Smart Business: Technology and Data Enabled Innovative Business Models and Practices: 18th Workshop on e-Business, WeB 2019, Munich, Germany, December ... Notes in Business Information Processing) 303067780X, 9783030677800

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LNBIP 403

Karl R. Lang · Jennifer Xu · Bin Zhu · Xiao Liu · Michael J. Shaw · Han Zhang · Ming Fan (Eds.)

Smart Business: Technology and Data Enabled Innovative Business Models and Practices 18th Workshop on e-Business, WeB 2019 Munich, Germany, December 14, 2019 Revised Selected Papers

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Lecture Notes in Business Information Processing Series Editors Wil van der Aalst RWTH Aachen University, Aachen, Germany John Mylopoulos University of Trento, Trento, Italy Michael Rosemann Queensland University of Technology, Brisbane, QLD, Australia Michael J. Shaw University of Illinois, Urbana-Champaign, IL, USA Clemens Szyperski Microsoft Research, Redmond, WA, USA

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More information about this series at http://www.springer.com/series/7911

Karl R. Lang Jennifer Xu Bin Zhu Xiao Liu Michael J. Shaw Han Zhang Ming Fan (Eds.) •

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Smart Business: Technology and Data Enabled Innovative Business Models and Practices 18th Workshop on e-Business, WeB 2019 Munich, Germany, December 14, 2019 Revised Selected Papers

123

Editors Karl R. Lang Baruch College New York, NY, USA

Jennifer Xu Bentley University Waltham, MA, USA

Bin Zhu Oregon State University Corvallis, OR, USA

Xiao Liu University of Utah Salt Lake City, UT, USA

Michael J. Shaw University of Illinois Champaign, IL, USA

Han Zhang Georgia Institute of Technology Atlanta, GA, USA

Ming Fan University of Washington Seattle, WA, USA

ISSN 1865-1348 ISSN 1865-1356 (electronic) Lecture Notes in Business Information Processing ISBN 978-3-030-67780-0 ISBN 978-3-030-67781-7 (eBook) https://doi.org/10.1007/978-3-030-67781-7 © Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved 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 Workshop on e-Business (WeB) is a premier annual conference on electronic business. The purpose of WeB is to provide a forum for researchers and practitioners to discuss findings, novel ideas, and lessons learned, to address major challenges, and to map out the future directions for e-Business. Since 2000, WeB has attracted valuable, novel research that addresses both the technical and organizational aspects of e-business. The 18th Annual Workshop on e-Business (WeB 2019) was held at the Munich Business School in Munich, Germany, on December 14, 2019. “Smart Business: Technology and Data Enabled Innovative Business Models and Practices” was the theme of the WeB 2019 workshop. As the pace of technology-enabled business innovations continues to accelerate, emerging technologies and new business models have not only transformed traditional e-Business firms and markets, but have also changed both the roles of stakeholders and the connections in business networks. New technologies such as business analytics, machine learning, and artificial intelligence have been successfully applied to enhance the efficiency of e-Business transactions and the effectiveness of e-Business service offerings as well as to facilitate faster and better organizational decision making. Disruptive technologies such as blockchain and videoconferencing (e.g., Zoom, Microsoft Teams, or Webex) are fundamentally changing the way in which business is being conducted, moving further towards automated, distributed, and smarter business arrangements that are increasingly conducted online in virtual settings. At the same time, new organizational market forms like the sharing economy are transforming entire industries at a global scale. All these transformational technological and economic forces, in turn, are also re-shaping the ecosystem of e-Business WeB 2019 provided an opportunity for academic scholars and practitioners from around the world to exchange ideas and share research findings related to these topics. The articles presented at the workshop cover a broad range of issues from the perspectives of consumers, technology users, business and management, industries, and governments using multiple perspectives, including technical, managerial, economic, and strategic thinking viewpoints. They employ various business research methods such as surveys, analytical modeling, experiments, computational models, data science, and design science. Among the 42 papers presented at WeB 2019, we selected 20 of them through a peer-review process for publication in this LNBIP volume. We are grateful to all the reviewers for providing insightful feedback to the authors and completing their review

vi

Preface

assignments on time despite tight deadlines. And, of course, special thanks to the authors for their contributions. December, 2020

Karl R. Lang Jennifer Xu Bin Zhu Xiao Liu Michael J. Shaw Han Zhang Ming Fan

Organization

Honorary Chairs Hsinchun Chen Andrew B. Whinston

University of Arizona, USA University of Texas at Austin, USA

Conference Chair Michael J. Shaw

University of Illinois at Urbana-Champaign, USA

Organizing Co-chairs Karl R. Lang Bin Zhu Jennifer Xu Xiao Liu Ming Fan Han Zhang

City University of New York, USA Oregon State University, USA Bentley University, USA University of Utah, USA University of Washington, USA Georgia Institute of Technology, USA

Local Organizing Committee Chairs Antonia Köster Heiko Seif

University of Potsdam, Germany Munich Business School, Germany

Program Committee Reza Alibakhshi Hsin-Lu Chang Michael Chau Cheng Chen Ching-Chin Chern Muller Cheung Huihui Chi Honghui Deng Aidan Duane Samuel Fosso Wencui Han Lin Hao Yuheng Hu Jinghua Huang Seongmin Jeon

HEC Paris, France National Chengchi University, Taiwan University of Hong Kong, Hong Kong SAR University of Illinois at Chicago, USA National Taiwan University, Taiwan Hong Kong University of Science and Technology, Hong Kong SAR ESCP Europe at Paris, France University of Nevada, Las Vegas, USA Waterford Institute of Technology, Ireland Toulouse Business School, France University of Illinois at Urbana-Champaign, USA University of Washington at Seattle, USA University of Illinois at Urbana-Champaign, USA Tsinghua University, China Gachon University, South Korea

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Organization

Chunghan Kang Dan Ke Anthony Lee Chenwei Li Hongxiu Li Mengxiang Li Shengli Li Xitong Li Margaret Luo Xiaopeng Luo Xin Luo Nirup Menon Jae-Hong Park Selwyn Piramuthu Liangfei Qiu Pouya Rahmati Raghu Santanam Ben Shao Yufei Shen Riyaz Sikora Chandra Subramaniam Vijayan Sugumaran Yinliang Tan James Thong Kai Wang Lizhen Xu Ling Xue Yuan Xue Dezhi Yin Wei Zhang Kevin Zhao Wenqi Zhou Wei Zhou

Georgia Institute of Technology, USA Wuhan University, China National Taiwan University, Taiwan University of Hong Kong, Hong Kong Tampere University, Finland Hong Kong Baptist University, Hong Kong SAR Peking University, China HEC Paris, France National Chung Cheng University, Taiwan Georgia Institute of Technology, USA University of New Mexico at Albuquerque, USA George Mason University, USA Kyung Hee University, South Korea University of Florida, USA University of Florida, USA University of Georgia, USA Arizona State University, USA Arizona State University, USA HEC Paris, France University of Texas at Arlington, USA University of North Carolina at Charlotte, USA Oakland University, USA Tulane University, USA Hong Kong University of Science and Technology, Hong Kong SAR National University of Kaohsiung, Taiwan Georgia Institute of Technology, USA Georgia State University, USA Pennsylvania State University, USA University of South Florida, USA University of Massachusetts at Boston, USA University of North Carolina at Charlotte, USA Duquesne University, USA ESCP Europe at Paris, France

Contents

Crowdfunding and Blockchain How Social Networks Dynamics can Affect Collaborative Decision Making on Crowdfunding Platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yanni Hu and Karl Lang

3

Go in the Opposite Direction? The Impact of Unavailability on Crowdfunding Success . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wanghongyu Wei and Michael Chau

18

The Impact of Blockchain on Medical Tourism. . . . . . . . . . . . . . . . . . . . . . Abderahman Rejeb, John G. Keogh, and Horst Treiblmaier

29

Business Analytics Creating a Data Factory for Data Products . . . . . . . . . . . . . . . . . . . . . . . . . Chris Schlueter Langdon and Riyaz Sikora

43

An Empirical Investigation of Analytics Capabilities in the Supply Chain . . . Thiagarajan Ramakrishnan, Abhishek Kathuria, and Jiban Khuntia

56

Finding Real-Life Doppelgangers on Campus with MTCNN and CNN-Based Face Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jingjing Ye and Yilu Zhou Time Series Analysis of Open Source Projects Popularity . . . . . . . . . . . . . . Shahab Bayati and Marzieh Heidary

64 77

Digital Platforms and Social Media Social Media or Website? Research on Online Advertising Type Based on Evolutionary Game . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiang He, Li Li, Hua Zhang, and Xingzhen Zhu

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Platform Discount Deciding, Seller Pricing and Advertising Investment in the Shopping Festival Based on Two-Sided Market Theory . . . . . . . . . . . Hua Zhang, Li Li, Xiang He, and Xingzhen Zhu

103

Who Picks Cherries? Understanding Consumers’ Cherry Picking Behavior in Online Music Streaming Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Changkeun Kim, Byungjoon Yoo, and Jaehwan Lee

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Contents

The Value of Free Content on Social Media: Evidence from Equity Research Platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tianyou Hu, Arvind Tripathi, and Henk Berkman

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Managing e-Business Projects and Processes Managing Cloud Computing Across the Product Lifecycle: Development of a Conceptual Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Timo Puschkasch and David Wagner

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Antecedents of Different Social Network Structures on Open Source Projects Popularity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shahab Bayati and Arvind Tripathi

143

Language Alternation in Online Communication with Misinformation . . . . . . Lina Zhou, Jaewan Lim, Hamad Alsaleh, Jieyu Wang, and Dongsong Zhang

158

A Taxonomy of User-Generated Content (UGC) Applications . . . . . . . . . . . Tien T. T. Nguyen and Arvind Tripathi

169

Global e-Business Influence of Ownership and Management on IT Investment in Indian Family Firms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xue Ning, Prasanna Karhade, Abhishek Kathuria, and Jiban Khuntia

185

Controlling Risk from Design Changes in Chinese Prefabricated Construction Projects: An Empirical Investigation . . . . . . . . . . . . . . . . . . . . Juan Du, Jiajun Zhang, Yifei Gu, and Vijayan Sugumaran

194

AHP-FCE Evaluation of Cross-Border e-Commerce Supply Chain Performance for Xi’an International Inland Port . . . . . . . . . . . . . . . . . . . . . Guo-Ling Jia

215

Knowledge Domain and Emerging Trends in Cross-Border E-commerce Coordination Mechanism Based on CiteSpace Analysis . . . . . . . . . . . . . . . . Shan Du and Hua Li

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Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Crowdfunding and Blockchain

How Social Networks Dynamics can Affect Collaborative Decision Making on Crowdfunding Platforms Yanni Hu(B)

and Karl Lang(B)

Baruch College, 55 Lexington Ave., New York, NY 10010, USA {yanni.hu,karl.lang}@baruch.cuny.edu

Abstract. Despite the increasing phenomena that social interactions among contributors by emerging technologies influence crowdfunding decision making, little is known about how social network dynamics formed by these social interactions affect contributors’ decision making. Drawing on a data set collected from an economic experiment conducted on Amazon Mechanical Turk (MTurk), we use a social network approach to investigate the effects of social network structure on collaborative decision making under a crowdfunding setting. Comparing four standard network structures – null, star, weak ties, mesh - Our analysis shows that the mesh network yields the best group collaboration performance, with social information displayed. The result of this research provides a specific and nuanced angle of the importance of social networks in emerging technology – enabled online crowdfunding. Keywords: Crowdfunding decision making · Social network structure · Social information

1 Introduction Crowdfunding is collaborative work - a group of people make mutual effort in reaching a fundraising goal. Contributors or backers at crowdfunding platforms always refer to each other’s crowdfunding decisions when they make their own crowdfunding decisions. The Internet has offered entrepreneurs and contributors a new interaction and influential channel to support projects by social network and social information sharing Recent empirical studies have investigated the impact of crowdfunding platform design factors on contribution behavior [27], but the effects of social network on contributors’ behavior have received less attention. Social network structure is of special relevance with backers’ decision making if we interpret crowdfunding platforms as networks of interactions among backers and project creators. For instance, many crowdfunding sites have facilitated social media tools (Facebook and Twitter sharing) that promote social interactions. Through these interactions, contributors at crowdfunding platforms can refer to, learn from, and cooperate with each other’s contribution decision making to reach the fundraising goal. As a crowdfunding project is a strategic campaign that requires © Springer Nature Switzerland AG 2020 K. R. Lang et al. (Eds.): WeB 2019, LNBIP 403, pp. 3–17, 2020. https://doi.org/10.1007/978-3-030-67781-7_1

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Y. Hu and K. Lang

backers’ mutual support and collaboration to reach a fundraising goal, it is essential to understand how different network structures help with social interaction that may influence contributors’ decisions to reach the fundraising goal. Social network structure is defined as the presence of regular relationship patterns within a single social network [25, 26]. Under online settings, social network structure typically applies to the pattern in which people interact with people by many IT artifacts. Previous literature from economics, finance, and information systems recognize social network structure relates to behaviors and well-being of people in a society. Many view social network structures as information-sharing channel which influence the economic preferences and consequences [5, 17, 20]. For instance, Allen et al. [1] examine the effects of social interaction on P2P lending and find socially connected areas with more Facebook’s friendship linkages have more lending activities. In crowdfunding related settings, Thies et al. [24] indicate that social networks through social media such as Facebook sharing have positive effect on backers’ funding decision. Suri and Watts [23] studied network structures on cooperation behavior and find that people conditionally cooperate in response to their neighbor’s decision. Fowler and Christakis [14] implements s series of one-shot public goods experiments and find that cooperation can cascade across three degrees of separation in a network. These findings provide primary motivation for this study. However, a more nuanced investigation of how individuals react to different social networks via social interaction and thus cooperate with each other during the crowdfunding is needed. Motivated by the practical but complex effect of social network structure on crowdfunding behaviors, in this research we examine typical social network typologies on crowdfunding contribution behavior and believe that this assessment is crucial in order to understand whether and which network structure affect contributions behavior most. In addition, there is a clear need to examine how cooperation and collaboration behavior involve and evolve in different network structures as information dissemination channels. Thus, this study attempts to answer the following research question: How does social network structure affect contributors group collaboration performance on a crowdfunding platforms? We design an experimental crowdfunding platform deployed on Amazon Turk that enables us to manipulate social network structure and configure four standard types with different degrees of connectedness: null, weak-tie, star, and mesh network (see Table 1). We also manipulate a second variable of interest, social information, which refers to participant-specific game information shared in the network. We conduct a series of experiments on Amazon Mechanical Turk (MTurk) by inviting online individuals play a fundraising game arranged on 4 typical typologies of social networks. Data were collected from MTurk workers as participants and analyzed with one-way ANOVA and regression methodology.

How Social Networks Dynamics can Affect Collaborative Decision

5

Table 1. Network structure typology and centrality measures

Network structure typology and centrality measures Null

Weak-tie

Star

Mesh

Networks in 7 points Measure: Community and Social Identity

Closeness Centrality: CC ' (Pk)

Social Identity

Null

Connectedness

Betweeness

and So-

Centrality: CB '

cial Distance Social Distance

***** ** (Invalid)

(Pk)

***** ** (Invalid) Mesh

0.10, 0.09, 0.09, 0.07, 0.07, 0.07, 0.07 (Average: 0.08) < Weak tie 9,8,8,0,0,0,0 (Average: 3.57)

< Weak tie

0.8 # of images with cosine score > 0.9

1,218 5

0.01% of total sample discarded

Fig. 7. Pictures from the same student with cosine similarity score of 0.905

We then manually checked the 1,218 pairs of images and group them into two categories: pairs that are highly alike, or doppelgangers; and pairs that are somewhat alike, not real doppelgangers. The results are concluded to Fig. 8. However, we did not perform a formal tagging to generate a gold standard from the dataset. This manual check was explorative. Figure 9 shows 6 pairs of top-ranked face photos with cosine similarity greater than 0.8 and are judged by human as highly alike pairs. These student pairs are considered true doppelgangers in our study. We plan to organize a photo shooting session with these students to mimic Brunel photography’s exhibition. For the somewhat alike but not real doppelganger group, we further investigated the possible reasons of their high cosine similarity score. We identified the following areas that could cause high cosine similarity while the two faces are not highly alike.

Finding Real-Life Doppelgangers on Campus with MTCNN and CNN Similarity > 0.9 Cosine Similarity Score

73

same person

0.8 < Similarity < 0.9

highly alike person

True Doppelgangers

0.8 < Similarity < 0.9

somewhat alike person

Alike but not Doppelgangers

Fig. 8. Doppelganger identification

Fig. 9. Doppelgangers on campus: top-ranked face pairs (0.7< cosine similarity k2 w. Based on the theory of declining marginal benefits, advertisers would pay 21 (αq)2 for website advertising, 21 (βq)2 for social media advertising. Since αq < βq, therefore 1 1 2 2 2 (αq) < 2 (βq) . In addition, associated with the classic advertising investment model reputational model [20], it could be seen that if advertiser needs to pay 21 q2 on advertising, √

the benefit is going to be p + 22 λq, in detail, λ refers to the browsing probability of the channel. To conclude, the√benefit of website advertising and social media advertising √ 2 2 are represented with p + 2 λ1 αq and p + 2 λ2 βq. In summary, the benefit matrix of consumers and advertisers is shown in Table 1. Table 1. Consumers and advertisers payoff matrix

Probability Browse (B1 )

Website advertising A1

Social media advertising A2

Y

1−Y

X

(q + wk1 − αq, √

p + 22 λ1 αq − 21 (αq)2

Do not browse (B2 )

1−X



0, − 21 (αq)2





(q + wk2 − βq, √

p + 22 λ2 βq − 21 (βq)2   0, − 21 (βq)2



Social Media or Website? Research on Online Advertising Type

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4 Equilibrium Analyses According to Table 1, the expected benefit of two strategies that consumers choose to ‘browse’ and ‘do not browse’ as well as the average benefit of the consumer groups are: UB1 = Y(q + wk1 − αq) + (1 − Y)(q + wk2 − βq) = q + wk2 − βq + Y(βq − αq + wk1 − wk2 )

(1)

UB2 = 0

(2)

  B¯ = XUB1 + (1 − X)UB2 = X q + wk2 − βq + Y(βq − αq + wk1 − wk2 )

(3)

Then the replicator dynamics equation for the consumer to select the ‘browse’ strategy is: F(x) =

    dX = X UB3 − B¯ = X(1 − X) q + wk2 − βq + Y(βq − αq + wk1 − wk2 ) dt (4)

Similarly, the expected benefit of two strategies of the advertisers as well as the average benefit of the advertiser group is:   √ √

 1 1 2 2 2 2 λ1 αq + (1 − X) − (αq) = − (αq) + X p + λ1 αq UA1 = X p + 2 2 2 2 (5)   √ √

 1 1 2 2 UA2 = X p + λ2 βq + (1 − X) − (βq)2 = − (βq)2 + X p + λ2 βq 2 2 2 2 (6)   √ √ 1 2 2 1 A¯ = Y − (αq)2 + X p + λ1 αq λ2 βq + (1 − Y) − (βq)2 + X p + 2 2 2 2 (7) Then the advertiser chooses the replicator dynamics equation of ‘social media advertising’ as: 

√ √ dY 1 2 2 1 2 2 F(Y) = βq − αq + (αq) − (βq) = Y (1 − Y ) X λ2 p − λ1 p + dt 2 2 2 2 (8) According to the replication dynamic system, the dynamic equilibrium solution is derived from the following equation:  F(x) = 0 (9) F(Y) = 0

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X. He et al.

When F(x) = 0, it is solved that X = 0, X = 1, Y = Y ∗ , among them, Y ∗ = −(q+wk2 −βq) ∗ βq−αq+wk1 −wk2 ; when F(Y) = 0, it is solved that Y = 0, Y = 1, X = X , among them, X ∗ =

1 2

2 (βq)2 −(αq) √ 2 2 2 βqλ2 − 2 αqλ1

√

. Therefore, the five equilibrium points in Eq. (1) are

solved as O (0, 0); A (0, 1); B (1, 1); C (1, 0); D (X ∗ , Y ∗ ). According to the replicator dynamics equations of consumers and advertisers, this paper describes the replicator dynamics diagram about consumers and advertisers, as shown in Fig. 1 and Fig. 2.

Fig. 1. Consumers replicator dynamics diagram

Fig. 2. Advertisers replicator dynamics diagram

Proposition 1. In the dynamic market, advertisers choose to invest social media advertising and consumers choose to browse, which are the evolutionary stability strategies. The evolution process is shown in Fig. 3. As shown in Table 2, (1, 0) and (0, 1) belong to stable points among 5 equalization points, (0, 0), (1, 1) and (X ∗ , Y ∗ ) belong to unstable points. In details, (1, 0) indicates that consumers browse social media advertisings; (0, 1) indicates that consumers do not browse the website advertisings. To achieve the biggest benefit, advertisers are more likely to invest social media advertisings as the final evolutionary stability strategy. Figure 4 depicts the evolution process of consumers and advertisers. Assuming that D is the initial point, the area of quadrilateral AODB indicates the probability that the initial point finally stabilized at (1, 0). Similarity, the area of quadrilateral CODB indicates the probability that initial point will eventually stabilized at (0, 1).

Social Media or Website? Research on Online Advertising Type

Fig. 3. Evolution stability legend

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Fig. 4. Evolution of process by consumers and advertisers

Table 2. Stability analysis of equilibrium points Equilibrium point

Det(J)

Tr(J)

Det(J)* Tr(J)

Stable or not

O (0, 0)

0

>0

Unstable

C (1, 0)   D X ∗, Y ∗

>0

0.90

>0.8

0.901

Good

PNFI

>0.50

0.718

Good

In order to further refine operation results and analyze confirmatory factors, detailed parameter estimation is shown in Table 6. Table 6. Parameter estimation of confirmatory factor. Variable

Estimate

Variable

Estimate

A1