Lean Six Sigma for Higher Education [1 ed.] 9781839094217, 9781839094200

Citation preview

ISSN 2040-4166 Volume 10 Number 4 2019

International Journal of

Lean Six Sigma

Lean Six Sigma for higher education Guest Editors: Jiju Antony, Chad Matthew Laux and Beth Cudney

Guest editorial

Guest editorial

1. Lean Six Sigma in higher education institutions: an idea that has arrived? 1.1 Introduction Lean Six Sigma has become predominate in many fields. It is among the most common continuous improvement methodologies today. Yet, Lean Six Sigma (LSS) has yet to penetrate a variety of endeavors, such as the public sector (Elias et al., 2018). In a previous issue of the International Journal of Lean Six Sigma (IJLSS), the topic of LSS adoption in the public sector was explored and while LSS is in its formative stages in public institutions, both empirical and theory-based studies are starting to be researched and disseminated (Elias et al., 2018). While higher education institutions, or HEIs, are not exclusively public, they typically share a mission: a statement of purpose of whom HEIs serve, why they exist, and grounded in community. However, education, and in particular higher education, has come under pressure from a variety of factors, where resources are a basis for much of that stress. And while other industries, namely, manufacturing, service and transportation, have adopted LSS to improve operations and focus on efficiency and effectiveness, HEIs have largely been impervious to such continuous improvement efforts (Antony et al., 2012). Indeed, considering the challenges that HEIs face, is it a time for LSS to finally arrive? 1.2 The challenge of higher education institution’s HEIs play a critical role in our society. Since the establishment of early technical colleges to today’s industrialized “Knowledge enterprise,” institutions of higher education have been a corner stone in educating society’s leaders, an incubator for advanced technologies, and an accelerator for economic development (Lu et al., 2017). Like other institutions today, higher education finds itself under stress today. In the United States, revenue for what are called “state” supported institutions, characterized by research intensity and liberal education, has crossed a new dangerous threshold: the majority of revenue now comes in the form of student tuition, rather than state support (Brownstein, 2018). This shift of support from the public to private domain has resulted in dramatic increases of student debt; at over $1.4tn and counting, is larger than the mortgage debt during the Great Recession (Daniels, 2018). Elsewhere, the picture looks little different: Horizon 2020, the EU’s financial instrument for aimed at securing Europe’s global competitiveness, remains seriously underfunding with regard to higher education support (EUA, 2019; European Commission, 2019; Highman, 2019). In China, governmental regulation on tuition has not been able to lower college tuition to the level that the public is willing to accept (Zeng, 2009). Things look similar elsewhere: in South Africa, and in East Africa in general, revenue “supplementation”, or shifting of costs away from public support, has had mixed success (Johnstone, 2004). The higher education environment is not only defined by cost or resources. However, cost is a useful representation for value, a theme of the research in this issue. In this special issue of the IJLSS, we focus on higher education and value of HEIs create, maintain, and deliver it in the modern age. The authors of these studies focus their attention a varying aspects of higher education and LSS and review the current literature to establish the state of the art of LSS in HEIs. We briefly discuss the eight articles in this special issue and highlight some future research directions.

857

International Journal of Lean Six Sigma Vol. 10 No. 4, 2019 pp. 857-861 © Emerald Publishing Limited 2040-4166 DOI 10.1108/IJLSS-10-2019-152

IJLSS 10,4

858

2. Overview of higher education institutions and Lean Six Sigma 2.1 Empirical and theoretical studies There have been some studies of Lean Six Sigma in higher education and we discuss a few here. For a start, the variety of research of LSS/HEIs reflects the variety found within HEIs. For example, while most individuals who attended college or university may recognize the teaching mission and perhaps as researchers as well, like other organizations, HEIs also carry a number of business practices. Indeed, the multiple missions of teaching, research and engagement brings into the HEI mission a variety of practices, stakeholders, in the HEI environment. The aspect of variety reflects the state of the art of research of LSS in higher education. Some researchers focus on the institution systematically. In Antony et al.’s (2012) study, the authors critically assess evaluate LSS’s potential for improving HEI business processes and present and success factors for successful LSS adoption. Leading from the top is critical to LSS success. In a study, Lu et al. (2017) describe a framework for leadership LSS in HEIs. Beyond leading the LSS effort, strategy is required for successful adoption. In a study by Antony (2014), the author describes readiness factors required for introducing a LSS initiative in an HEI context. The development of LSS from a historical perspective reveals potential opportunities for LSS in HEIs is the focus of a theoretical study by Hess and Benjamin (2015). Jenicke et al. (2008) describe the unique aspects of HEIs vs. other settings. In addition, the authors examine the challenges of LSS in academia and propose a guiding LSS-HEI framework (Jenicke et al., 2008). In a follow-up study, Holmes et al. (2015) a framework for LSS project adoption and present a weighted scorecard approach for project selection.

2.2 Case studies To date, LSS has been adopted in a number of HEIs. In a study by Svensson et al. (2015) presents a case study of LSS adoption within King Abdullah university. Another case of LSS adoption comes from an Irish university where improvements to the business operations and the importance of leadership and management of a LSS methodology (O’Reilly et al., 2019). Finally, Vijaya Sunder (2016) presents a case study that notes the value of LSS adoption in an HEI university libraries process that follows the DMAIC approach. In summary, there have been some LSS research studies in HEIs, primarily concerning theoretical and empirical areas. We believe this demonstrates that the LSS research in HEIs is in a nascent stage.

3. Overview of the articles in the special issue The articles in this issue provide more direction of LSS research in this unique field and greatly add to the body of knowledge through a variety of LSS applied research studies.

3.1 Attributes valued by students in higher education services: a lean perspective For instance, Petrusch and Vaccaro discuss the value of academic administrative services, as perceived by students and find a relationship among lean, service, and HEI amenity in their evidence based approach. The student perspective is one that is rarely adopted among the field as the student-customer idea, to put it succinctly, may vary among academic opinions. Regardless, a voice of the customer analysis of students is welcome indeed.

3.2 How to use Lean Six Sigma methodology to improve service process in higher education: a case study In a case study by Li, Laux and Antony, the authors describe the business process of award preparation to support academicss grant submission process in HEIs, a function that is crucial to research intensive universities, generalizable to other research intensive environments and describes the details of how DMAIC may applied in this LSS for Service approach. In addition, this was a student led project, based upon a graduate level LSS course. 3.3 Implementing Lean Six Sigma and discrete-event simulation for tutoring operations in higher education institutions Furterer, Schneider, Key, Zalewski, and Laudenberger bring us another LSS application in a case study of implementing LSS for student tutoring, an important, but very labor-intensive operation in HEIs and usually a source of consternation for both students and faculty. The ability to model the process may help other units apply this approach broadly. 3.4 A Lean Six Sigma approach for improving university campus office moves Wheeler-Webb and Furterer bring another case of applying LSS toward HEI space allocation and add to the literature through an area of HEI costs through applying this DMAIC approach to similar fixed asset processes, an area focus for sustaining the university mission. 3.5 Lean Six Sigma in higher education institutes: an Irish case study In O’Reilly, Healy, Murphy and Ó’Dubhaills’ case study, the authors describe key understandings to guide others in implementing LSS in HEIs through this longitudinal study of LSS adoption in an Irish institution. This work adds to the literature through a number of administrative improvements highlighted within. 3.6 Kaizen in university teaching: continuous course improvement Kregel introduces a novel study of applying a LSS technique, Kaizen, to the teaching function in university. Many academic programs follow standards and Kregel’s study can support the continuous improvement challenges of accreditation that many departments, and their representative faculty, confront (to use a kind word) in their academic field. 3.7 Applying Lean Six Sigma to grading process improvement The theme of pedagogy continues in Oliver, Oliver and Chen’s study of how to apply LSS methods in improving the student grading process, likely every faculty’s favorite activity. Take note readers, the time savings noted should interest any representative faculty. 3.8 Evaluating university leadership performance using Lean Six Sigma framework Not least, Tetteh conducts a critical evaluation of leadership performance in HEIs using a LSS framework. In this empirical work, Tetteh surveys what students perceive, and value, as performance management (PM) and finds a relationship between LSS and PM. Let’s not forget about the students! 4. Conclusions and future concerns HEIs provide a fundamental role in many societies. The globalization trends that have been occurring over the past 50 years do not show evidence of slowing down anytime soon. In

Guest editorial

859

IJLSS 10,4

860

1998, UNESCO identified a number of trends that have only accelerated in the last decade (Altbach et al., 2009). Higher education has become a competitive enterprise, with the characteristics of an organization that must compete for scarcity, as students replace funding from state resources (Altbach et al., 2009). The report goes on to state that as universities compete for status and rank, the competitive nature can contribute toward a decline in sense of academic mission, community, and values (Altbach et al., 2009). The ability to maintain the academy requires effort from a variety of resources, disciplines and ideas as the commercialization of higher education strains the social mission (Altbach et al., 2009). LSS is one contributive effort that can impact these trends of massification of the academy and that we hope the literature presented in this work will support a concerted effort to respond to the concern for quality in higher education. Jiju Antony

School of Management and Languages, Heriot-Watt University, Edinburgh, UK Chad Matthew Laux Department of Technology Leadership and Innovation, Purdue University, West Lafayette, Indiana, USA, and Beth Cudney Department of Engineering Management and Systems Engineering, Missouri University of Science and Technology, Rolla, Missouri, USA References Altbach, P.G., Reisberg, L. and Rumbley, L.E. (2009), “Trends in global higher education: tracking an academic revolution”, A Report Prepared for the UNESCO 2009 World Conference on Higher Education. Antony, J. (2014), “Readiness factors for the lean six sigma journey in the higher education sector”, International Journal of Productivity and Performance Management, Vol. 63 No. 2, pp. 257-264, doi: 10.1108/IJPPM-04-2013-0077. Antony, J., Krishan, N., Cullen, D. and Kumar, M. (2012), “Lean six sigma for higher education institutions (HEIs): challenges, barriers, success factors, tools/techniques”, International Journal of Productivity and Performance Management, Vol. 61 No. 8, pp. 940-948, doi: 10.1108/ 17410401211277165. Brownstein, R. (2018), “American higher education hits a dangerous milestone”, The Atlantic, available at: www.theatlantic.com/politics/archive/2018/05/american-higher-education-hits-a-dangerousmilestone/559457/ Daniels, M. (2018), “Health care isn’t our only ludicrously expensive industry”, The Washington Post, available at: www.washingtonpost.com/opinions/health-care-isnt-our-only-ludicrously-expensiveindustry/2018/02/06/6ce64d28-0629-11e8-b48c-b07fea957bd5_story.html Elias, A.A., Pepper, M. and Sloan, T. (2018), “Editorial”, International Journal of Lean Six Sigma, Vol. 9 No. 2, pp. 178-184, doi: 10.1108/IJLSS-06-2018-132. EUA (2019), “Sufficient, sustainable, and simple EU funding for universities”, available at: https://eua. eu/resources/campaigns/8-eu-funding-for-universities.html (accessed 1 October 2019). European Commission (2019), “What is horizon 2020?”, available at: https://ec.europa.eu/programmes/ horizon2020/what-horizon-2020 (accessed 1 October 2019). Hess, J.D. and Benjamin, B.A. (2015), “Applying lean six sigma within the university: opportunities for process improvement and cultural change”, International Journal of Lean Six Sigma, Vol. 6 No. 3, pp. 249-262, doi: 10.1108/IJLSS-12-2014-0036.

Highman, L. (2019), “Future EU-UK research and higher education cooperation at risk: what is at stake? ”, Tertiary Education and Management, Vol. 25 No. 1, pp. 45-52, doi: 10.1007/s11233018-09013-w. Holmes, M.C., Jenicke, L.O. and Hempel, J.L. (2015), “A framework for six sigma project selection in higher educational institutions, using a weighted scorecard approach”, Quality Assurance in Education, Vol. 23 No. 1, pp. 30-46, doi: 10.1108/QAE-04-2014-0014. Jenicke, L.O., Kumar, A. and Holmes, M.C. (2008), “A framework for applying six sigma improvement methodology in an academic environment”, The TQM Journal, Vol. 20 No. 5, pp. 453-462, doi: 10.1108/17542730810898421. Johnstone, D.B. (2004), “Higher education finance and accessibility: tuition fees and student loans in Sub-Saharan”, Journal of Higher Education in Africa JHEA/RESA, Vol. 2 No. 2, pp. 11-3611, doi: 10.2307/24486232. Lu, J., Laux, C. and Antony, J. (2017), “Lean six sigma leadership in higher education institutions”, International Journal of Productivity and Performance Management, Vol. 66 No. 5, doi: 10.1108/ IJPPM-09-2016-0195. O’Reilly, S.J., Healy, J., Murphy, T. and Ó’Dubhghaill, R. (2019), “Lean six sigma in higher education institutes: an Irish case study”, International Journal of Lean Six Sigma, doi: 10.1108/IJLSS-082018-0088. Svensson, C., Antony, J., Ba-Essa, M., Bakhsh, M. and Albliwi, S. (2015), “A lean six sigma program in higher education”, International Journal of Quality and Reliability Management, Vol. 32 No. 9, pp. 951-969, doi: 10.1108/IJQRM-09-2014-0141. Vijaya Sunder, M. (2016), “Lean six sigma in higher education institutions”, International Journal of Quality and Service Sciences, Vol. 8 No. 2, pp. 159-178, doi: 10.1108/IJQSS-04-2015-0043. Zeng, X. (2009), “The goals for regulating college tuition”, Frontiers of Education in China, Vol. 4 No. 2, pp. 175-187, doi: 10.1007/s11516-009-0011-4.

Guest editorial

861

The current issue and full text archive of this journal is available on Emerald Insight at: www.emeraldinsight.com/2040-4166.htm

IJLSS 10,4

Attributes valued by students in higher education services: a lean perspective

862

Anete Petrusch and Guilherme Luís Roehe Vaccaro Universidade do Vale do Rio dos Sinos, Sao Leopoldo, Brazil

Received 3 July 2018 Revised 19 January 2019 17 April 2019 Accepted 8 May 2019

Abstract Purpose – The purpose of this paper is to use theoretical and field evidence to discuss what the valueattributes for academic-administrative services as perceived by students in higher education institutions (HEIs) and how such organizations deliver them. An emerging framework relating value-attributes for HEIs’ administrative and academic services is presented from the perspective of students. Design/methodology/approach – Focus group analysis with Brazilian HEI’s students supported this study. Extensive theoretical references from lean services and services theory contribute to building an emerging framework that extends the background on the subject. Findings – The following framework of eight value-attributes for administrative services in HEIs were studied: reliability, empathy, access, responsiveness, self-service technology convenience, communication, personalization and imperceptibility. The value-attributes may receive different degrees of prioritization and improvement effort according to the type of service and strategic positioning of the organization. Research limitations/implications – Field evidence is limited by the extent of students and organizations accessed. Implications include directing future research to produce a quantitatively validated model and as an emerging framework, to support decision-planning in the context of HEIs. Originality/value – The study extends the literature relating the connection between lean services, services theory and higher education services. No similar study has been found in Brazilian HEIs.

Keywords Higher education service, Lean service, Student service evaluation, Value-attributes Paper type Research paper

International Journal of Lean Six Sigma Vol. 10 No. 4, 2019 pp. 862-882 © Emerald Publishing Limited 2040-4166 DOI 10.1108/IJLSS-07-2018-0062

1. Introduction Depending upon the educational system adopted by each country, the higher education system may be public, private or mixed. At least for the latter two systems, there is a competitive market in which a growing number of players compel higher education institutions (HEIs) to strive for students and funds to ensure their sustainability. The private higher education sector is growing globally, especially in Latin America and Asia (Kinser, 2010). In such a scenario, HEIs need to increase efficiency and to provide new or increased value through services that can differentiate them from their competitors (Olivares and Wetzely, 2014). HEIs face pressure to be more cost-effective and customer-driven (Isaksson et al., 2015). HEIs are complex organizations comprising “thousands of business processes” to support and facilitate their primary functions of research, education and innovation (Svensson et al., 2015, p. 953). These processes could be much more efficient and effective if many forms of waste were eliminated (Douglas et al., 2015). HEIs are increasingly aware of the necessity to improve their processes with better quality (Sunder, 2014). Lean management can promote the necessary HEIs’ process improvements because it emphasizes value for the customer, efficiency, effectiveness, savings, sustainability,

performance, and quality (Balzer, 2010). The lean approach has shown positive outcomes (Comm and Mathaisel, 2005; Balzer, 2010; Taylor, 2012). Strong evidence of using lean principles to improve a number of processes provided by different functional areas of HEIs may be found (Balzer, 2010; Balzer et al., 2016). Evidence that has arisen from case studies indicates that lean management provides high-quality education, cost-reduction, value maximization, and respect for the long-term interest of students, faculties and staff (Thirkell and Ashman, 2014). Evidence for the adoption of process improvement methodologies such as lean and lean six-sigma in HEIs is growing, though the reports still refer to earlier stages (Sunder, 2016a). To deliver value to the customer is a fundamental driving concept of lean (Womack and Jones, 1996, 2005). However, even being a well-addressed concept in the marketing of goods and services (Ostrom et al., 2010), it still lacks better understanding in the context of HEIs (Hines and Lethbridge, 2008). One concern is the definition of value-attributes. That is, what exactly do students value when looking for HEIs’ services? This paper presents a framework relating value-attributes for HEIs’ administrative and academic services in the perspective of students from private Brazilian HEIs, a market mobilizing over 6 million students (INEP, 2017). Results were obtained through focus group interviews and content analysis, resulting on eight value-attributes: reliability, empathy, access, responsiveness, self-service technology convenience (SSTC), communication, personalization and imperceptibility. Each attribute was then conceptually analyzed, considering the literature on this subject. The remainder of this paper is organized as follows: Section 2 presents key concepts sustaining the research, and this is followed by the research method described in Section 3. Results follow, accompanied by the analysis in Section 4 and discussion in Section 5. Finally, Section 6 addresses key remarks, limitations and future research opportunities. 2. Background 2.1 Lean approaches to services in higher education institutions and value to the customer In the context of HEIs, lean approaches can contribute to sustainability through waste reduction and increased efficiency (Balzer et al., 2015), if well implemented and focused on delivering effective value to the customer (Comm and Mathaisel, 2005). Lean may bring a philosophic framework associated with principles and tools still unusual to HEIs that may be able to improve their value delivery effectiveness (Balzer, 2010). Despite that, in HEIs, it is still necessary to emphasize that lean is much more of a value-adding than a cost-cutting framework (Thomas et al., 2015). Value, for a lean approach in higher education, means to meet or exceed the customer’s requirements and expectations in a profitable way. Cost savings come from reducing waste. To eliminate waste provides real value to the customer (Radnor and Bucci, 2011). Overall, difficulties of accepting lean approaches in the context of HEIs are partially related to the acceptance of the term “customer” (Doman, 2011; Hines and Lethbridge, 2008; Taylor, 2012). To define the customer is problematic (Douglas, Antony and Douglas, 2015). From one perspective, there are pedagogical implications about the relationship of such term with “student” (Eagle and Brennan, 2007). As Hess and Benjamin (2015, p. 260) observed, “few institutions openly identify their students as ‘customers’.” From another perspective, the comprehension of customer is more complex in the context of HEIs because it includes other stakeholders (Antony, 2014; Antony et al., 2012; Eagle and Brennan, 2007) and diverse and complex instances need to be considered (Sunder, 2016b). This complexity is related to the roles acted with each stakeholder, and the services expected and delivered by HEIs. As a consequence, it becomes complex to understand, which value is desired to be delivered to

Higher education services

863

IJLSS 10,4

864

each customer (Jongbloed et al., 2008). There is still a “gray area” for customer categorization, and many failures of improvements occur when customers are not clearly defined, and their demands are not correctly understood (Sunder, 2016b, p. 1104). Radnor and Bucci (2011) highlight that the concept of customer is understood and connected with better services. However, the concept of value delivered to the customer needs to be better developed because it is still associated with better processes for internal staff. Employees understand that adding value through lean processes is beneficial to them, but they still miss a link between these improvements and the perceived value to students (Radnor and Bucci, 2011; Francis, 2014). There is a consciousness about the student as a customer who might be attracted, wants value for money, and needs to be heard to maintain the university’s brand and reputation (Taylor, 2012). Although, there is still evidence that some assumptions about students’ requirements in lean improvements are taken without hearing from them (Radnor and Bucci, 2011). 2.2 Lean and service enabling conditions The effectiveness of lean in higher education services is still a subject of research. For Carlborg, Kindstrom and Kowalkowski (2013), the lack of understanding of who is the beneficiary and what is the value to be delivered leads to the undesired effect of putting effort to increase service efficiency without increasing customer satisfaction. Depending on the type of service, lean principles such as flow and standardization may lead to reduced customer satisfaction. Low demand diversity and low contact services will be most benefited from lean approaches, per these authors. To sustain such a statement, Carlborg et al. (2013) present an interpretation of the matrix of service interdependency patterns developed by Larsson and Bowen (1989). The matrix analyzes the degree of input uncertainty, associating three dimensions: (1) the customer’s disposition to participate in the service (i.e. the extent of customer’s participation in the service production process); (2) demand diversity (i.e. the specificities of demands, including adding value to goods belonging to the customer, transforming customer’s features and the different demands to be addressed); and (3) the main locus of inter-dependency among front-office, back-office and the customer. As a result, four different service designs arise: pooled, sequential customized, reciprocal and sequential standardized (Larsson and Bowen, 1989). Table I summarizes this matrix with its main characteristics.

Table I. Main characteristics of the service interdependency typology

Service design

Pooled

Sequential customized

Reciprocal

Sequential standardized

Customer disposition to participate Diversity of demand Main locus of interdependencies Standardization Service delivery

Low

Low

High

High

Low Back-office

High Front- and back-office

High Customer and front-office

Low Customer

High Back-office

Low Front- and back-office

Low Front-office

High Back-office

Source: Based on Larsson and Bowen (1989)

In this matrix, higher education services (the main service of education) are classified as a reciprocal, as they involve both high participation from the student in its production and a high diversity of demand. Nevertheless, academic-administrative services differ from that classification because of their peripheral and supporting role to the core processes in this context. Thus, they may be considered as pooled services, sequential-standardized services or sequential-customized services. To Carlborg et al. (2013) only in pooled services do lean principles increase efficiency and customer satisfaction. In the other services design, lean implementation to increase efficiency occurs at the expense of the satisfaction of the customer. Each type of service design will require an adequate approach to create the intended value through its processes. This perspective applies to the higher education context. Lean comes as a framework with principles and tools to organize and orient the processes, to highlight the value delivered to the customer, and to continuously increase efficiency and quality. 2.3 Value-attributes A customer recognizes value in a service when he is willing to accept the associated sacrifices in exchange for a more satisfying or less expendable set of benefits (Priem, 2007). For a service to endure, however, it is necessary that the customer recognizes and pays not only for the value but also for the amount associated with the costs involved in the service production (Lepak et al., 2007). In a lean perspective, this means that once the value for the customer is defined, then the operations and processes should be directed and subordinated to it (Balzer et al., 2015). In the literature surveyed for lean in higher education, all publications refer to value as a core concept. Fewer publications, however, indicate what is exactly valued by the customer – the value-attributes. Hess and Benjamin (2015) mention timeliness of earning a degree as a value for the student. Hines and Lethbridge (2008) point out timeliness, responsiveness and straightforwardness as values for the customer. Womack and Jones (2005) observed that, despite improvements in processes and the quality of products and services, reduction in failures, defects and costs, and increase in profits, the customer experience was deteriorating when empathy deviated from value. Therefore, the authors proposed a generic framework of value statements that they called “principles of lean consumption.” This framework is a useful guide to develop a preliminary statement of values and expectations to guide the improvement work in HEIs (Balzer, 2010). These six principles were expressed in the customer’s voice (Womack and Jones, 2005, p. 15): (1) solve my problem completely; (2) do not waste my time; (3) provide exactly what I want; (4) deliver value where I want it; (5) supply value when I want it; and (6) reduce the number of decisions I must make to solve my problems. Additional definitions were compiled from service theory to contribute to the discussion of value-attributes in a lean context. From that standpoint, the customer defines value from his perception of the quality of the service (Cronin et al., 2000). To measure the quality of service, the most common and widely used instrument is the SERVQUAL (Ladhari, 2009). Parasuraman et al. (1988) structured this instrument by measuring five

Higher education services

865

IJLSS 10,4

866

dimensions: reliability, responsiveness, assurance, empathy and tangibles. The starting point of this instrument came from 10 determinants of service quality established previously by the same authors (1985). These five dimensions, and therefore, the 10 determinants may be assumed to be value-attributes, as they can represent the perceptions of the customers about the quality of service. The interest to this research is in the definitions associated with these dimensions/determinants that are assumed to be value-attributes. These definitions are related to traditional customer interactions with service companies, i.e. non-internet-based interactions (Parasuraman et al., 2005). Although, the role of technology in services cannot be ignored currently. In all kinds of services, technologies have revolutionized service delivery and will keep changing it because of their speed, capacity, connectivity, functionality and ease of use (Parasuraman and Colby, 2015). The appeal of convenience that technologies can provide to the customers are changing transactions from full-services to self-services. A self-service technology offers to the customer the possibility to dictate the time and location of a service transaction with more flexibility and less effort (Collier and Kimes, 2012). The principles of lean consumption (Womack and Jones, 2005) understood that a general prescription about what needs to be considered as value for the customer can be associated to value-attributes depicted from the literature, as can be observed in Table II. One may perceive the interdependence among the principles and the value-attributes, as well as the sense established by the authors of previous research. In this research, the literature review presented above is used to support the proposed analysis of the value-attributes of academic and administrative services in HEIs, under the perception of HEIs’ students. 3. Method This research was conducted through focus groups and content analysis to allow an indepth understanding of the object studied. Extensive research in the literature was carried out based on indexed databases such as Scopus and EBSCO sourced with cross titles, abstract and keywords search for lean, higher education, university, lean service, value attributes and value for the student. Papers from 2010 to 2016 have been prioritized, however, this has not prevented us from considering articles published in the previous period. Particularly for the two latter keywords, the search proves to be a rather difficult because of the focus of interest on the definitions of value attributes. In many papers that were found, value attribute may have been mentioned yet not conceptually defined. 3.1 Focus groups Focus groups are an effective technique that has the advantages of gathering a wealth of data and allowing the interviewers to build a common sense about the categories under analyzes (Flick, 2009). Patton (2002) recommends that a group should gather six to eight individuals with interviews lasting from 30 min to 2 h. Besides the research aim, the decision to run three focus groups considered the effective capacity to conduct the interviews within the research planning. In qualitative research, large samples do not bring significant gains because relevant information tends to be repeated in different groups with the same social and cultural context (Oliveira, 2011). Field data were collected from three different groups and gathered following four criteria: educational program diversity, university diversity, academic level diversity and student’s progression diversity. Groups were nominated G1, G2 and G3 and totaled 16 students –

Reliability Empathy Understanding/knowing the customer Assurance Credibility Courtesy Competence Communication Security Reliability

Service value-attributes associated

Reliability Responsiveness Access Timeliness Straightforwardness Self-service technology Supplying value when the Responsiveness customer requests Timeliness Self-service technology Delivering value where Access the customer defines Tangibles Self-service technology Reducing customer’s Access decisions to solve a Straightforwardness problem Self-service technology

Providing what customer wants exactly Not wasting the customer’s time

Solving the customer’s problem completely

Principles of lean consumption

X X X

X X X X X

X X

X

X

X X

Parasuraman, Zeithaml and Berry (1988)

X X X X

X X X X X X

X

X

Parasuraman, Zeithaml and Berry (1985)

X

X X

X X

X

Hines and Lethbridge (2008)

Authors

X

X

X

X

Collier and Kimes (2012)

X

X

Hess and Benjamin (2015)

Higher education services

867

Table II. Association among the principles of lean consumption (Womack and Jones, 2005) and service value-attributes

IJLSS 10,4

868

undergraduate, master and PhD – from 12 different academic programs and five universities located in southern Brazil. Group 1 brought together six undergraduate students from two different programs at the same university. They were between 18 and 26 years old and in-different progression stages. Group 2 gathered five undergraduate students from three universities and all of them were from different programs. They were between 18 and 21 years old and most of them were in the initial years of their programs. Group 3 brought together four masters students from different programs and one doctoral student. All of them were from the same university and they were between 26 and 43 years old. In this group, only one student was less than a year into his program. The others were in the second year of their programs. Each group was organized to run with eight individuals, but some of them did not come to the appointment. The invitation for participation and the constitution of the groups took place differently at each interview because it was based on an open invitation and volunteer participation held on social media and invitations forwarded from researchers’ network. In Group 1, the fact that most of them were from the same undergraduate program made them feel free to interact with each other and to deepen each other’s ideas. As the focus of the interview was on academic-administrative services and these services are standardized at the university of these students, it is understood that this limitation does not significantly affect the opinions generated by the group. In Group 2, the age of the participants was very narrow. The fact that the interviewees were from three different universities led them to report and compare different experiences because the way of conducting the processes and the form of organization of the academic-administrative services were different. Group 3 got together students from graduate programs of different areas of knowledge at one same university. This group was constituted with the assumption that there could be new elements about the perception of value-attributes from the experience of students who had already undergone undergraduate education and had professional experience and who were experiencing a different reality in a graduate program. Data collection protocol followed a semi-structured script, stimulating the groups to exploit specifics when relevant topics to the research objectives emerged. The narration of experiences and perceptions of students when using academic services was solicited through open questions that allowed them to follow subjacent ideas and perceptions about value-attributes to stakeholders. The interviews were recorded in audio with the agreement of the interviewees and lasted approximately one hour each. 3.2 Content analysis Content analysis was applied to extract relevant information from the recorded data, which allowed us to form emerging categories (Flick, 2009). This approach aims to mitigate the effects of individual interpretation during data analysis and ensure knowledge discovery through both emerging, and previously identified constructs and structures (Bardin, 2010). Codification was based on thematic registry units. A thematic analysis focuses on the search for nuclei of meaning that can be analyzed either by their simple mention (qualitative data) or by the frequency with which they appear (quantitative data) (Bardin, 2010). Both approaches were adopted. First, each registry unit was defined by direct comparison of concepts proposed by the interviewees. Recurrence of registry units and their association to each focus group characteristic were also analyzed to identify emerging content patterns. From such analysis, an emergent categorization of common characteristics of the registry units (Bardin, 2010) regarding value-attributes to the students was proposed and confronted to the surveyed literature. The analysis was operationalized by using the MAXQDA 11 software.

4. Results 4.1 Value-attributes from the perception of the students From data collection, eight attributes were identified that related to what students consider value in HEIs’ administrative and academic services: reliability, empathy, access, responsiveness, self-service technology convenience (SSTC), communication, personalization and imperceptibility. Reliability, empathy, access, responsiveness and SSTC were the most recurring category, although each surveyed group had emphasized different elements. Figure 1 presents an interaction intensity matrix, depicting the relative frequency of the registry units for each attribute in connection to other attributes in the same group. Larger circles refer to a higher frequency (quantitative data) of the value-attribute registry unit. The eight categories cannot be understood in isolation because there are interrelations between them. When analyzed, these interactions and distinct associations are identified. Figure 2 shows the relative frequency representing such results based on the overall registry units from the three focus groups. Larger circles represent a higher frequency of the relationship among value-attributes registry units. Based on the content analysis, a relational map (Figure 3) was produced to represent the interactions and their intensities according to Figures 1 and 2. In Figure 3, the most recurrent categories were shown by their larger box dimensions, and the intensity of a relationship between categories was shown by the thickness and color of the arrows: the more intense the relation, the thicker the arrow, and complementary, darker the color. Figures 1-3 were produced with the support of the MAXQDA software. Each of the categories and their interrelationships is presented and discussed subsequently.

Higher education services

869

Figure 1. Interaction intensity matrix: attributes versus groups

Figure 2. Interaction intensity matrix: attribute versus attribute

IJLSS 10,4

870

Figure 3. Value-attributes map for academic and administrative services in private HEIs: students’ perspective

4.2 Reliability Reliability, as established by the interviewees, is a value-attribute related to all other categories and for this reason it is central to the analysis even though it is not the most quantitatively recurrent category, as seen in Figure 1. Reliability rises from the expectation that someone will assist and resolve a student’s demands at the first contact, even if the desired solution is not immediate. It relates to a correct and adequate response, rejecting the idea that the student will be guided to another department to solve the request. It concerns resoluteness and is similar to the definition of reliability as stated by Parasuraman et al. (1985, 1988). Examples of problems faced that reinforce reliability as a value-attribute are the following: documents filled incorrectly by the staff, requiring more than one student’s displacement; lack of response to emails; and system bugs that penalize students who need to guarantee their enrollment. Positive experiences mentioned to highlight reliability as a value-attribute are the substitution of paper-based by electronic processes and dispensing with the presentation of documents related to previously performed activities in the same university. The students remarked that the university, through its customer relation channels, must provide a correct answer that solves the problem and show willingness to understand the demand and to be sympathetic to it. A properly posed demand from a student should not cause problems directly or indirectly to himself. The proposition of a correct and adequate service and problem-solving capacity are part of the expected delivery condition: “solve my problem completely.” The first principle of lean consumption by Womack and Jones (2005)

is strongly related to the category defined as reliability, but it is not only related to this principle. Providing exactly what the student wants and considering his time, not wasting it, are also aligned with this concept. 4.3 Empathy Empathy refers to caring for the student, being sensitive to the real demands he presents, and respecting his specificities. For the groups interviewed, empathy is related to courtesy, attention, availability, willingness of the institution to meet the student’s needs and understanding him. Examples mentioned by the groups about lack-of-empathy situations relate to the following: unanswered feedback promises; unbalanced degrees of attention and dedication to students depending on the program they belong to; and changes in tools and systems without adequate orientation to the students. Positive examples cited include reminder emails sent by the library on the previous day to remember to return books and proximity and carefulness by the administrative staff of the student’s program, avoiding the need for him to resort to general offices. The interviewees associated the empathy category with clearing topics and doubts with the students before the service, allowing them some choices; offering a welcoming environment and a quality service; encouraging employees to “put themselves in the students’ shoes;” and ensuring transparency for the service process. This definition is related to the empathy and assurance definitions that contain the idea of courtesy, credibility, and understanding the customer completely as stated by Parasuraman et al. (1988). Different from the findings of these authors (1988), however, empathy, as the interviewees enunciated, needs to be treated in a different category than communication and access, despite being connected to them. A significant connection is found between the categories of empathy and reliability. If empathy refers to understanding exactly what the student needs and to meeting his demand, then, in the long term, providing the most appropriate service will refer to building trust and fulfilling what has been agreed as the service, i.e. reliability. This concept is broadened when empathy is related to the university’s willingness to meet the students and their real needs and demands. If considering the six principles of lean consumption of Womack and Jones (2005), empathy does not seem to be properly considered because understanding what the student wants is required prior to addressing a demand. Empathy with the problem of a student presumes that the institution is able to “put itself in the student’s shoes.” 4.4 Access For the interviewees, access means accessibility, straightforwardness and simplicity. It refers to the student being able to know where the solution for his need is located and being able to access the information or service easily and quickly. Following the definition of Parasuraman et al. (1985), access refers to ease of contact and accessibility. The service must be available in such a way that it can be accessed or located with no effort. The delivery of the service should not oblige the customer to wait longer than the effectively needed. In the focus groups, the access category appeared always to be based on previous experience: access to documents; access to classroom materials; access to the website, registration, campus services, and to everything that is important to their academic life in a simple and easy way. Although the accessibility of physical space has been mentioned, access is mainly concerned with the virtual environment – website, mobile application and virtual platform. Respondents indicated that they wanted to be able to obtain documents

Higher education services

871

IJLSS 10,4

872

provided by the university, to deposit documents required by the institution, to download materials for classes, to consult any information, and to locate themselves in the campus through tools with easy access and simplified management in a virtual environment. The time expended to access a service is an aspect, which has been stressed in the access category, raising negative reports related to service quality and planning such as long waiting times to receive face-to-face attendance, the queues in peak periods caused by unsatisfactory opening hours and unbalanced personnel allocation, and the time lost entering the virtual environment and not finding the material that should be available. The idea of access is faced in Hines and Lethbridge (2008) in the HEIs context when they refer to uncomplicated services. In a more general context, the access attribute is faced in Womack and Jones (2005) in the sixth lean consumption principle – reducing customer’s decisions to solve a problem and is related to the fifth principle of delivering value where he wants. Additionally, the idea of not wasting the customer’s time is also observed in the second lean consumption principle. 4.5 Responsiveness Responsiveness relates to attending (immediately or in a short time) to the student’s demand. It is related to the expected availability of a service so that the demand is properly received and processed within an adequate time. As defined by Parasuraman et al. (1985, 1988), responsiveness refers to the readiness of the personnel and their timeliness to provide a service. Negative experiences related to this category include excessive delays for getting a correct response to requests, excessive e-mail exchanges to solve a problem and the increased makespan on this interaction, and the absence of the staff responsible for signingon trainee documents, hindering the start of students’ practices. Reliability and access are the categories most related to responsiveness. In the definition of responsiveness, the immediate or short-term attendance and the timeliness in the delivery of a service as agreed is related to the availability of the service (access) and the consolidation of consistency of performance (reliability). Responsiveness, for Womack and Jones (2005), is related to not wasting the time of the customer and supplying value when he requests it. The efficiency of a productive system is a time-related measure. For lean production, waiting time is waste that needs to be eliminated. Time is important not only for production but also for the customer. Womack and Jones (2005) reinforce this understanding by linking two of the six principles of lean consumption to time: “do not waste my time” and “provide value when I want to.” The first is related to waiting time and the second to delivery in the desired time. These value attributes are also mentioned by Balzer (2010) for waiting time and by Balzer (2010), Hess and Benjamin (2015), and Hines and Lethbridge (2008) for timeliness. 4.6 Self-service technology convenience SSTC involves all the services mediated by information and communication technologies (ICTs), which allow ubiquitous or virtual service requests for the student. The following channels were mentioned: email, chat, websites, mobile applications and virtual platforms for learning. The students referred to issuing documents, answers to doubts, access to teachers’ materials, access to information, peer-to-peer communication and news over the internet as relevant aspects of SSTC as a value-attribute on the researched subject. Its value increases the more it gives autonomy to the student in relation to the physical attendance at the university. This aspect may lead to making the student a larger part of the academic-administrative

service, and this is expected, according to the interviewed students. Nevertheless, this also implies an expectation of cost reduction for the student. The description of the SSTC category is connected to the definition of access, but it is not limited to it. Access focuses on the displacement factor and face-to-face contact, which are mitigated by self-service technology. SSTC presumes more involvement of the student himself in the realization of part of the service, increasing flexibility, disintermediation and immediacy to demands. Regarding the possibilities of SSTC, the interviewees have the opinion that outside of the classroom, most academic-administrative demands could be treated through self-service technology to reduce the student’s time and displacement. On the one hand, SSTC was positively referred to the certainty of getting an answer to an online contact with the university. Other positive references include when the website presents the necessary information for the student; when a mobile application makes easier to find the classroom for the student; and when academic credit assessment is done directly through the system without the need for paper proofs. In all these cases, there is no need for physical displacement of the student because the demand is met in the virtual environment. On the other hand, the experience with self-service technologies can be negative when the online attendance directs the solution out from the virtual world; when there are interruptions in the online attendance without forwarding a solution; and when there is a change or upgrade of the ICT system, which was not previously informed or which makes the process more complex. The SSTC category is also related to responsiveness. If SSTC provides autonomy for the student to seek the service when he wants, then it contributes to responsiveness because he expresses a desire for an immediate response to obtain the service. In the context of HEIs, the groups interviewed pointed out autonomy through SSTC as related to reduced waste of time and displacement and to increased simplicity that allows them to focus on their main objective, which is to study. They understand the advantages of technologies as mentioned by Parasuraman and Colby (2015): speed, capacity, connectivity, functionality and ease of use. Under such a perspective, SSTC may be a contributing factor to the lean consumption principles presented by Womack and Jones (2005) about wasting time and delivering value when and where desired. 4.7 Communication Communication refers to the university providing all necessary information for the student’s academic life, including academic processes, routine requests and daily events. It is expected by the interviewees to be broad and massive in scope and transparent. The university is expected to make clear the rules for the student, so that he knows exactly what service he is hiring. Clarity refers to knowing about everything that is offered and allowing the student to choose what suits him best. This definition is consistent with what Parasuraman, Zeithaml and Berry (1985) define as communication for their determinants of service quality. The authors also mention that communication implies that the institution understands what is needed to adjust its language to serve different customers. The interviewees mentioned the necessity of timely communication because of the impact of missing information on the student’s life. The need to use alternative media to ensure that communication reaches the student and, conversely, the necessity of narrowing the communication channels have been presented as expected characteristics of communication. In the sense of prioritization, the difficulty of sorting out what is important from the excess of information that comes through various media was mentioned as related to clarity. As a general expression, a negative perception occurs when the student does not

Higher education services

873

IJLSS 10,4

874

receive information or is unaware of the existence of services because of inadequate communication. Communication is strongly related to the empathy category. Although very close, empathy and communication are different categories of analysis. While empathy presupposes establishing an effective communication relationship, it is related to a more individualized movement toward the student or a specific group of students. Communication, in turn, even though it represents a movement of attention to the student, refers to a more general and non-individualized communication movement. In Womack and Jones (2005), communication, as value-attribute, is related to reducing the decisions that the student has to make to get the correct information, and therefore, to optimizing his time. 4.8 Personalization Personalization, as a value-attribute, means that the student wants to have a “tailor-made” service to assist him with his specifics demands considering each reality. The student wants to feel that he is being treated specially welcomed to meet his needs. Although empathy and personalization are related, they differ from each other. The empathy value-attribute relates to understanding a student’s demand and seeking the best solution among the standardized options that the HEI can offer. The personalization valueattribute focuses on the intention to produce a unique solution that is specific to that student. In other words, personalization is related to understanding and knowing the customer and there is individualized attention in which the specific demands are known as Parasuraman, Zeithaml and Berry (1985) state. However, personalization presupposes an equally individualized solution, which is not signaled by these authors. In Womack and Jones (2005), this value-attribute is linked to the principle of complete resolution of the problem and to delivering as specified – “provide what I want exactly.” When compared to the other categories identified in the general context of the focus groups, personalization was less emphasized. It was characterized as a peripheral attribute for the definition of what adds value to academic-administrative services. The reason for this may be attributed to the broad role of academic services provided by the HEIs. The same is true for the imperceptibility category, defined below. 4.9 Imperceptibility Imperceptibility as a value-attribute refers to students not having to worry about processes, which are not linked directly to the primary goal of improving knowledge. It refers to not needing to be involved in behind the scenes processes, which only should become perceptible to the student when effectively necessary. It also considers that secondary processes should not interfere with the main process – the teaching-learning relationship in the classroom. Imperceptibility is not discussed specifically as an attribute of value or a determinant of service quality in the publications surveyed. Even so, it could be said that reducing the number of decisions that the students need to make to solve their problems – Womack and Jones’s (2005) sixth principle – is related to reduce student discomfort. However, it entails greater action from the university personnel, so the student should be less involved in the workflow of his demands and the demands from the university to the student. Summing up the previous description, Table III presents the definition for the eight attribute-values as stated by the interviewees.

Value-attribute

Definition

Reliability

The expectation that the demand will be assisted and resolved at the first contact, even if the solution is not immediate. It relates to resoluteness, to a correct and adequate response, rejecting the idea that the student will be guided to another department to solve the demand Movement of caring for the student, being sensitive to the real demands he presents, respecting his specificities. It is related to attention, availability, and willingness by the institution to meet the student’s needs. It refers to understand exactly what the student needs and to meet his demand, providing the most appropriate service Accessibility, straightforwardness and simplicity. The student knows where to get the solution for his needs and is able to access the service easily, quickly, with no effort. The delivery of the service should not oblige the student to wait longer than the effectively needed Attendance, immediate or in a short time, to the student’s demand. It is related to the expected availability of the service so that the demand is properly received and processed within adequate time The offer of ubiquitous or virtual services for the student. Value increases the more it gives autonomy to the student in involvement in the realization, by himself, of part of the service, increasing flexibility, disintermediation and immediacy to put demands To provide all necessary information, offers and rules for the student’s academic life, including academic processes, routine requests, educational commitment and daily events. It is broad and massive in scope and transparent. It allows the student to know what is offered and to choose what suits him better Service with a unique solution to fit the specific demand of the student. The student wants to feel being treated specially, welcomed on his needs and individual reality It concerns running processes involving the student only when he is necessary. Secondary processes shall not interfere with the main process – the teaching-learning relation in the classroom

Empathy

Access Responsiveness SSTC Communication

Personalization Imperceptibility

5. Discussion Academic-administrative services involve processes to prepare, support, monitor, and certify the interactions between a student and the program in which he is enrolled and the HEI. They are responsible for fulfilling the legal norms and procedures established either by external regulators or by the HEI, while fulfilling the specific needs demanded by the student and which originate from such a relationship. From this understanding, two kinds of demands may be identified: those arising from norms and rules to be attended; and those related to expectations and needs that arise from the context in which HEI and student interact. The eight attributes identified contribute to organizing and prioritizing the value stream flow through the academic-administrative processes offered by the HEI, as they are defined from the perspective of the student – the beneficiary of the service. By analyzing the value-attributes that resulted from the focus groups, such categorization is beneficial to better understand the different emphases of value involved therein. All the three groups highlighted a certain antagonism when describing the attributes of value for the academic-administrative services. On the one hand, the students desire increased autonomy to demand and to perform the services they need. On the other hand, they emphasize the need for personalization, guidance, and welcoming by the HEI. This difference is related to the diverse types of services under analysis in this relationship. To better understand what was exposed above, the services positioning matrix by Larsson and Bowen (1989) was used. General services and those related to norms and regulations typically represent a subgroup of pooled services. There is a considerable degree of standardization, and their execution is focused on the back-office. Examples involve course

Higher education services

875

Table III. Value-attributes for academicadministrative services as stated by students

IJLSS 10,4

876

Figure 4. Association between categories of academicadministrative services and the identified value-attributes

enrollment procedures, registry access, and standardized document emission. Those differ from sequential-standardized services in which the student’s participation is necessary. Their execution depends upon individual information from the student, as in the case of academic credit evaluation. Finally, sequential-customized services are related to the specific needs of the student, as in non-standardized document emission. In this case, guidance and welcome will overcome the desire for autonomy, according to the participating students’ point of view. Hence, customization can be associated with HEI-student interaction to fulfill individual needs. Standardized solutions may be associated with student’s autonomy, as his participation is necessary and defines the service solution. Pooled services, when fulfilling protocolary needs, allow HEI’s autonomy and also student’s autonomy. In the last case, the student does not want to be involved in the service production, meaning that the student is keen to accept the criteria and service specifications from the HEI in exchange for the autonomy to demand and receive the service at his convenience. From the previous analysis, the academic-administrative services were categorized into three classes and the value-attributes resulting from this research were associated to each of them. Figure 4 graphically represents this association. As stated by Carlborg et al. (2013), value-attribute depends on the service-category. From Figure 4, one can note that sequential-customized services (in the pink area in Figure 4) are associated to attributes involving individualized specifications and the premise of interaction to deliver value (empathy, personalization, access, communication, reliability and responsiveness). In this sense, the focus of the service-value is on the interaction established by the student and the HEI representatives. The front-office and back-office need to interact actively to deliver the exact service requested. Pooled services (in the blue area in Figure 4) in their turn, give HEI the autonomy to determine the specifications and availability of the services. They are related to value-attributes, which refer to making the service available to the student (SSTC, access, communication, reliability, responsiveness and imperceptibility). This gives the back-office the autonomy to specify the appropriate channels and manners to do so. Finally, sequential-

standardized services (in the yellow area of Figure 4) are related to value-attributes, which refer to a higher willingness of the student to actively participate in the service production (access, reliability, responsiveness, SSTC and communication). According to Carlborg et al. (2013), the use of lean approaches may result in a trade-off between increasing efficiency and reducing customer satisfaction. This thesis may be questioned from two of the eight value-attributes identified in this research: access and empathy. Access is defined by convenience, simplicity, and easiness for the student to submit a service demand. This definition implies that the student should not wait for more than the strictly necessary time to present his demand. To reduce wait time means to improve the service production flow, hence, leading to associate access to value-deliverance to the student. To Carlborg et al. (2013), access is related to pushing the customer through the process, which differs from the characterization presented above. In the studied context, access is related to removing obstacles and mitigating their effects on the student, allowing him to pull the flow whenever necessary either in sequential-standardized or in sequential-customized services. Empathy is more relevant to sequential-customized services, as it is related to caring about the specificities and needs of each demanding student. It is related to attention, to availability, and to the will of the HEI to reach the student in a valuable manner. Nevertheless, to Carlborg et al. (2013), there is a trade-off between standardization and customization. However, from a perspective of process improvement, standardized actions and channels may contribute to facilitate reaching empathy with the student and his needs, avoiding differences of access to students. This standardization neither does conflict with providing an individualized treatment nor needs to affect delivering value to the student. Empathy means to consider the need for individualized caring about the student’s needs through standardized procedures and activities, which shall be evaluated and systematically improved (Liker, 2004). The divergences from Carlborg et al. (2013) lay in how academic-administrative services need to be unfolded into processes and activities. From the lean perspective, continuous improvement departs from a process-based view, and improvement actions need to consider the value-attributes established by the customer. Only if the value is perceived by the customer will it reflect into his satisfaction (Cronin et al., 2000). In addition, considering that academic-administrative services are peripheral to the HEI’s core business and the students’ interests, they serve as a support to qualify the relationship between those parts. Nevertheless, there is a gap when this concept is linked to the principles of lean consumption proposed by Womack and Jones (2005). The first statement “solve my problem completely” is absolute; there is no satisfaction otherwise. However, there may be occasions in which the student’s need may not be completely fulfilled because of existing norms and regulations, for instance. In such cases, for the HEI to not depart from the student’s expected satisfaction and to ensure he keeps trusting the institution, one course of action would be to consider the value-attributes of empathy and personalization when designing the service. Following this argument, a previous principle should be added to the list proposed by Womack and Jones (2005): “understand my problem completely.” Hence, the eight value-attributes identified from the interviews were associated with the principles of lean consumption as proposed by Womack and Jones (2005) and added to a new proposition as presented in Table IV. To solve the student’s problem completely presupposes a complete understanding of the problem. By stressing this aspect, it is possible to emphasize, in this context of services, other aspects related to relationship and service specification, such as empathy and personalization. The association of lean consumption principles with value-attributes is useful for lean process improvements. If the problem lies on one or more of these principles, waste reduction proposals can be better designed by considering the adequate value-attributes.

Higher education services

877

Table IV. The extended principles of lean consumption and the value-attribute identified for academicadministrative services

Understand my problem completely Solve my problem completely Provide exactly what I want Do not waste my time Supply value when I want it Deliver value where I want it Reduce the number of decisions I must make to solve my problems

X X

X X X X X X X X

X

X X

X X X X

X

X X

Empathy Personalization Reliability Access Responsiveness SST convenience Communication Imperceptibility

878

Principle of lean consumption

Value-attribute

IJLSS 10,4

Complementary, if HEIs need to improve their processes, it will be useful to understand that not all of the value-attributes need to be stressed to improve quality and add value to the process. By defining the service design category, only some of them need to be improved. For example, if the student wants a customized document, offering a face-to-face attendance will aggregate more value than offering a self-service technology where the student will not be sure if their need is adequately comprehended and handled. In Table V, there are some examples of academicadministrative services and their categories and associated value-attributes. By understanding the different value-attributes, one may adopt the appropriate lean techniques and tools to reduce waste and non-added-value activities. Services can then be designed so that they better meet the needs of the students from what they signal as having value. The management of academic-administrative services, therefore, must consider the differences of value-attributes in their operations and how they must be emphasized depending on the service category. Doing this will enable both increasing service efficiency and customer satisfaction. There were eight value-attributes that resulted from the groups interviewed. In the definitions presented by the students, it is possible to identify most of the value-attributes that emerged from the literature review.

Higher education services

879

6. Conclusion In an environment of competition for students, increasing efficiency in the HEIs’ processes is a necessity for their survival (Thomas et al., 2015). However, HEIs still need to excel in listening to and understanding what the student considers as value. Balzer (2010) states that value in many HEIs is what the academic support areas themselves define as such. The hierarchical and administrative structure of HEIs makes it difficult for students to provide their perceptions of value (Balzer, 2010). Value is still associated with better processes for internal staff. To deliver value to the customer is something that still needs to be developed (Radnor and Bucci, 2011). To know what the customer needs and what he considers to have value and what is valuable in services is mandatory. The voice of the customer (VOC) techniques, common in lean and lean six-sigma, require careful planning and resources (Found and Harrison, 2012) that sometimes are not available. The framework of eight value-attributes for academic and administrative services in HEIs may receive different degrees of prioritization and improvement effort according to the type of service and strategic positioning of the organization. This framework can be a useful starting point to establish what adds value for process improvements complementary to VOC techniques or even when there are no resources to run these techniques. This research intends to disclose students’ value-attributes for academic and administrative services in HEIs by approaching lean service with service theory. Lean service is still an in Service categories (Larsson and Bowen, 1989)

Value-attributes

Pooled services

ICT solutions, access, communication, reliability, responsiveness, imperceptibility

Sequential-standardized services Sequential-customized services

Access, reliability, responsiveness, ICT solutions, communication Attention, personalisation, access, communication, reliability, responsiveness

Examples of academic-administrative services Online enrolment; standardized documents emission (enrolment certificate and academic history); book lending renewal; check schedule and room numbers for classes First enrolment to an academic program; loaning of library books Request of prerequisite relieve for course enrolment; non-standard enrolment; non-standardized documents emission

Table V. Categories of services and HEIs’ academicadministrative services with respective valueattributes

IJLSS 10,4

880

developing area (Gupta et al., 2016) and needs more studies that have a dialog with the services theory. In this sense, knowing that all lean service processes need to be oriented to value for the customer is not enough. Service theory can enlighten issues related to service delivery, valueattributes, value creation and customer experience that can propose new ways of how-to-do lean in services. In the same way, service theory can be enlightened by lean service because service research has the potential to engage in more inter- and trans-disciplinary theorizing with landing disciplines (Gustafsson et al., 2016). Thus, this research presented value-attribute definitions as clarified by the students and also based on definitions found in service theory. These attributes are more relevant depending on the diversity of the demand and the disposition of the student to participate in the service process. This comprehension is necessary to develop a better lean process. In modeling the improvements, it is not necessary to consider all of the value-attributes, but only those that matter most and these depend on the specific type of service and the expectations. Like any research, this one also has limitations. A first aspect to be observed concerns the group interviews. A higher number of academic programs and HEIs should be represented to provide greater diversity and to extend the participation of students. However, although invitations were sent to a considerable number of HEIs’ representatives, the response is not a factor within the control of the researcher. Another limitation is the nature of the enrollment of the students. The participants belong to in-person attendance programs in private institutions. No students from distance-learning programs or public HEIs contributed to the research. Although this is a limitation of this research, it is also an opportunity for future research because the nature of interaction changes in distancelearning programs and the context of relationships changes in public HEIs. What would be the value-attributes to be considered for designing academic-administrative services from the perspective of these students? Some suggestions for future research are to produce a quantitatively validated model for students of private HEIs and to verify qualitatively if the value-attributes are the same for students of public HEIs and distance-learning students. Case studies to verify the applicability of the proposed framework are also welcomed research. Finally, and from a broader perspective, empathy is drawn to the fact that value is a central element of lean philosophy and in the services theory. However, there still is a lack of research focusing specifically on value and its attributes, how the attributes are defined, if indeed the students are the clients that should define and create them, and how lean solutions or tools can meet these defined value attributes. References Antony, J. (2014), “Readiness factors for the lean six sigma journey in the higher education sector”, International Journal of Productivity and Performance Management, Vol. 63 No. 2, pp. 257-264. Antony, J., Krishan, N., Cullen, D. and Kumar, M. (2012), “Lean six sigma for higher education institutions (HEIs): challenges, barriers, success factors, tools/techniques”, International Journal of Productivity and Performance Management, Vol. 61 No. 8, pp. 940-948. Balzer, W.K. (2010), Lean Higher Education: Increasing the Value and Performance of University Processes, Taylor and Francis, Nova York. Balzer, W.K., Brodke, M.H. and Kizhakethalackal, E.T. (2015), “Lean higher education: successes, challenges, and realizing potential”, International Journal of Quality and Reliability Management, Vol. 32 No. 9, pp. 924-933. Balzer, W.K., Francis, D.E., Krehbiel, T.C. and Shea, N. (2016), “A review and perspective on lean in higher education”, Quality Assurance in Education, Vol. 24 No. 4, pp. 442-462. Bardin, L. (2010), Análise de Conteúdo, 4. edição, Edições 70, Lisboa.

Carlborg, P., Kindstrom, D. and Kowalkowski, C. (2013), “A lean approach for service productivity improvements: synergy or oxymoron?”, Managing Service Quality: An International Journal, Vol. 23 No. 4, pp. 291-304. Collier, J.E. and Kimes, S.E. (2012), “Only if it is convenient: understanding how convenience influences self-service technology evaluation”, Journal of Service Research, Vol. 16 No. 1, pp. 39-51. Comm, C.L. and Mathaisel, D.F.X. (2005), “A case study in applying lean sustainability concepts to universities”, International Journal of Sustainability in Higher Education, Vol. 6 No. 2, pp. 134-146. Cronin, J.J., Brady, M.K. and Hult, G.T.M. (2000), “Assessing the effects of quality, value, and customer satisfaction on consumer behavioral intentions in service environments”, Journal of Retailing, Vol. 76 No. 2, pp. 193-218. Doman, M.S. (2011), “A new lean paradigm in higher education: a case study”, Quality Assurance in Education, Vol. 19 No. 3, pp. 248-262. Douglas, J.A., Antony, J. and Douglas, A. (2015), “Waste identification and elimination in HEIs: the role of lean thinking”, International Journal of Quality and Reliability Management, Vol. 32 No. 9, pp. 970-981. Eagle, L. and Brennan, R. (2007), “Are students customers? TQM and marketing perspectives”, Quality Assurance in Education, Vol. 15 No. 1, pp. 44-60. Flick, U. (2009), An Introduction to Qualitative Research, 4th ed., SAGE, London. Found, P. and Harrison, R. (2012), “Understanding the lean voice of the customer”, International Journal of Lean Six Sigma, Vol. 3 No. 3, pp. 251-267. Francis, D.E. (2014), “Lean and the learning organization in higher education”, Canadian Journal of Educational Administration and Policy, No. 157, April, pp. 1-23. Gupta, S., Sharma, M. and Sunder M, V. (2016), “Lean services: a systematic review”, International Journal of Productivity and Performance Management, Vol. 65 No. 8, pp. 1025-1056. Gustafsson, A., Högström, C., Radnor, Z., Friman, M., Heinonen, K., Jaakkola, E. and Mele, C. (2016), “Developing service research – paving the way to transdisciplinary research”, Journal of Service Management, Vol. 27 No. 1, pp. 9-20. Hess, J.D. and Benjamin, B.A. (2015), “Applying lean six sigma within the university: opportunities for process improvement and cultural change”, International Journal of Lean Six Sigma, Vol. 6 No. 3, pp. 249-262. Hines, P. and Lethbridge, S. (2008), “New development: creating a lean university”, Public Money and Management, Vol. 28 No. 1, pp. 53-56. Isaksson, R., Garvare, R., Johnson, M., Kuttainen, C. and Pareis, J. (2015), “Sustaining Sweden’s competitive position: lean lifelong learning”, Measuring Business Excellence, Vol. 19 No. 1, pp. 92-102. Jongbloed, B., Enders, J. and Salerno, C. (2008), “Higher education and its communities: interconnections, interdependencies and a research agenda”, Higher Education, Vol. 56 No. 3, pp. 303-324. Kinser, K. (2010), “The global growth of private higher education”, ASHE Higher Education Report, Vol. 36 No. 3, pp. 121-133. Ladhari, R. (2009), “A review of twenty years of SERVQUAL research”, International Journal of Quality and Service Sciences, Vol. 1 No. 2, pp. 172-198. Larsson, R. and Bowen, D.E. (1989), “Organization and customer: managing design and coordination of services”, Academy of Management Review, Vol. 14 No. 2, pp. 213-233. Lepak, D.P., Smith, K.G. and Taylor, M.S. (2007), “Value creation and value capture: a multilevel perspective”, Academy of Management Review, Vol. 32 No. 1, pp. 180-194. Liker, J.K. (2004), The Toyota Way, The McGraw-Hill Companies, New York, NY. Olivares, M. and Wetzely, H. (2014), “Competing in the higher education market: empirical evidence for economies of scale and scope in German higher education institutions”, CESifo Economic Studies, Vol. 60 No. 4, pp. 653-680.

Higher education services

881

IJLSS 10,4

882

Oliveira, D.M.T.D. (2011), “Amostra qualitativa e recrutamento”, in Perdigão, D.M., Herlinger, M. and White, O.M. (Eds), Teoria e Prática Da Pesquisa Aplicada, Elsevier, Rio de Janeiro, pp. 120-127. Ostrom, A.L., Bitner, M.J., Brown, S.W., Burkhard, K.A., Goul, M., Smith-Daniels, V., Demirkan, H. and Rabinovich, E. (2010), “Moving forward and making a difference: research priorities for the science of service”, Journal of Service Research, Vol. 13 No. 1, pp. 4-36. Parasuraman, A. and Colby, C.L. (2015), “An updated and streamlined technology readiness index”, Journal of Service Research, Vol. 18 No. 1, pp. 59-74. Parasuraman, A., Zeithaml, V.A. and Berry, L.L. (1985), “A conceptual model of service quality and its implications for future research”, Journal of Marketing, Vol. 49 No. 4, pp. 41-50. Parasuraman, A., Zeithaml, V.A. and Berry, L.L. (1988), “SERVQUAL: a multiple-item scale for measuring consumer perceptions of service quality”, Journal of Retailing, Vol. 64 No. 1, pp. 12-40. Parasuraman, A., Zeithaml, V.A. and Malhotra, A. (2005), “E-S-Qual: a multiple-item scale for assessing electronic service quality”, Journal of Service Research, Vol. 7 No. 3, pp. 213-233. Patton, M.Q. (2002), Qualitative Research and Evaluation Methods, 3rd ed., Sage. Priem, R.L. (2007), “A consumer perspective on value creation”, Academy of Management Review, Vol. 32 No. 1, pp. 219-235. Radnor, Z. and Bucci, G. (2011), Analysis of Lean Implementation in UK Business Schools and Universities, Associaton of Business Schools, London, p. 74. Sunder, M.V. (2014), “Quality excellence in higher education system through six sigma: student team engagement model”, International Journal of Six Sigma and Competitive Advantage, Vol. 8 Nos 3/4, p. 247. Sunder, M.V. (2016a), “Lean six sigma in higher education institutions”, International Journal of Quality and Service Sciences, Vol. 8 No. 2, pp. 159-178. Sunder, M.V. (2016b), “Constructs of quality in higher education services”, International Journal of Productivity and Performance Management, Vol. 65 No. 8, pp. 1091-1111. Svensson, C., Antony, J., Ba-Essa, M., Bakhsh, M. and Albliwi, S. (2015), “A lean six sigma program in higher education”, International Journal of Quality and Reliability Management, Vol. 32 No. 9, pp. 951-969. Taylor, J. (2012), “Fads and fancies: the use of new management tools in UK universities”, Excellence in Higher Education, Vol. 3 No. 1, pp. 1-13. Thirkell, E. and Ashman, I. (2014), “Lean towards learning: connecting lean thinking and human resource management in UK higher education”, The International Journal of Human Resource Management, Vol. 25 No. 21, pp. 2957-2977. Thomas, A.J., Antony, J., Francis, M. and Fisher, R. (2015), “A comparative study of lean implementation in higher and further education institutions in the UK”, International Journal of Quality and Reliability Management, Vol. 32 No. 9, pp. 982-996. Womack, J.P. and Jones, D.T. (1996), Lean Thinking: Banish Waste and Create Wealth in Your Corporation, Vol. 2, Simon and Schuster, New York, NY. Womack, J.P. and Jones, D.T. (2005), Lean Solutions: How Companies and Customers Can Create Value and Wealth Together, Simon and Schuster, New York, NY. Corresponding author Anete Petrusch can be contacted at: [email protected]

For instructions on how to order reprints of this article, please visit our website: www.emeraldgrouppublishing.com/licensing/reprints.htm Or contact us for further details: [email protected]

The current issue and full text archive of this journal is available on Emerald Insight at: www.emeraldinsight.com/2040-4166.htm

How to use lean Six Sigma methodology to improve service process in higher education A case study Na Li and Chad Matthew Laux Department of Technology of Leadership and Innovation, Purdue University, West Lafayette, Indiana, USA, and

Lean Six Sigma methodology

883 Received 26 November 2018 Revised 20 February 2019 Accepted 17 April 2019

Jiju Antony School of Management and Languages, Heriot-Watt University, Edinburgh, UK

Abstract Purpose – The purpose of this paper is to use a practical case study approach to demonstrate the power to use lean Six Sigma (LSS) to improve service process in a higher education institution (HEI). The paper also illustrated the barriers and challenges met and lessons learnt for the LSS adoption in this HEI. Design/methodology/approach – Prior to the study, extensive literature review was conducted to understand various aspects of LSS in HE industry. The authors use a single descriptive case study as methodology to explain how DMAIC was applied within a HEI environment. Findings – In this LSS case study, the team found HEI service process contains a large human behavior component, which dramatically increases the unpredictability of the entire service delivery process and increases the complexity of the process and the ability of the improvement team to identify the root cause. This case study demonstrates the numerous challenges will occur in working with the intangible factors that are both hard to recognize, quantify and rarely tracked by organization. Practical implications – During the research, the pre-award service process was studied, data were recorded and various statistical tool and techniques were used to discover and resolve the root cause. The lessons learnt of the LSS adoption in this service process in HEI and the problems encountered were all recorded in this study, which will be helpful for future research in HEI industry. Originality/value – From the literature review, LSS has been widely adopted in manufacturing industry, increased adoption in service, but there has been limited academic research about the implementation in nonprofit, service sectors, particular to higher education industry. The major benefit of implementation LSS in both manufacturing and service is considerable improvement to the bottom line. However, in this HEI case study, it has opened up the direction to implement LSS to better serve your customer as ultimate mission instead of financial gains. Keywords Six Sigma, Lean Six Sigma, Higher education, DMAIC, Service process, Paper type Case study

1. Introduction In this paper, the authors introduced a Lean Six Sigma (LSS) project in a service department in an Institution of Higher Education and explored the challenges met during the application. The objective of this paper is to use a practical case study approach to demonstrate the power to use LSS to improve service process in Higher Education. Barriers and challenges met during the LSS application in a selected service department (Pre-Award)

International Journal of Lean Six Sigma Vol. 10 No. 4, 2019 pp. 883-908 © Emerald Publishing Limited 2040-4166 DOI 10.1108/IJLSS-11-2018-0133

IJLSS 10,4

884

in one university are also observed and discussed in the end of the paper. The authors used DMAIC methodology to help a service department in improving customer service and provide detailed descriptions about a problem solving processes. The uniqueness of this service process is that customers’ various expectations, communication method and individuals play a critical role in the success of process performance. A large human behavior component dramatically increases the unpredictability of the entire service delivery process and increases the complexity of the process and the ability of the improvement team to identify the root cause. The key research questions are how to use effectively LSS to improve service process in Higher Education? What are the major obstacles and lessons learned from this practical case study? 2. Lean Six Sigma in higher education 2.1 What is lean? Lean principles started in the manufacturing sector with a goal of minimizing or eliminating waste, or activities that do not add value to paying customers, therefore, increasing the value to customers through improved business efficiency (Womack et al., 1990; Womack and Jones, 2010; Naslund, 2008). Lean has its own improvement methodology through a systematic and continual improvement by all personnel, through an organizational setting, to improve process speed to customer, fixing the connections and interfaces among process steps, or activities, with less work and improved process flow. Derived from Taichi Ohno’s Toyota Production System, has become predominant in a variety of industries (Womack et al., 1990; Sareen et al., 2014). A common definition of Lean is a dynamic process of change, relying upon a set of principles and best practices aimed at continuous improvement (Womack et al., 1990; Albiwi et al., 2015). In addition to reduction of waste, improved process flow, is a basic principle where customers pull demand through an organization, rather than product and services being pushed by an organization upon an indifferent customer base. As a result, Lean adoption leads to the production of high-quality products, more satisfied customers, to meet market demands, with relatively small levels of inventory (Naslund, 2008). 2.2 What is Six Sigma? Six Sigma is a rigorous, focused and highly effective implementation of proven quality principles and techniques (Pyzdek and Keller, 2014). Six Sigma aims to control errors in business performance within 3.4 defects per million opportunities produced. With high profile adoptions by companies such as General Electric (GE) in the mid-1990s, Six Sigma has spread widely (Goh, 2002). Kumar et al. (2008) pointed out that one of the most common myths about Six Sigma is that it is beneficial to manufacturing processes. Since the application of Six Sigma was originally limited to manufacturing, the application of Six Sigma to service has resulted difficulties and challenges. As a result, the service sector has been considerably slower in embracing Six Sigma than manufacturing (Furterer and Elshennawy, 2005). Recently, more organizations across multiple industries have realized the benefits from LSS in cost reduction and customer satisfaction. 2.3 Lean Six Sigma LSS is a combination of both Lean thinking and Six Sigma to reduce waste, improve flow, reduce variation for improving customer satisfaction. While Six Sigma is based upon the reduction of unacceptable customer variation, LSS for service is a business environment methodology that maximizes shareholder value by achieving the fastest rate of

improvement in customer satisfaction, cost, quality, process speed, and invested capital (George and George, 2003). LSS for service offers large opportunity for improvement. George and George (2003) illustrated the three key reasons why service process need to apply to LSS: (1) Service processes are usually slow process, which are expensive process. (2) Service processes are slow because there is far too much “Work-in-Process” (WIP). This is from the unnecessary complexity in service/product offerings. (3) In any slow process, 80 per cent of delay is caused by less than 20 per cent of the activities. The percentage of academic articles for focused on LSS in services doubled from 20 per cent to 40 per cent during five years (Tjahjono et al., 2013). Service operations, which are comprised more than 80 per cent of GDP in the United States, is still growing rapidly worldwide (George and George, 2003). Even in the manufacturing industry, only 20 per cent of product prices are driven by direct manufacturing labor, with 80 per cent originating from indirect costs associated with support and design functions (George, 2003), such as human resources, accounting and customer relations. The literature of the application of LSS in service is primarily focused on highly repetitive service process (Nakhai and Neves, 2009), sharing a similar characteristic with manufacturing. There exists a research gap for the practical application of LSS in service process which contained high human behavior components and a variety of customer expectations. 2.4 Higher education institutions Universities are complex organizations, consuming significant resources, with a variety of business processes (Svensson et al., 2015). HEI’s have a variety of stakeholders, in lieu of a single ‘customer’ base, where administrators, faculty, staff, students, alumni, donors and tax paying citizens all having a stake in the institution. The core mission of a public HEI typically includes discovery, learning and engagement through the creation, dissemination and application of new knowledge. To support these core functions, are a variety of supporting processes that support the HEI primary function of research, education, and engagement (Svensson et al., 2015). The HEI mission has become more important since the Great Recession, where HEI value has become more important in an age of slower growth in the U.S. (Valero and VanReenen, 2016). With over 60 per cent of bachelor degrees awarded by public institutions, HEI’s are still needed to produce a skilled workforce to compete in a global economy (Laux et al., 2017). This case study is based upon a public, land grant, university in the USA; the characteristics in this section describe those of a publicly supported higher education institution (HEI), even though the same characteristics may be common among other HEI’s. 2.5 Lean Six Sigma in higher education Higher education as an industry faces many challenges that have impacted other sectors: increased cost, a reduction of resource support, declining student base, resulting in an overall context for change. In an globalized, competitive environment, HEI’s are competing for students and funding, motivated redesign business processes in order to reduce administrative overhead and improve the services delivered to students, industry partners, faculty and researchers (Svensson et al., 2015). Thus, universities are increasingly working systematically to improve business processes. The higher education environment is complex, organized with both public and private support and exhibiting variation country

Lean Six Sigma methodology

885

IJLSS 10,4

886

by country. The complex, and wide variation exhibited by HEI’s includes other barriers to adoption of a LSS philosophy. This includes basic understanding of continuous improvement, a structure of decentralization which mitigates impact of process improvement systematically, and a lack of communication, customer types/needs, visionary leadership, and management commitment (Antony et al., 2012a; Svensson et al., 2015). Antony (2012) pointed out that LSS can be a very powerful problem-solving methodology in tracking process inefficiency, however this methodology has not been widely adopted by many universes and colleges due to the traditionally misconception that it is only meant to for manufacturing companies. Additionally, the decentralized nature of traditional universities and no direct linkage to the core business of research and education also contribute to the slow adoption of LSS in HE industry (Svensson et al., 2015). 3. Case study research methodology The research methodology of this paper is based upon case research. Research methods in case include experimentation, survey, and archival analysis. Yin (2014) pointed that “How” and “Why” questions are likely to favor using a case study, experimentation or historical. If the requirements for control of human behavioral study are needed and a research study is focused on contemporary events, the research method selected should be selected, especially if the research strategy is best suited to a differing set of conditions, such as HEI’s (Yin, 1981). In this study, the following research question is asked: RQ1. How to use LSS methodology to improve process in Higher Education? Based on the research type and conditions, the qualitative research method that the authors planned a single-case study approach. The single case approach also supports readers to understand the phenomena under study, within context: how to use LSS to improve a service process in Higher Education based on the implication experience and observation in one University. According to Lee (1999), the unit of analysis in a case study is the phenomenon under study and deciding this unit appropriately is central to a research study. Yin (2009) describes a case study as an empirical inquiry that investigates a contemporary phenomenon within its real-life context. A case study entails the detailed and intensive analysis of a single case – a single organization, a single location or a single event (Bryman and Bell, 2006). The extent to which generality can be claimed from a single case study is limited, but by documenting case experiences in the light of existing literature, each case adds to the sum of knowledge available for future practitioners and researchers (Antony et al., 2012b). 4. Higher education case study The subject of this study is a Sponsored Program Services (SPS) Pre-Award office, which coordinates the administrative activities related to researchers submitting grant proposals for every academic college at a university in the USA. Sponsored projects are established when funds are awarded to the University by external sources in support of research, instruction, training, service, or other scholarly activities under an agreement. The specialists in Pre-Award office will work with PIs and the office of research and Partnership’s Proposal Development team to prepare grant applications, including proposal development and final submission. In SPS office, success is often defined by factors Critical to Quality, such as critical to schedule, service lead time and late proposal submission rate. A critical feature of this service office is that financial profit is not their priority; instead,

they are more mission driven and focus on providing better service to the staffs in this university (Figure 1). The service process for SPS office is shown in Figure 2, created with input of SPS staff. An examination of the status of the SPS office before the project revealed that current monthly workload varies between 250 and 500 proposals received per month. The variation in monthly workload resulted from proposal deadlines from different granting agencies which cannot be controlled by SPS office. However, the proposal variation impacts workers’ morale and effectiveness, efficiency in processing proposals and the ability to complete all proposals in the timeline required by the various granting agencies. Service lead time of each proposal is impacted by the complexity of the grant, the experience of the individual processing the grant, communication within the department and communication with other related departments at this University. The variation of service lead time for each case is large, with a baseline range from 4 to 80þ hours. As each proposal is received, the proposal is assigned a priority factor, which is linked to the potential award amount, though not always determined solely by award potential. The proposal process is cyclical, with proposal load varying over time. In peak months, the specialists in SPS office will process proposal according to priority factor. Monthly workload and service lead time will finally impact the KPI of SPS office, which is late proposal submission rate. The team drafted a project charter, which detailed defined the current problem to resolve in this project, based on the baseline data. Available data collected on the SPS process was analyzed by Minitab, both in descriptive and predictive analysis. Non-quantitative techniques, such as Fishbone diagram, FMEA and other typical Lean wastes were used in

Lean Six Sigma methodology

887

Figure 1. Vison of sponsored program services office

IJLSS 10,4

888

Figure 2. Pre-award proposal submission service process flow chart

analysis. Finally, on-site observation and interviews with process owners were completed to monitor and evaluate project progress. The entire SPS staff is organized into cross-functional teams with responsibility for: Proposals, Award Management, Contract and License Negotiation, Data Access and Support Services, Research Administration, Regulatory Compliance and Agricultural and International Programs. Currently, the organizational hierarchy of Pre-Award SPS basically has three levels: one director, four managers and twenty specialists. Among the total twenty specialists, eight to ten are level-one specialists; six are level-two specialists and only four of them are level three specialists (See Figure 3). Level one specialist are fairly new employees and needs training and assistance from other specialists. Level two specialist are more experienced and are able to handle common types of grant proposals. Level three specialists are senior ones and will be assigned highest complexity grant proposals. They usually will be overwhelmed by excess workload in peak times. 5. DMAIC implementation The project followed the LSS DMAIC framework and consisted of five phases: Define, Measure, Analysis, Improve and Control. Each phase is described as below, along with major milestones. 5.1 Define phase The goal of the Define phase was to have the team and sponsors reach a definition of the project through based upon scope, goals, and financial and performance targets. The “Define” stage was influential in aligning the project with the voice of the customer (VOC) and specific project outcomes. While the project sponsor communicated a key bottleneck in the system, the exact definition of the area for improvement and project goals needed to be aligned with the strategic business plan of the institution, as well as VOC. 5.1. 1 Voice of customer. Information from the customer service surveys were analyzed to identify the voice of the external customer, personnel who receive both funds and public recognition through the supporting work of the sponsored programs office. According to the

Lean Six Sigma methodology

889

Figure 3. Hierarchy of preaward SPS office

survey, the overall satisfactory of the process was good, with all the responses to this question as ‘very satisfied’. However, this may not imply the entire reality as people working with the office are usually generous about their comments, based upon informal feedback. When the LSS team investigated survey areas, the ratio of personnel whom ‘strongly agree’ that they have received effective support ‘at the beginning’ and ‘during’ the process were 61.33 per cent and 70.67 per cent, at these respective stages. In addition, there were over 23 per cent of personnel that did not ‘highly agree’ that the administrative support provided to the question that the sponsored programs office could help them save time, a crucial mission of sponsored programs. Consequently, the survey results indicated the importance of improving customer service level. 5.1.2 Project charter. In the beginning of the project, the team communicated with the office director and negotiated compromise to adjust to scope, resourcing, timing, and team membership as needed (Figure 4). 5.1.3 Problem statement and goals. In the peak months of grant application, once the number of monthly proposals is above 350, proposals will exceed the maximum capacity of the office. With the most urgent problem that SPS was facing at was lack of working time in peak months, late submissions rate would also increase accordingly. During 2015 September to 2016 August, 23 of 3997 proposals were late submissions. The impact of late grant submissions is the explicit lost time and effort of all personnel involved in developing the specific proposal. Other impacts of late submissions could be a loss of funding, personnel, and reputation to the university and respective faculty and staff. While the optimal project goal is zero “defects”, or late submissions, due to limited time, resources and uncontrollable

IJLSS 10,4

890

Business Case

Opportunity Statement

The current workload varies between 250 and 500 proposals per month, with each case requiring between 4 and 80+ hours of work. Each proposal is assigned a priority factor, which is linked to the potential amount of the award, but not always determined solely by award potential. Variations in processing times are impacted by the complexity of the grant, the experience of the individual processing the grant and communication within the department and with other related departments at Purdue. Variation in monthly workload impacts worker morale and effectiveness, efficiency in processing proposals and the ability to complete all proposals in the timeline required by the granting agency.

Current late submission rate is 0.56% and there was 20 proposal didn’t submit on time from September 2015 to September 2016. The maximum monthly capacity of the SPS office is 350, however there are around 500 proposals need to be handled during peak months. Variability in worker experience and type of proposal directly impacts the balance of the workload amongst the staff, as well as the overall capacity of the SPS department. Huge wastes and non-value added time are identified in service process.

Goal Statement

Project Scope

Improve external customer satisfaction level by decreasing late proposal submission rate from 0.56% to 0.23%

This scope is limited to the current staff within the SPS Pre-Award office and the tasks associated within that department. This project will only focus on process submission process and not on proposal review and proposal assignment.

Project Plan Acvity June DEFINE

Figure 4. Project charter

Form team Develop charter Receive chart approval MEASURE Develop staff survey Review proposal types Administer staff survey ANALYZE Analyze survey Establish proposal categories Establish skill categories IMPROVE Create skill matrix Create proposal priorizaon Create training matrix Validate matrices CONTROL Standardize priorizaon matrix Standardize skill matrix Establish training plan CLOSE OUT REPORT

July

Team Selection

Time August September October November December

Project Sponsor-Director of SPS office Project Leader-Black Belt Team Member A Team Member B Team Member C

factors outside the SPS office, a goal to reduce the yield submissions by half (0.56 per cent to 0.25 per cent), or a goal of 4.9 sigma, short term, was negotiated with the SPS leadership, and based upon conventional SS project goals (Harry and Lawson, 1992). 5.1.4 Financial benefits. A very critical feature of this Higher Education (HE) project was that it SPS is a service, mission-oriented unit, part of a larger, nonprofit, state-based institution, instead of a typical for-profit enterprise. SPS’s mission is to assist Purdue’s faculty, staff, and students in securing and managing sponsored program support, and in delivering maximum public benefit from sponsored projects. Since SPS office is a service office in a university and of the service provided, outright, tacit, financial benefits were not evaluated. The ability to meet the mission, through customer satisfaction, via late proposal submission, was the crucial measure of project success. 5.1.5 SIPOC and scope. The methodology presented by Ray and Das provides three different avenues for project selection (Ray and Das, 2010). The first looks at performance

metrics from available data. Using performance metrics, the “big Y” strategic area is connected to direct process outputs “small y’s”, relating to a product, a specific defect and the process from which that defect occurs. This first process was the methodology used by the project team. To accomplish this goal, a SIPOC chart was created to identify the stakeholders and scope in this project and is shown in Figure 5. In this project, the project team focused on the proposal submission process in terms of the major objective illustrated above. The “big Y” was late submission rate. The “small y’s”

Lean Six Sigma methodology

891

Figure 5. SIPOC model of SPS service

IJLSS 10,4

892

that contributed to late submission rate were proposal effort score, PI lead time, and capacity of the SPS office. SPS office has four Pre-Award centers on campus: College of Engineering (EPC), College of Agriculture and School of Veterinary Medicine (AVPC), SPPC (COS/COP/CHHS) – College of Science, College of Pharmacy, and College of Health and Human Sciences and CPC/DP– Central Pre-Award Center (College of Education, College of Liberal Arts, College of Technology, School of Management, Regional Campuses, and all other. Among all the months, February, June and September to November are the peak months of Sponsored Program Activity, with September encompassing the largest number of submissions per year, total. In stratifying the centers by proposal counts, the EPC Pre Award center has the highest number of proposals (Figure 6). Funding amount was also analyzed, by time to evaluate process stability. In terms of amount, there was no peak time, based on 2015-2016 in proposal amount ($) (Figure 7). 5.2 Measure phase In the Measure stage, the project team worked with the SPS office staff, measured value added process time and Non-value added activities, based upon Lean categories of waste, with the big “Y”- Late submission rate and two small “Y”s-Proposal Effort Score and PI lead time. 5.2.1 Current status for Ys. 5.2.1.1 Y-Late submission rate. The project team defined and calculated the “defect rate” in this HEI project, which is the rate of late proposal submission. During 2015 September to 2016 August, 3997 proposals were accepted, and 23 proposals were late submissions, which would result in direct failure to submit an application to granting agency for the researchers, academic department of the university and PIs even before it can be reviewed by the grant agency. Among these 3987 on time proposal submissions, “last minute” submissions occupied 19 per cent; late minute definition means the proposal was submitted one to two

Figure 6. Number of proposals in pre-award area (2015-2016)

Lean Six Sigma methodology

893

Figure 7. Amount of proposals in pre-award area (2015- 2016)

hours before the granting deadline. These last minute submissions are susceptible to high risk of being late, easily spilling over to a late submissions category (Figure 8). The numbers in column “Prior to deadlines” are the categories of the submission time prior to the deadline. 1 to 9 corresponding to different levels as follow:

Figure 8. Distribution of prior to deadlines

IJLSS 10,4

    

894

   

less than 30 min; 30-60 min; 1-2 h; 3-4 h; 4-8 h; more than 1 day; more than 1 week; not applicable; and after 5pm on due date.

The expected submission time is at least 3-4 hours before the deadline because buffer time needed for unexpected issues. Category 9 is late submission. From Figure 9, we can find that late submission will happen in peak months of proposal submitted. 5.2.1.2 y1 -PI lead time. PI lead time is the time prior to the deadline the PI requests of SPS’s support through SPS active engagement on the proposal development process. PI time refers to the time gap between the point in time that specialists in SPS office received the proposal and the proposal deadline (Figure 10). 1 to 9 corresponding to different levels as followings:  0-4 h;  4-8 h;  1 day;  2-3 days;  4-5 days;  1 week;

Figure 9. Time series plot of late submission proposal (2015-2016)

Lean Six Sigma methodology

895

Figure 10. Distribution of PL lead time

  

2-3 weeks; 1 month or more; and No hard deadline

5.2.1.3 y2 -proposal effort score. The proposal effort score is calculated by an SPS created tool, according to the complexity of each individual proposal. The SPS tool evaluates the effort needed from the specialist among 13 characteristics, including grant submission method, sponsors forms, budget forms and so on. The complexity of the proposal varies from minimum score as 1 to maximum score as 423 with and average effort score equaling 26 (Figure 11). 5.2.1.4 y3 -capacity of sponsored program services office. Currently, there is no specific data on tracking the overall work capacity of SPS office, but the director estimated an average capacity to be 350 proposals per month. This means once the number proposals exceed 350, specialists need to work over time to manage the submissions. The imbalanced workload will tend to be severe because senior specialists need to manage many more proposals that have a high effort score. 5.2.2 Value mapping. Business process mapping, using swim lanes, was developed to further understand the focus and help the improvement team to identify where the wastes and non-value added activities were added in proposal submission process. The processing time for the value added activities amounted to 4.5 hours (Figure 12). The SS team analyzed the average processing time based on different granting agency types, which includes Federal, Industrial and Foundations, University/Research Foundation, Foreign Governments and State/Local Governments. There was no statistical difference of the processing time among Sponsor types, with an average of 8 to 9 days. The overall average processing time of all the sponsor types was 9 days, or approximately 71 hours, considering the normal working hours of 8 hours per workweek. A maximum processing time and the maximum processing time needed in record was 2 months (Figure 13).

IJLSS 10,4

896

Figure 11. Descriptive statistics for proposal effort score

Figure 12. Process mapping for proposal submission process in pre-award office

After constructing the business process map of the value, the improvement team calculated the process cycle efficiency (PCE), which is also called value added ration, to evaluate the efficiency of the service process. The PCE model is shown in equation (1).

Lean Six Sigma methodology

897

Figure 13. Descriptive statistics report of processing time

Process Cycle Efficiency ðPCE Þ ¼

Value  added time Cycle time

(1)

Value added time (VA) refers to the time spent on process steps that add value to the final product from customer perspective. Non-Value added time (NVA) is the opposite. The smaller PCE means less in efficiency for this service process. Since there was no data available to track value added and cycle time for each process step, the improvement team calculate the total process cycle efficiency as follows: PCESPS service ¼

4:5 ¼ 6:4% 70:8

In this service process, PCE is only 6.4 per cent, which means there are 93.6 per cent time is NVA. In the Analysis phase, the improvement team further analyzed the waste time with the SPS office director. 5.3 Analysis phase In analysis phase, the project team investigated relationships among individual PI’s, SPS staff specialists, grant types, and other factors recorded in the data logs of the SPS office. The LSS team used a cause-and-effect (C&E) diagram to explore various waste existing in the proposal process. The data below is compiled for every proposal. The project team explored relationships among the data obtained in the initial phases of the Pre-Award services to the final complexity score to better predict the number of hours required by the individual staff member for each proposal. These potential relational factors were recorded and visualized, and can be found in Table I and Figure 14.

1 2 3 4 5 6 7 8 9 Total

Table I. Categories 1 to 4 of proposal submission prior to deadline in terms of different grant types

125 116 324 453 891 1,012 165 875 20 3,981

11 6 12 23 11 25 26 10 1 125

# of PI lead time w submission category 1 5 5 9 22 13 26 30 6 0 116

# of PI lead time w submission category 2 11 9 35 60 20 72 86 31 0 324

# of PI lead time w submission category 3 6 23 44 71 44 89 131 48 0 456

# of PI lead time w submission category 4

42 37 90 115

National Scien ce Foundation

7 10 14 27

Purdue University

898

Prior to deadline

2 2 6 17

Dpt Energy

8 3 33 56

National Institute Health

IJLSS 10,4

Lean Six Sigma methodology

899 Figure 14. Categories 1 to 4 of proposal submission prior to deadline in terms of different grant types

With an expected submission time of at least 3-4 h before the deadline, even a late submission rate is less than 1 per cent of total. Currently, 19 per cent of proposals were submitted less than 3 hours before the deadline where these proposals had high potential risk of late submission (Figure 15). The three small y’s that contributed to late submissions were: (1) PI lead time. (2) Proposal Complexity/Proposal effort score. (3) Capacity of SPS office.

Figure 15. Cumulative percentage of prior to deadline timeline

IJLSS 10,4

900

Table II. Regression analysis for proposal effort score and PI lead time with final submission prior to grant deadline

The followings are the analysis results regarding to the three small “y”s: (1) A major reason for late submission is the external SPS variable- short PI lead time. As PI lead time refers to the time that the PI contacts the SPS office before the submission deadline, this means the PI may fail to allow themselves and the SPS staff enough time for the proposal to be completed in a timely fashion. Regression relationship test result shows that the complexity of the proposal and PI lead time is statistically related to final submission prior to grant deadline (Table II). The ‘Proposal Effort Score’ is a measurement of the complexity of each proposal, which is related to submission method, sponsor forms and some other internal, non-disclosed characteristics. The value of the ‘Proposal Effort Score’ is dependent upon each case and cannot be changed. As a result, to decrease the late submission rate, the focus should be put on PI lead time because the complexity of each individual proposal is fixed. (2) Another major reason related to the internal variable is the unbalanced workload, which could contribute to a bottleneck of capacity of work. This variable is very hard to quantify but was pointed out by the top management during analysis. Senior specialists work overtime on highly complex scored proposals, while new specialists may have the capacity in time because new specialists are unable to process highly complicated proposals initially, and second, the number of complex scored proposals to match specialists is limited. Currently, there is no standard proposal assignment process for complexity. A fishbone diagram was created which analyzed the factors associated with imbalanced workload for the SPS office staff. The fishbone includes the factor groupings of those factors associated with the Principle Investigator, the SPS Staff, the existing process and the associated granting agency. These factors were selected for the number of variables which can potentially impact the complexity and time required for the submittal of a proposal. Complexity of the grant proposal, communication method between SPS staff and the PI, training to PI about the grant proposal submission are the major factors under “PI”. Training to SPS staff, especially level one specialists, prioritization of work completion sequence and motivation mechanism in SPS are the critical factors under “Staff”. Method of grant proposal assignment to different SPS staff, standard documentation method for SPS staff, assessment method of grant proposal complexity are the three major factors under “Process”. The highest dollar amount of each grant, documents required to complete a grant proposal submission and different types of grant proposal defined are the factors under “Granting Agency”. As staff members work on numerous proposals simultaneously, with multiple granting agencies and various faculty members, each of these factors not only impact complexity but also potentially impact each other. The fishbone diagram can be found in Figure 16.

Parameter Intercept Proposal_Effort_Scor PI_Leadtime

Estimate

Standard error

t value

Pr > |t|

4.143317456 0.018993017 0.281470772

0.08357348 0.00115132 0.01207366

49.58 16.50 23.31