and the role of relationship marketing.

2 Research Design A qualitative research design was used, using a targeted sample of experts: the directors of six golf clubs in Andalusia (Spain). The selection criteria used was that the golf club had a percentage of outgoing members and subscribers of at least 25% per year. They were contacted through the researchers’ relationship with the founder of one of the golf clubs in Andalusia. All interviewees had the purpose of the research explained to them, and the reasons why they were selected. Participation was voluntary, confidential and informed consent was obtained. The interviews were conducted between April and May 2020 via the Zoom platform and lasted between 40 to 60 min. Interviews and classification of the themes that emerged was done manually by the researchers. Open-ended questions were asked to allow managers to elaborate on details of their management experiences in order to gain a more complete understanding of the phenomenon (Creswell, 2005).

3 Finding A number of themes emerged, which gradually outlined the defining characteristics of the type of customer, the “member or subscriber” of the golf club. The subscriber is a figure very similar to the member. The main difference is that the subscription is for a determined period of time, usually one year, with the need to renew his or her membership annually. This figure is more flexible than that of the member, as it does

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not oblige the client to continue paying a fee when he/she is no longer a user of the golf club. The demographic profile of this customer is largely male, between 35 and 75 years of age, with an average purchasing power. 3.1 Club Loyalty All the directors highlight the loyalty to the club among the characteristics of this client base. Some point out that golfers tend to play at the club where they started playing golf and that it is difficult for them to change clubs. The figures provided by the majority of those interviewed show that the percentage of members and subscribers who remain at their club is over 90% per year. They point out that the reasons for leaving are usually due more to giving up playing golf rather than moving to another club. “The bulk of members and subscribers have been playing at this club for years, normally those who start playing at a club usually become members or subscribers and continue to play at the same club”. 3.2 Sense of Belonging to a Club The interviewees point out that the golf club member feels identified with the club and likes to represent it when visiting another golf club. Moreover, they are regular customers and attendees of the activities organised by the club to which they belong. “Sometimes at the weekend they go to play competitions at other clubs when there is no activity here, so they like to feel identified with the club they come from and in a way they feel they represent it.” 3.3 Frequency of Visits to the Club All interviewees point out that the vast majority of members and subscribers are regular customers. This is the way to make the membership fee or the annual subscription profitable. They point to a minimum of 2 times a week to every day of the week. “Members and season ticket holders play a minimum of 2 times a week, if they do not buy the season ticket or pay the membership fee it does not pay off”. “Some play every day, although this is not the norm. But they are certainly regular customers of the club, at least every weekend”. 3.4 Interaction Between Club Members The interviewees emphasise the social nature of golf and how it is a sport in which relationships are established between the vast majority of club members. The member or subscriber usually always plays with the same companions, so the staff’s treatment of one of them can influence the relationship with the rest of the group. “There is always a leader of the group, so you have to pay a lot of attention to the relationship with this client, as most of the time he/she has great power of influence on the rest of the group”.

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3.5 Participation in Club Activities They add that club activities are necessary to retain this type of customer. All interviewees say that competitions are held with a certain frequency, at least once a month, and most clubs have an annual competition for members and subscribers that rewards regular play. All in all, the management is carried out with due consideration to the importance of this customer and the competitions that are held revolve around members and subscribers. “Almost every weekend we organise club competitions, it is fundamental to maintain the social mass”. The course managers add that around 20–30% of members or subscribers go at the weekend with family or friends, not only to play golf, but also to participate in what they call “club life”. This means spending time at the facilities, from playing golf to eating in the restaurant, passing time practising in the facilities or at the golf school, etc. “At the weekends they tend to do club life with their families, they go out to play golf or have a lesson or practice their swing or putt and then stay for lunch as normal”. “…At the club you can take part in a lot of activities, even though it’s all about the game”. 3.6 Use of the Services of the Club House Members and subscribers are the customers who most frequently, and on a regular basis, use additional services and facilities complementing the golf course, such as the cafeteria, the restaurant, changing rooms, driving range, golf school, golf equipment shop, etc. The interviewees point out that family events are usually held at the golf club’s facilities. “Most of the afternoon rounds end at what we call the 19th hole, it’s time to have a drink in the cafeteria and discuss the game”. “They usually buy all their golf equipment in the club shop”. “He is a very loyal customer of the club, holding almost all the family events in the golf club restaurant”. 3.7 Personalised Service This regular customer tends to establish a close relationship with staff, with whom they become familiar. “They are used to being treated in a very familiar way, when staff aren’t too busy, they can come in and talk to them for a long time”. The relationship with management is also a close and trusting one. The managers work on relational marketing, establishing a relationship with this client that allows them to satisfy their needs to a high degree. “We want our members and subscribers to be happy and to make a lot of life at the club, they are our best ambassadors”. “To take care of this customer, you establish a close and trusting relationship with them, you are in constant contact with them and you promote the club activities that you know they demand”. 3.8 Barriers to Online Booking When managers are asked about the barriers to make online bookings they encounter for members or subscribers, they point to several factors related to the characteristics of this type of customer. The lack of familiarity with online applications in the older group, the ability to do it directly at the club when they finish playing, the sense of entitlement to receive such a service and the preference for personalised treatment. In addition, the type

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of relationship between this customer and the company has made personalised service a habit. “Some don’t see the need to book online, they feel that this is their club and they want the time to be spent with them as long-standing and regular customers, just as the foreign golfer is looked after”. “Also, some of the older ones are not very skilled with online reservations, they are not used to it”, “for those who come to play every day, it is very difficult for them to book online, for them it is much more convenient to get back from playing and for the staff to make the reservation for them”. 3.9 The Operational Costs of Not Booking Online Interviewees have highlighted the difficulty of dealing with the reception and ensuring that both the member or subscriber and the green fee-paying customer are treated satisfactorily, in the high season of tourism. They point out that the decrease in availability generates discomfort among regular customers and makes it difficult to maintain their trust, while maximising revenue with the “passing through” customer. They point to a high time cost for reception staff in making reservations for members under these circumstances. “When members make a reservation at the club, the staff show them the availability, but it is common that the reservation is not immediate in the high tourist season, usually because the reduced availability generates complaints from the member and difficulties in matching the bookings. This delays the booking time a lot”. “… Staff have to give them a time that is not necessary if they themselves consulted availability and matched tee times with their partners”. 3.10 Online Booking Versus Personalised Service All managers point out that bookings from their members or subscribers account for only 5–8% of their online bookings. The relationship of trust that club staff and management establish with this customer leads to difficulties in making online bookings. The member or season ticket holder prefers the personalised treatment they are used to compared to online bookings. “They are used to a familiar and personalised service. When it is suggested to them that they book online, they prefer to be attended to by the club, sometimes even using the staff’s private telephone to make the booking. This makes the booking process complicated, especially in the peak golf season”. “We have tried to get them to book online to facilitate the booking process, but the club membership prefers the familiar service they are used to.”

4 Discussion The member or subscriber is a customer who is an important source of fixed annual income, the customer base with whom the long-term relationship is established. Moreover, income is not only received from the main activity, the golf course, but also from complementary activities, such as catering or the golf merchandise trade. However, the findings indicate that it is sometimes difficult to balance with the other source of income, the green fees of a golf club in a tourist destination. As Wang, (2012) points out, managers are faced with the problem of maintaining a balance between the long-term relationship with members and immediate benefit to the passing customer.

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From our analysis of the qualitative research it is clear that the main barriers to membership in golf clubs derive from the profile of this type of customer. According to previous studies, the golf club member has a sense of loyalty and belonging to the club (Back and Lee, 2009), participates enthusiastically in club activities and social interaction is common among the vast majority of members (Hwang et al., 2018). Their status as regular customers, accustomed to personalised service, is the main obstacle to online booking. When the high season arrives and the availability of courses decreases, it becomes difficult for them to receive the personalised service they are used to. However, members and subscribers want to be treated as usual, which jeopardises the relationship of trust with this customer. The high seasonality of golf clubs that are tourist destinations complicates the management of bookings and the balance between the relationship of trust with members and maximising revenue. The existence of barriers to online booking for this customer leads to high operational costs and sometimes inefficient booking management. This negatively affects the optimisation of capacity and therefore revenue. The qualitative exploration conducted in this research highlights the challenge for managers to maintain the balance between relationship marketing and revenue maximisation. This research has also highlighted the importance of the relationship that is established between club staff and this customer. Yang and Coates, (2010) have already pointed out the important role of golf club employees who deal directly with the customer in golfer satisfaction. Management is concerned with maintaining trusting relationships with them, knowing their behaviour, and the activities it organises in the club revolve around them, basing its business strategy on relationship marketing. However, the relationship marketing strategy widely used by managers, as shown above, is not sufficient to achieve the objective of increasing online bookings of members and subscribers. It follows from our analysis that the cause is the characteristics of this customer, rather than a misguided marketing strategy. We discovered that this customer is not only a fixed source of income, but also an asset for the company. This customer profile projects a positive image of the club. Their sense of loyalty and belonging to the club makes them ambassadors for the company, capable of attracting other players or non-golfing customers to attend the additional facilities and activities at the golf course. The findings suggest that an incentive programme could be put in place for members who book online. In other industries such as airlines and hotels, the use of this tool is common, such as loyalty programmes (Dekay et al., 2009). Managers could take advantage of the trusted relationship with this customer to find out the most valued reward method among members. As in the hotel industry, loyalty programmes can affect customer behaviour (Pesonen et al 2019). This research proposes the consideration of this tool in the golf course sector.

References Back, K.-J., Lee, J.-S.: Country Club Members’ Perceptions of Value, Image Congruence, and Switching Costs: an Exploratory Study of Country Club Members’ Loyalty. J. Hosp. Tourism Res. 33(4), 528–546 (2009)

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Bondrea, A.A., Draghici, M.I., Stefaneascu-Mihaila, R.O.: Price differentiation and rate fencing in golf course sector”. Paper presented at the International Multidisciplinary Scientific Conferences on Social Sciences and Arts (SGEM 2014), Albena, Bulgaria (2014) Creswell, J.W.: Educational Research: Planning, Conducting and Evaluating Quantitative and Qualitative Research. Pearson, New Jersey (2005) Dekay, F., Toh, R.S., Raven, P.: Loyalty Programs: Airlines Outdo Hotels. Cornell Hosp. Q. 50(3), 371–382 (2009) Huth, C., Kurscheidt, M.: Membership versus green fee pricing for golf courses: the impact of market and golf club determinants. Eur. Sport Manag. Q. 19(3), 331–352 (2019) Hwang, J., Han, H., Hyun, S.S.: The antecedents and consequences of visitors’ participation in a private country club community: The moderating role of extraversion. J. Destin. Mark. Manag. 7, 89–100 (2018) Kimes, S., Schruben, L.: Golf course revenue management: A study of tee time intervals. J. Revenue Pricing Manage. 1, 111–120 (2002) Li, W.L.: Revenue Management in the Golf-course Industry: Feasibility and Strategies In: 2014 4th International Conference on Applied Social Science, Pt 3, 53, 426–432 (2014) Licata, J.W., Tiger, A.W.: Revenue Management in the Golf Industry: Focus on Throughput and Consumer Benefits. J. Hosp. Market. Manag. 19(5), 480–502 (2010) McMahon-Beattie, U., Yeoman, I., Palmer, A., et al.: Customer perceptions of pricing and the maintenance of trust. J. Revenue Pricing Manage. 1, 25–34 (2002) Noone, B.M., Enz, C.A., Canina, L.: The Uniqueness of Revenue Management Approaches in Nontraditional Settings: The Case of the Golf Industry. J. Hosp. Tourism Res. 43(5), 633–655 (2019) Pekgün, P., Uyar, E., Garner, B.: Applying pricing and revenue management in the golf industry: Key challenges. J. Revenue Pricing Manage. 13, 470–482 (2014) Pesonen, J., Komppula, R., Murphy, J.: Plastic loyalty – Investigating loyalty card programs for a Finnish hotel chain. Tour. Manage. 73, 115–122 (2019) Rasekh, L., Li, Y.: Golf course revenue management. J. Revenue Pricing Manage. 10, 105–111 (2011) Yang, H., Coates, N.: Internal marketing: service quality in leisure services. Mark. Intell. Plan. 28(6), 754–769 (2010) Wang, X.L.: Relationship or revenue: Potential management conflicts between customer relationship management and hotel revenue management. Int. J. Hosp. Manag. 31(3), 864–874 (2012)

The Influence of the Covid-19 Pandemic on Social Media Engagement of Luxury Hotels Mónica Gómez-Suárez(B) , Mónica Veloso, and Myriam Quinones Finance and Marketing Department, Universidad Autónoma de Madrid, Madrid, Spain [email protected]

Abstract. The objective of this study is to analyze the use of social media by luxury hotels, focusing on identifying the reactions of consumers toward the experiential messages issued by this type of hospitality firm. This research emphasizes how the crisis caused by the coronavirus pandemic affected potential guests’ online engagement behavior. An exploratory observation of the official social media accounts of 36 premium hotels in the Canary Islands allows for extracting digital content from 1070 Facebook posts published in 2019 and 624 posted in 2020. Based on descriptive analysis, engagement indexes are built on quantifiable elements of participation (i.e.: likes, shares, and comments). The key implication of this research is that luxury hotels have considerable scope to improve their communications on social media, especially regarding the design and diffusion of messages that include affective components. Keywords: online engagement · customer experience · hospitality · COVID-19

1 Introduction This research aims to examine how users (potential tourists) reacted to messages published by luxury hotels on Facebook before and during the coronavirus pandemic, which dramatically affected tourism activity. During Covid-19 outbreak, social networks became an ideal promotional platform for hotels to interact with their customers. However, even though the hotel industry has spent considerable time and resources optimizing the use of social media channels, there is still little empirical evidence available on how the communication process works and for what overall purposes (Harrigan et al., 2017). Thus, the nature and effectiveness of the hotels’ relationship with social media users represent a relevant research topic in the hospitality industry (Leung et al., 2017). Customer engagement in social media characterizes by repeated interactions between This study was supported by the project BBforTAI (PID2021-127641OB-I00 MICINN/FEDER), Biometrics and Behavior for Unbiased and Trustworthy AI with Applications It also benefited from the Professorship Excellence Program under the multiyear agreement signed by the Government of Madrid and the Autonomous University of Madrid, UAM (Line #3). The research was conducted under the framework of the Research UAM Group TECHNOCONS “Consumer Behavior and Technology”. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. C. Gázquez-Abad et al. (Eds.): NB&PL 2023, SPBE, pp. 134–141, 2023. https://doi.org/10.1007/978-3-031-32894-7_15

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a customer and a brand/organization that affect self-brand connection and brand-usage intention. As a result, customers who engage with a company online via social media feel more connected to the firm’s brands, trust those more, and display greater loyalty toward the company’s products and services. While the evaluation of any experience has both a cognitive and an emotional component, tourists tend to make emotional evaluations of their experiences (Serra-Cantallops et al., 2018). Communication that includes experiential content promotes the development of affective ties with the brand, which influences subsequent behavior (GómezSuárez and Veloso, 2020). However, studies on the topic of destination brands point out that rational messages are very broadly used by hospitality firms on social media (Molinillo et al., 2018). Likewise, the use of social media by European destination brands produces low levels of customer response due to an overreliance on unimaginative traditional marketing approaches (U¸saklı et al., 2017). Therefore, this study analyses how the pandemic has affected social media engagement published by luxury hotels, paying special attention to the potential tourists’ reactions to messages with experiential content. The following questions drive this research: 1) How did the COVID-19 crisis influence this relationship? and 2) In the specific context of luxury hotels, how does the hotel’s experiential content on social media affect user engagement? This research contributes to extant knowledge by improving the understanding of how luxury hotels generate customer engagement through social media and by offering relevant information about how the emotional investment of customers changed due to the COVID-19 outbreak. It is based on an observation study applied to the Facebook posts of 36 premium hotels published during the summers of 2019 and 2020. The results show that behavioral engagement with luxury hotels increases when the hotels’ communication strategy on social media focuses on broadcasting messages related to the customer experience.

2 Theoretical Background Participation in the online community generated around a brand is an indicator of engagement with the company or brand (Vaiciukynaite & Gatautis, 2018). In the tourism sector, social media facilitates customer engagement (Harrigan et al., 2017). Luxury hotels use social networks extensively to reinforce their image and support their competitive advantage (Cervellon and; Galipienzo, 2015). The use of social media allows them to engage with their customers and, in turn, to improve the understanding of their needs and adapt to their preferences. More specifically, these platforms help hotels build lasting relationships with their clients, generate direct conversations with customers, and elicit opinions from other stakeholders (Leung et al., 2017). The resulting dialog allows the co-creation of content, which can greatly influence consumer choice and the hotel’s reputation. When the followers of a social profile use these options frequently, they are not only showing interest in the content of the publications but also signaling their proneness to establish communication with the brand (Wang et al., 2017; Oh et al., 2017). Actions such as liking a post, sharing a post, exchanging opinions, and reacting with emojis are manifestations of user engagement (Bonsón and Ratkai, 2013; Wang et al., 2017). Regarding engagement, users can express their involvement with the content in three different ways: (1) using the icons that represent different reactions (likes and other

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emoticons), (2) commenting or (3) sharing content. The “likes” allow users to express their positive feelings, although this type of interaction represents the lowest degree of commitment and cognitive effort (Vaiciukynaite and Gatautis, 2018). Comments and shares show a higher degree of adherence to the company than “liking”, which only requires a “click”. Comments allow users to express their opinions and feelings, representing a medium engagement level. Content sharing means that users distribute the message published by the brand, often including content published by the user (Kim and; Yang, 2017), and this represents the highest level of involvement (Vaiciukynaite and Gatautis, 2018). Then, “likes” are classified as indicators of popularity, comments as indicators of commitment, and the act of sharing content as an indicator of virality. Table 1 summarizes the definitions and how the engagement rates are calculated based on these measures. Table 1. Engagement Indexes Popularity (P) P2

Attractiveness and visibility Total number of likes / Total The average number of of the messages number of posts positive reactions per publication

P3

(P2 / Total number of Followers) * 1000

The average number of positive reactions per 1000 fans per post

Commitment (C) C2 C3

Higher level of participation Total number of comments / with other users and with Total number of posts the brand itself (C2 / Total number of Followers) * 1000

The average number of comments per publication The average number of comments per 1000 fans per post

Virality (V) V2 V3

The interest of users in the Total number of shares / brand and its content shared Total number of posts through social media (V2 / Total number of Followers) * 1000

The average number of “shares” per publication Number of media “shares” per 1000 fans per post

ENGAGEMENT (E) E

E = P3 + C3 + V3

Total Engagement

Source: Own elaboration based on Bonsón and Ratkai (2013),Molinillo et al. (2018)

3 Methodology The content of the messages was extracted from posts on the official Facebook sites of all the premium hotels located in the Canary Islands (36). This tourist destination represents a first-rate market. It attracted 13 million tourists in 2019, who spent more

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than 16,000 million euros during their holidays in Spain. In 2020, the number of visitors dropped to 3.5 million due to the coronavirus pandemic, which represents a decline of 71% compared to the previous year. That year, their spending totaled 4,816 million euros. The temporal scope of this study includes the main months of the summer campaign (July and August) in two consecutive years: 2019 (pre-COVID-19 pandemic) and 2020 (during the COVID-19 pandemic). Despite the spectacular growth of other platforms, especially among younger users, such as Tik Tok or Instagram, Facebook is still the most popular social media all over the world with more than 2900 million users (Statista, 2022). This platform allows higher levels of interaction between the hotel and potential hotel customers, and among users which in many cases influence other users’ purchase decisions (Madhusanka et al., 2020). In this sense, Facebook allows us to classify, count and proceed with text messages easily in comparison with other social media platforms. The sampling unit is each publication of each observed hotel. The data were complemented using social media measurement tools, which granted access to hashtags, keywords, types of shared content, and frequency, among other parameters. In total, 1070 Facebook posts from 2019 and 625 posts from 2020 were analyzed. These include posts in Spanish and English. First, an exploration of the textual information was carried out. This process involved downloading publications and compiling the data to identify possible discrepancies, which could have biased the analysis. The content was then classified into topics and categories with similar characteristics. Likewise, keywords were tagged, and their frequency of appearance was recorded. In addition, the number of fans, the number of publications, and the number of likes, comments, and shares were noted down. Subsequently, the messages that had experiential content were detected (431) and then encoded using Excel. Finally, their popularity, commitment, virality, and engagement rates were calculated.

4 Results The sampled hotels posted an average of 29.61 messages per hotel in 2019 (pre-COVID19) and 17.36 in 2020 (during-COVID-19). Before focusing on the specific indexes, a straightforward count of words could offer a first insight to understand the type of messages posted. Table 2 shows the number of times and the weight of the words most frequently mentioned. Informational posts about events tend to dominate. Words (mainly in Spanish) such as “enjoy”, “views” and “experience” were included most often in 2019’s posts, replaced by terms such as “security” and “safety” in 2020. In 2020, words such as “security”, “dream”, “safe” and “protocols”, which were not mentioned in 2019, were used to help minimize the perceived risk of COVID-19. Also related to the pandemic, mention was made of “reopening”, “return” or “waiting”, as was remunerative content that encouraged visiting, such as “discount”. Related to the comparison between the two years, to understand if the Covid-19 pandemic could affect the different observable variables extracted from social media, Table 3 shows the total number of publications, experiential posts, fans, and reactions (likes, comments, and shares) for each one of the two years under observation. These differences are statistically significant. The ANOVA test for the raw data of each numeric

138

M. Gómez-Suárez et al. Table 2. Words: Number of Mentions and Weight 2019

2020

Rank

Count

Weight

Word

Count

Weight

Word

1

103

1.04%

Enjoy

117

1.23%

Security

2

72

0.73%

Spa

88

0.93%

Enjoy

3

52

0.52%

Restaurant

62

0.65%

Book

4

68

0.69%

Pool

57

0.60%

DreamDays

5

49

0.49%

Views

63

0.66%

Suites

6

43

0.43%

Bar

39

0.41%

Beach

7

42

0.42%

Wellness

38

0.40%

DreamSAFE

8

41

0.41%

Experience

36

0.38%

Reopening

9

40

0.40%

Sea

32

0.34%

Beach

10

40

0.40%

Resort

32

0.34%

Return

11

37

0.37%

Place

31

0.33%

Waiting

12

65

0.66%

Suites

30

0.32%

Experience

13

56

0.56%

Beach

29

0.31%

Sunset

14

30

0.30%

Team

26

0.27%

Views

15

30

0.30%

Family

25

0.26%

Discount

16

29

0.29%

Relax

25

0.26%

Sun

17

26

0.26%

Book

24

0.25%

Sea

18

26

0.26%

Sun

23

0.24%

Information

19

25

0.25%

Night

22

0.22%

Protocols

20

24

0.25%

Festival

21

0.22%

Web

9921

9517

variable as dependent and the years (2019, 2020) as the independent variable with two levels showed that means were different at a p-value higher than a significance level of 5%. Although the overall number of fans increased by almost 25% from one year to the next, the number of publications decreased by 41%. During 2020, the number of “likes” dropped by 13% compared to the same period in 2019, while the number of comments was practically the same (0.7%). In contrast, there was an increase in shares of almost 70% year-on-year. The number of fans could increase for two reasons. A year has passed between each data collection. Hence, if the accounts continued to publish content on social media for 12 months, they likely attracted new users. More importantly, during 2020, the time spent on social networks probably increased. Thus, users with more free time might have followed specific hotel pages to travel there once it was possible. That is, they

The Influence of the Covid-19 Pandemic on Social Media

139

Table 3. Total Number of Publications, Fans, and Reactions (2019–2020) Variables

2019

2020

Incr

Publications

1070

625

-41.6%

Experiential

244 (23% of publications)

187 (30%) of publications)

-23.4%

Fans

278895

346854

24.4%

Likes

41220

35796

-13.2%

Comments

4725

4756

0.7%

Shares

3721

6276

68.7%

would follow the pages for inspiration. As for why other metrics such as likes decreased and shares increased, one possible explanation is that users were “more involved” or “committed” to hotels during the pandemic. In this case, instead of pressing a simple like (behavior with the lowest degree of involvement), they shared the content of the publication (behavior with the highest degree of involvement). To understand the true impact of these indicators, it is pertinent to analyze the popularity, virality, commitment, and engagement indices (Table 4). As mentioned before, “likes” are indicators of popularity, comments are indicators of commitment, and sharing contents are indicators of virality. The results show that popularity grew by 20%, commitment by 39%, and virality by 132%. To show the actual increase in engagement rates between the two years, “likes” were divided by the number of publications and fans. Facebook fans ‘reactions to the luxury hotels’ posts (engagement) increased by 30% during the health crisis compared to 2019. Relating to the experiential content, messages focused on “experience” are those that express both plans and activities enjoyed by guests on the hotel grounds and surroundings. The second column of Table 4 shows the difference in indexes related to this type of message. Experiential content was present in 23% of the messages posted in 2019 and 30% of the posts from 2020. These types of messages generated an increase in engagement of 61% year-on-year. Table 4. Engagement Indexes TOTAL MESSAGES

EXPERIENTIAL CONTENT

POPULARITY

Index

2019

2020

2019

2020

P2

Likes/Post

38.523

57.274

32.275

56.374

P3

(P2/Fans) *1000

.138

0.165

.116

.176 (continued)

140

M. Gómez-Suárez et al. Table 4. (continued) Annual increment

20%

52%

COMMITMENT C2

Comments/Post

4.416

7.610

2.787

8.332

C3

(C2/Fans) *1000

.016

0.022

.010

0.026

39%

161%

VIRALITY V2

Shares/Post

3.478

10.042

3.451

5.674

V3

(V2/Fans) *1000

.012

0.029

0.012

0.018

Annual increment

132%

43%

ENGAGEMENT E

P3 + V3 + C3 Annual increment

.166

0.216 30%

.138

0.220 61%

5 Conclusions This research identifies and quantifies travelers ‘reactions to hotels’ posts on social media. It contributes to improving the understanding of customer engagement with luxury hotels through Facebook. The focus on travelers that show interest in a destination hardly hit by COVID-19 is an additional contribution of this research work. This study provides information on two points in time, before and after the pandemic, providing insights into the impact that fear of COVID-19 had among users of higher-category hotels. The results suggest that the number of social media fans of luxury hotels increased during the pandemic and that the active participation of potential clients (likes, comments, and shares) increased in 2020 compared to 2019. The findings also indicate that potential guests appear more engaged when the messages have experiential content. From a managerial point of view, there are several contributions to this study. The health crisis favored customer interactions with the content posted by premium hotels on social networks. To design an efficient communication strategy, it is relevant to understand what type of communication is the most effective (Leung et al., 2017). After a period of crisis that has affected tourist mobility enormously, luxury hotels could reactivate their interaction with potential clients by emphasizing posts with experiential content. Social media messages related to the establishment’s atmosphere and entertainment offered by the hotel is relevant. Hotels, more specifically their community managers, must resort more to posting emotional content that alludes to the enjoyment of the tourist experience because these publications encourage the creation of emotional bonds. In addition, content with attractive promotions or contests, combined with content that offers reassurance about the safety of the destination could increase customer engagement. In doing so, the efficiency of social networks as amplifiers of positive comments could improve. This continues to be an opportunity for the competitive differentiation of any hotel brand. Regarding limitations, the data obtained come from the Facebook pages of

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hotels from a single country. Hence, future studies could adopt a broader transnational approach by analyzing samples of hotels in several countries. Measuring the activity on different alternative social media, such as Instagram or Tik Tok, could be also helpful to understand hotels ‘marketing actions and users’ engagement interaction with their messages. Finally, the use of techniques such as sentiment analysis would allow a better understanding of the emotional reactions of the users to the content of the messages published on social media.

References Bıçakcıo˘glu, N., ˙Ipek, ˙I, Bayraktaro˘glu, G.: Antecedents and outcomes of brand love: the mediating role of brand loyalty. J. Mark. Commun. 24(8), 863–877 (2016) Bonsón, E., Ratkai, M.: A set of metrics to assess stakeholder engagement and social legitimacy on a corporate Facebook page. Online Inf. Rev. 37(5), 787–803 (2013) Cervellon, M.C., Galipienzo, D.: Facebook page content, does it matter? Consumers’ responses to luxury hotel posts with emotional and informational content. J. Travel Tour. Mark. 32(4), 428–437 (2015) Exceltur. (2021). https://www.exceltur.org/pib-turistico-espanol/ Date: 06–11–2022 Gómez-Suárez, M., Veloso, M.: Brand experience and brand attachment as drivers of WOM in hospitality. Spanish J. Mark. 24(2), 231–246 (2020) Harrigan, P., Evers, U., Miles, M., Daly, T.: Customer engagement with tourism social media brands. Tour. Manage. 59, 597–609 (2017) Kim, C., Yang, S.U.: Like, comment, and share on Facebook: How each behavior differs from the other. Publ. Relat. Rev. 43(2), 441–449 (2017) Leung, X., Bai, B., Erdem, M.: Hotel social media marketing: A study on message strategy and its effectiveness. J. Hosp. Tour. Technol. 8(3), 239–255 (2017) Madhusanka, J.D.T., Weerasiri, S., Karunarathne, W.V.A.D.: The impact of electronic word of mouth on brand evaluation leading to brand attachment: a comparative study on consumer electronics and cosmetic brands in Sri Lanka. Kelaniya J. Manage. 9(2), 1 (2020) Molinillo, S., Liébana-Cabanillas, F., Anaya-Sánchez, R., Buhalis, D.: DMO online platforms: Image and intention to visit. Tour. Manage. 65, 116–130 (2018) Oh, C., Roumani, Y., Nwankpa, J., Hu, H.: Beyond likes and tweets: Consumer engagement behavior and movie box office in social media. Inf. Manage. 54(1), 25–37 (2017) Serra Cantallops, A., Cardona, J.R., Salvi, F.: The impact of positive emotional experiences on eWOM generation and loyalty. Spanish J. Mark. 22(2), 142–162 (2018) Statista https://es.statista.com/estadisticas/513452/numero-global-usuarios-trimestrales-fac ebook/ 10 Dec 2022 U¸saklı, A., Koç, B., Sönmez, S.: How “social” are destinations? Examining European DMO social media usage. J. Destin. Mark. Manag. 6(2), 136–149 (2017) Vaiciukynaite, E., Gatautis, R.: How can hotel companies foster customer sociability behavior on Facebook? J. Bus. Econ. Manag. 19(4), 630–647 (2018) Wang, R., Kim, J., Xiao, A., Jung, Y.: Networked narratives on humans of NY: A content analysis of social media engagement on Facebook. Comput. Hum. Behav. 66, 149–153 (2017)

Changes of Online Shopping Among the Elderly During the Corona-19 Pandemic Hanna Gendel Guterman(B) , Idit Sohlberg, and Shalom Levy Department of Economics and Business Administration, Ariel University, Ariel, Israel [email protected]

Abstract. The elderly is an important segment for the business industry. More elderly now shops online than ever before. There is scant previous research that explores this group according to subgroupings of age, and the published studies typically included a limited range of products and services. This research aims to close this gap by segregating respondents into three groups according to age: 55–64, 65–74 and 75+ years. Furthermore, a second aim is to find the effects of the COVID-19 pandemic on Web activities for these subgroups. The data was based on two surveys of the same 328 identified respondents, before and during the pandemic. The results show that on-line shopping diminished mainly in the oldest age group, about 70–75 years, but remained the same in regard to on-line banking activities. Surprisingly, during the pandemic, there was a reduction of on-line shopping, chiefly among the youngest group. However, the elderly increased their on-line activity in one area: shopping for food. Notably, the pandemic produced weaker effects on on-line shopping in the oldest group. Keywords: Elderly group age; web shopping · Web banking activity · Corona-19 pandemic

1 Introduction In most developed countries, the share in population of the elderly, defined as people aged 65 and over, is growing rapidly. According to a widespread study, the relative income level of the elderly in developed countries was higher or almost equivalent to the average national income per capita (OECD 2017). Thus, the elderly population plays a significant role in the consumption economy, which will increase in the coming years (Kwan and Walsh 2018). The elderly also participates more in using the Web for various goals, among them business activities and shopping (Lian and Yen 2014; Yap et al. 2022). In spite of the existing research (Chan and Chou 2018), the elderly is not a homogeneous group, but change their buying behavior in regular stores and on the Web as they advance in age. No recent research was found regarding the web behavior of the elderly according to their relatively age, young older or older elderly. In the last three years, around the globe people faced the terrible results of the COVID-19 Pandemic and the elderly was one of the most affected group by it. Due to imposed closures in many countries, many consumers, especially the elderlies, moved to shopping on the Web. However, research © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. C. Gázquez-Abad et al. (Eds.): NB&PL 2023, SPBE, pp. 142–150, 2023. https://doi.org/10.1007/978-3-031-32894-7_16

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regarding this group in connection with the web focused mostly on health help by the web (Chou et al. 2023) or shopping food (Gao et al. 2020). This research is closing the gap with double aims: First, to explore shopping habits of sub-groups of the elderly for products and services on the Web in a wide domain range. Second, to reveal what changes have occurred due to the pandemic. This study is based on two surveys of the elderly, one before and one during the pandemic. The respondents were divided into three groups: pre-elderly (age 55–64), young elderly (age 65–74) and older elderly (age 75–84).

2 Background 2.1 COVID-19 Pandemic Effects The COVID-19 pandemic has considerably changed the habits of millions of Internet users around world. Government closure forced large populations to depend on their residential Internet connectivity for work, social activities, and shopping (Feldmann et al. 2020; Al-Halbusi et al. 2022). The interactions through the web increased dramatically, by young and not-so-young alike (Zamboni et al. 2021), arousing not positive thoughts and feelings (Faqih 2022). Yet, it was found that the elderly has also more responsive self-control and can recover from negative emotions in shopping compared with younger consumers (Kim and Jang 2015). 2.2 Ageing Population and Consumer Behavior In most developed countries, the share of elderly people in the population is growing rapidly. The number of elderly is expected to double again by 2050, and to hold a share of 35% of the population in Europe, 28% in North America, 25% in Latin America, and 24% in Asia. Usually, people over 65 are considered to be elderly (OECD 2020). Nonetheless, this is not an accepted definition in all circles, and in academic research, some regard elderly at age 60+ (Kim and Jang 2015) and others even as low as 55+ (Chaouali and Souiden 2019). Contrary to the popular image of the elderly having a rather low income, those over 65 were found to have incomes amounting to 88% of the average population income per capita. The elderly fared best in France, Israel, and Luxembourg, with incomes equal to or slightly higher than the population average (OECD 2017). Clearly, the elderly in developed countries is an essential segment of the economy in general and of the consumer market in particular (McCloskey 2006). Research about the behavior of elderly consumers on the web has focused mainly on specific services such as banking (Moliner-Tena et al. 2018) and in certain product types such as food (Kohijoki 2011), apparel (Mumel and Prodnik 2005). 2.3 Ageing Currently, the elderly feels themselves more physically and mentally fit than their parents at the same age. They differ from the earlier generation by having higher purchasing power, better health and longer life expectancy, leading to changes in lifestyle and shopping behavior (Marjanen et al. 2016). Regarding shopping on the Internet, there are two

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issues to be addressed: frequency of use of the Internet by the elderly, and the activities the elderly are performing. Internet use has been found to decrease with age (Pierce 2010; Rainie (2010)). However, the elderly is found to have a faster growth rate of using the Internet (Lian and Yen 2014). The share of adults 65 + years in the United States using the Internet in 2021 was 75% compared to 98% of the younger group. Research has found that there are specific Internet functions that are used more by the elderly including travel reservations (Zickuhr 2010), banking (Wang et al. 2017; Jiang et al. 2022), and Internet shopping and auctions (Hilt and Lipschultz 2004; McCloskey 2006; Yap et al. 2022).

3 Method 3.1 Sample The research was based on two surveys. The first was done in 2019 before COVID-19; the second was done during the pandemic in 2020. It included 328 identified respondents that participated in both surveys. Respondents were aged 55 to 84 and divided into three groups: pre-elderly aged 55–64, 80 respondents; young elderly aged 65–74, 170 respondents; and older elderly aged 75–84, 78 respondents (due to the fact of their reduced share in the population). The socio-demographic traits were very similar in each group: About half of the respondents were men and half were women; 20% had a high school education, 49% an academic one. Regarding economic status measured by income, 30% had less than the average, 35% the average, and 35% above the average. 3.2 Procedure The respondents were approached through an e-mail panel survey company and were chosen by using a stratified sampling system according to age and gender. Anonymity and confidentiality were promised and respected. The research was approved by the ethics committee of the university. 3.3 Measurements The survey instrument consisted of items that were related to different types of shopping on the Web, as found in statistical data (Karmona 2017; CBS Israel 2021), one question for a typical product, service of shopping, and one general question on shopping on-line. A five-point Likert scale was used, ranging from 1 = never, to 5 = always. Data analysis was performed with SPSS: exploratory factors test, Tests of reliability, two-way ANOVA analyses and Paired Samples t Tests.

4 Results The analyses tested the following hypotheses: H1: The level of shopping on the Web decreases with age. H2: The level of shopping on the Web increased during the pandemic. H3: The change of shopping activities on the Web due to the pandemic will have less effect on the oldest sub-group of the elderly (75 +).

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4.1 Period Before COVID-19 4.1.1 Validity and Reliability First, items were subjected to an Exploratory Factor Analysis (EFA) with Varimax rotation. Two factors were produced that explain 52% of the cumulative variance. The first factor (7 items) describes product shopping on-line and the second factor (4 items), the use of the Web for banking and credit card activities. Following Podsakoff et al. (2003), Harman’s one-factor test was used, showing that the single factor of all items explains 37% of the total variance. This procedure indicates that common method variance bias may not be a severe problem. Tests of reliability of the scale of product, services shopping, resulted in Cronbach’s alpha = .77 and the second scale, the banking services, resulted in Cronbach’s alpha = .74. A test showed that being active in these two types has high correlation of 2-tailed: .46 (Correlation is significant at the 0.01 level). The findings of the Web activities are represented in Table 1. Table 1. Web activity level before COVID19 Group / Age

All

55–64

65–74

75 +

Shopping in general

3.26

3.40

3.36

2.88

Scale of shopping product/service

2.24

2.42

2.29

1.94

Scale of banking activities

3.71

3.66

3.76

3.69

Regarding general shopping, no significant differences were found between the two younger groups, but both had higher scores than the 75+ group (sig. = .01). The same result was found for shopping products (sig. < .01). However, concerning bank activities, no significant difference was found between the groups. Table 1 shows that there is a significant difference in all the groups between general shopping and detailed answers to various types of shopping (sig. = .000). Yet for all groups, there were significant higher scores for banking activities compared to the questions of general shopping and detailed shopping. Table 2 exhibits the scores found for each type of shopping/bank activity. Table 2. Web activity level before COVID-19 Activity / Age

All

55–64

65–74

75 +

Shop for clothes

2.01

2.12

2.10

1.68a

Shop for books

1.98

2.04

2.05

1.77

Shop for electronic devices

2.20

2.38b

2.26b

1.84a

Shop for food

2.27

2.41

2.32

2.04

1.69

1.95b

1.73b

1.32a

Shop for sporting goods

(continued)

146

H. G. Guterman et al. Table 2. (continued) All

55–64

65–74

75 +

Order prepared food for delivery

2.39

2.67b

2.36

2.14c

Order prescriptions

3.17

3.38b

3.22

2.81

Check bank balance

4.33

4.22

4.35

4.41

Make transfers and payments

4.06

4.05

4.05

4.09

Stock market investing

2.61

2.26a

2.72

2.76

3.86

4.10b

3.90b

3.50c

Activity / Age

Pay by credit card

Note. a significant difference from both groups; b significant difference from 75+; c significant difference from 55–64 Table 3. Comparison of economic status before and during COVID-19 restrictions by age Group / Age

All

55–64

65–74

75+

before

23.5

56.8

16.8

2.7

during

19.8

49.4

12.7

4.1

Work part-time:

before

13.7

17.3

15.6

5.4

during

11.6

17.3

13.3

Income

before

3.04

2.99

3.05

3.08

during

2.98

2.88

3.05

2.95

Work full time:

1.4

Table 4. Web activity level before and during COVID-19 restrictions by age Group / Age General shopping

Scale of shopping

Scale of banking activities

before

All

55–64

65–74

75+

3.26

3.40

3.36

2.88

during

3.00

2.95

3.14

2.73

% change

−8

−13

−7

−5

before

2.24

2.42

2.29

1.94

during

2.06

2.14

2.11

1.82

% change

−8

−12

−8

−6

before

3.71

3.66

3.76

3.69

during

3.57

3.50

3.63

3.50

% change

−4

−4

−3

−5

Table 2 reveals differences between the activities of the groups. However, usually the group of 75+ are less active in product shopping and services. In this category, the highest

Changes of Online Shopping Among the Elderly

147

Web shopping is in prescription ordering, food shopping and delivery, and electronic devices shopping. 4.2 Changes in Economic Situation Due to COVID-19 A comparison of economic status before and during the pandemic was used to explaining differences in using the Web. The findings reveal that the rate of unemployed increased during the pandemic due mainly to the closure of many working places, especially among the 65–74 group. Yet there were no indications of significant changes in income between the two periods, perhaps due to government support of the unemployed during to the pandemic (Tables 3 and 4). Decreases in using the Web for shopping were established, all of them significant (sig. < .01), except for the general shopping of the elderly group 75+. The major decrease was among the younger group of 55–64. The lesser decrease was among the elderly group, 75+. However, banking activities were reduced by about the same percentage across all age groups. There were differences found in the changes of the different types of activities using the Web from before and during the pandemic. Table 5 reveals that during the pandemic, the only Web activity that increased was shopping for food online, notably, in the group of 65–74. The greatest reduction in Web activities was in shopping for nonessentials such as electronic and sports devices, books, and prepared food delivery. The reduction in Web activities may contribute to the reduction of payments with credit cards, representing general reduction of making purchases of all kinds, especially non-Web purchases. Table 5. % change in on-line activities level before and during COVID-19. Activity / Age

All

55–64

65–74

75+

Shop for books

−12.9

−15.8*

−12.4*

−10.7*

Shop for electronic devices

−28.1

−30.6*

−28.9*

−22.1*

Shop for food

5.8

−1.0

10.3*

2.6

Shop for sporting goods

−14.8

−18.4*

−16.1*

−5.1

Order prepared food for delivery

−13.0

−13.0*

−13.7*

−5.1

Order prescriptions

−1.6

−5.1

0.1

−3.8

Check bank balance

−0.1

−0.9

0.3

−0.3

Make transfers and payments

−2.3*

−1.2

−2.0

−4.3*

Stock market investing

−10.7*

−9.8*

−10.2*

−12.7*

Pay by credit card

−5.2*

−7.2*

−4.0*

−5.8*

Note. * The change is statistically significant.

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5 Discussion This research has established that on-line shopping decreases when consumers become older. Nevertheless, the reduction occurs around the age range of 70 to 75 years, which coincides with the age when workers generally retire. Moreover, it was also proven that there are no differences between the groups regarding banking services, including the older elderlies, services which were adopted early on the Web. However, this older group showed significantly less use of credit cards, perhaps due to reduced trust in them. Thus, Hypothesis 1 was partly accepted. One can presume that in the comings years that those who are now young will not reduce their use of the Web because of experience. Contrary to hypothesis H2 and general behavior of the adult population, most of online shopping by the elderly decreased during the pandemic in many shopping activities. The reduction can be explained by more caution in spending money in a time of economic uncertainty, increased unemployment among those ages that were still working before the pandemic, fear of sickness posed by human interactions (even though this interaction is even less when using the Web). Yet online shopping for food, which is one of the earliest and the biggest categories, increased during this period. There was significant reduction in financial activities on the Web due to the recession effected by the pandemic. Hypothesis 3 was accepted. The reduction of on-line shopping (not including online bank activities) was less pronounced by the 75+ years group. This can be explained by the fact that this group did not suffer from increased unemployment and also the inclination to overcome crises more easily than younger people (Kim and Jang 2015). The theoretical contribution of this research and its unique is treating the elderly as a heterogenic group, showing that consumer on-line behavior is changing according to the elderly group’s ages. This study’s uniqueness is using the same respondents in the surveys, before and after, resulting in a reduced bias of samples. The empirical contribution of the research is the separation of the elderly into subgroups, giving the opportunity for more precisely targeted marketing. The major limitation of the research is that the sample was collected by a Web panel, in which respondents are more accustomed to the Web. Thus, the absolute numbers for using the Internet may be biased and not reflect the numbers in the general population. Second, this research was conducted in one country. For more generalization, future research should be conducted in other countries.

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Author Index

A Alaminos, David 100 Ali, Shoaib 41 Argila-Irurita, Ana María 100 B Bartholomew, Darrell 50 Bekdache, Salwa 70 Belaid, Samy 13 Brengman, Malaika 89 Briegel, Hunter 50 Brüggemann, Philipp 119 C Chen, Allen 65 Chen, Yi-Mu 65 D D’Arco, Mario 23 Del Mar Martín-García, María 127

K Karoui, Sedki 13 Kumar, Nanda 3 L Lacoeuilhe, Jérôme 13 Levy, Shalom 142 Luis Ruiz-Real, José 127 M Marino, Vittoria 23 Murthi, B. P. S. 3 N Naveed, Muhammad P Pahwa, Parneet

41

3

Q Quinones, Myriam 134

F Farah, Maya F. 41, 70 Fehri, Dorsaf 13 Fuduri´c, Morana 31

R Ramadan, Zahy 70 Resciniti, Riccardo 23

G Gázquez-Abad, Juan Carlos 127 Gómez-Suárez, Mónica 134 Guillén-Pujadas, Miguel 100 Guterman, Hanna Gendel 142

S Salvietti, Giada 57 Schlesinger, Walesska 83 Schultz, Carsten D. 119 Škare, Vatroslav 31 Sohlberg, Idit 142 Solé-Moro, María Luisa 100

H Hampton, Stephen 50 Hellemans, Johan 89 Horvat, Sandra 31

U Uribe-Toril, Juan 127

I Ieva, Marco 57

V Varga, Ákos 31 Veloso, Mónica 134

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. C. Gázquez-Abad et al. (Eds.): NB&PL 2023, SPBE, pp. 151–152, 2023. https://doi.org/10.1007/978-3-031-32894-7

152

Author Index

Villavicencio, María 83 Vizuete-Luciano, Emili 100

Y Yang, I.-Hsuan

65

W Willems, Kim 89

Z Ziliani, Cristina

57