本研究使用S-O-R模型並結合人際互動關係3因子‒知覺相似性、知覺專業性與知覺熟悉度，探討參加生鮮食品LINE群組團購消費者，因其他群組成員所帶來的知覺相似性、知覺專業性與知覺熟悉感，與心流體驗狀態及持續購買意願的關聯性。
本研究主要以居住在台灣大台北地區參加過生鮮食品LINE群組團購的消費者為研究對象，採立意抽樣的方式發放問卷，累計發放480份問卷，回收有效樣本435份，有效樣本回收率為90.6%。透過敘述性統計、驗證因素分析、結構性方程模型等方法，來驗證本研究所提出之各項假設。其主要研究結果如下：
1.	知覺相似性、知覺專業性與知覺熟悉度都顯著正向影響生鮮食品團購LINE群組成員的心流體驗。
2.	心流體驗對持續購買意願有顯著的正向影響。
3.	在Line生鮮食品團購群體中，知覺相似性、知覺專業度均對持續購買意願有顯著的正向影響，且知覺相似性的作用比知覺專業度的作用更重要。
4.	知覺熟悉感並不能直接增強消費者的持續購買意願。然而，知覺熟悉感確實會因消費者的心流體驗來影響消費者的持續購買意願。
綜合上述結果，LINE群組團購從業人員應掌握其所連結的個別成員連結點，增加整體群體間的熟悉度和相似度，鼓勵頻繁的成員互動，因與同類、專業的同行進行個人互動，交換資訊，會產生信任的感知，從而促進消費者的持續購買意願。

This study employs the stimulus-organism-response (S-O-R) model and combing the interpersonal interaction (perceived similarity, perceived expert, and perceived familiarity) to discuss the association among the perceived similarity, perceived expert, perceived familiarity, flow experience, and continuance purchase intention among consumers who participate in LINE group buying platforms of fresh foods.
The thesis based on the consumers who participate in LINE group buying platforms of fresh foods in Taipei city. Purposive sampling is applied in the research, a total of 480 questionnaires are distributed, and 435 valid questionnaires are returned, resulting in an effective return rate of 90.6%. Descriptive statistics, confirmatory factor analysis, structural equation modeling was used to verify the hypotheses proposed in this study.  The main research findings are the following:
1.	Perceived similarity, perceived expert, and perceived familiarity all significantly affect the flow experience of members of LINE group buying platforms of fresh foods. 
2.	Flow experience has a significant and positive influence on continuous purchase intention.
3.	In LINE group buying platforms of fresh foods, both perceived similarity and perceived expert significantly positively impact on continuous purchase intention, and the effect of perceived similarity is more important than that of perceived expert.
4.	Perceived familiarity does not directly enhance consumers’ continuous purchase intention. However, it can certainly affect the continuous purchase intention of consumers through their flow experience.
Comprehensive the above results, the LINE group buys practitioners should grasp the links of individual members, increase overall familiarity and similarity between groups, encourage members of frequent interaction, because personal interaction with peers of similar, professional, exchange of information, will produce the perception of trust, so as to promote continuous purchase intention of consumers.

目錄
目錄	V
表目錄	VI
圖目錄	VII
第一章	緒論	1
第一節	研究背景與動機	1
第二節	研究目的	3
第三節	研究章節結構	4
第二章	文獻探討	5
第一節	台灣生鮮食品團購LINE群組發展探究	5
第二節	S-O-R模型理論概述	7
第三節	研究變數間之關聯性	9
第三章	研究方法	13
第一節	研究架構及假說	13
第二節	研究假設	13
第三節	研究變數定義與衡量工具	14
第四節	研究對象與範圍	16
第五節	資料分析方法	17
第四章	資料分析結果	19
第一節	回收樣本描述	19
第二節	結構方程模型分析	20
第三節	中介效果分析	24
第五章	結論與建議	26
第一節	研究發現與管理意涵	26
第二節	研究限制	28
第三節	研究建議	28
參考文獻	31
附錄：正式問卷	40

表目錄
表 2-1 團購平台優劣比較	6
表 4-1 回收樣本描述性分析統計	19
表 4-2 驗證性分析結果彙整	21
表 4-3 研究構面的負荷量－跨負荷量矩陣	22
表 4-4 研究構面描述性統計與相關係數矩陣	22
表 4-5 研究模型路徑分析結果表	24
表 4-6 中介效果彙整表	25
表 5-1 研究假設資料彙整	26

圖目錄
圖 3-1 研究架構	13
圖 4-1 研究變數之因果關係路徑圖	23

一、中文文獻
LINE官方(2021)。台灣限定!群組新功能「LINE揪團」正式上線。LINE 媒體關係。 
   https://linecorp.com/zh-hant/pr/news/zh-hant/2021/3858，上網日期：2021年12月23日。
方世榮(1996)。製造部門角色與製造策略的實證研究—資訊/電子業。朝陽學報，第 1 期，211-238 頁。
台灣趨勢研究(2021)。第三級警戒下防疫新生活調查‒生活篇。台灣趨勢研究股韓份有限公司/趨勢洞察。 
   https://www.twtrend.com/trend-detail/COVID-19-Level3-new-life-survey01/，上網日期：2021年12 
   月23日。
李琪、李欣、魏修建(2020)。整合SOR和承諾信任理論的消費者社區團購研究。西安交通大學學報(社會科 
   學版)，第 40 期，25-35頁。
汪標(2021)。使用結構方程模型的社區團購參與意向研究。物流工程與管理，第 9期，152-155頁。
國家教育研究院。教育部重編國語辭典修訂本。https://dict.revised.moe.edu.tw/dictView.jsp? 
   ID=31917，上網日期：2021年12月25日。
張靜(2020)。SOR 模型下管道選擇對消費者購買意願的影響機制分析‒以心流體驗為中介變量。商業經濟研 
   究，第 6 期，73-75頁。
郭淑雲(2001)。消費者特性與網際網路購物意願關係之研究-以生鮮食品為例。	國立中興大學行銷學系碩 
   士論文。
蜂傳imbee網站(2021)。LINE官方帳號功能大全，行銷入門指南。 https://www.imbee.io/zh/LINE- 
    official-account/ ，上網日期：2021年12月23日。
韓金星、張喆、古晨妍(2016)。網絡團購中消費者社會互動對團購信任的影響。復旦大學管理學院，中國知 
    網期刊，第28卷第9期，148-161頁。10.14120/j.cnki.cn11-5057/f.2016.09.013
 
二、英文文獻
AI-Natour, Sameh., Benbasat, Izak., & Cenfetelli, Ronakd T. (2006). The role of design characteristics in 
  shaping perceptions of similarity: the case of online shopping assistants. Journal of the Association 
  for Information Systems, 7(12), 821-861. https://aisel.aisnet.org/jais/vol7/iss12/34/
Anderson, J.C., & Gerbing, D.W (1988). Structural equation modeling in practice: A review and 
  recommended two-step approach. Psycholgical Bulletin, 103(3), 411- 
  423.https://doi.org/10.1037/0033-2909.103.3.411
Animesh, A., Pinsonneault, A., Yang, S.B., & Oh, W. (2011). An odyssey into virtual worlds: exploring 
  the impacts of technological and spatial environments on intention to purchase virtual products. 
  MIS Quarterly, 35(3), 789-810. https://doi.org/10.2307/23042809
Bansal, H.S., & Voyer, P.A. (2000). Word-of-mouth processes within a services purchase decision 
  context. Journal of Service Research, 3(2), 166–177. https://doi.org/10.1177/109467050032005
Bilgihan, A., Nusair, K., Okumus, F., & Cobanoglu, C. (2015). Applying flow theory to booking 
  experiences: an integrated model in an online service context. Information & Management, 52 (6), 
  668–678. https://doi.org/10.1016/j.im.2015.05.005
Chen, Y.M., Hsu, T.H., & Lu, Y.J. (2018). Impact of flow on mobile shopping intention. Journal of 
  Retailing and Consumer Services, 41, 281–287. https://doi.org/10.1016/j.jretconser.2017.04.004
Congwen, D., Jiabin, Y., Shuxuan, J. (2010). The characteristics of web site and consumer online 
  shopping conduct: an empirical study based on flow experience. International Conference on 
  Logistics Systems and Intelligent Management, 3, 1774–1778.
Constant, D., Sproull, L. and Kiesler, S. (1996). The kindness of strangers: the usefulness of electronic 
  weak ties for technical advice. Organization Science, 7(2), 119- 
  135.https://doi.org/10.1287/orsc.7.2.136
Csikszentmihalyi, M. (1990). Literacy and Intrinsic Motivation. Daedalus, pp. 115–140.
Ding, D.X., Hu, P. J.-H., Verma, R., & Wardell, D.G. (2010). The impact of service system design and flow 
  experience on customer satisfaction in online financial services. Journal of Service Research, 13(1), 
  96-110. https://doi.org/10.1177/1094670509350674
Ettis, S.A. (2017). Examining the relationships between online store atmospheric color, flow 
  experience and consumer behavior. Journal of Retailing and Consumer Services, 37, 43–55. 
  https://doi.org/10.1016/j.jretconser.2017.03.007
Flowers, M.L. (1977). A laboratory test of some implications of Janis’s groupthink hypothesis. 
  Journal of Personality and Social Psychology, 35(12), 888-896. https://doi.org/10.1037/0022- 
  3514.35.12.888
Fornell, C., & Larcker, D.F. (1981). Evaluating structural equation models with unobservable variables 
  and measurement error. Journal of Marketing Research, 18(1), 39- 
  50.https://doi.org/10.1177/002224378101800104
Fu, S., Yan, Q., & Feng, G.C. (2018). Who will attract you? Similarity effect among users on online 
  purchase intention of movie tickets in the social shopping context. International Journal of 
  Information Management, 40, 88-102. https://doi.org/10.1016/j.ijinfomgt.2018.01.013
Gao, L., & Bai, X. (2014). Online consumer behaviour and its relationship to website atmospheric 
  induced flow: insights into online travel agencies in China. Journal of Retailing and Consumer 
  Services, 21(4), 653-665. https://doi.org/10.1016/j.jretconser.2014.01.001
Gao, L., & Bai, X. (2014). Online consumer behaviour and its relationship to website atmospheric 
   induced flow: insights into online travel agencies in China. Journal of Retailing and Consumer 
   Services, 21(4), 653-665. https://doi.org/10.1016/j.jretconser.2014.01.001
Gefen, D. (2000). E-commerce: the role of familiarity and trust. Omega, 28(6), 725–737. 
    https://doi.org/10.1016/S0305-0483(00)00021-9
Gilly, M.C., Graham, J.L., Wolfinbarger, M.F., & Yale, L.J. (1998). A dyadic study of interpersonal 
    information search. Journal of the Academy of Marketing Science, 25, 83-100. 
    https://doi.org/10.1177/0092070398262001
Graham, K.A., Dust, S.B., & Ziegert, J.C. (2018). Supervisor-employee power distance incompatibility, 
    gender similarity, and relationship conflict: a test of interpersonal interaction theory. Journal of 
    Applied Psychology, 103(3), 334–346. https://doi.org/10.1037/apl0000265
Guo, Y.M., & Poole, M.S. (2009). Antecedents of flow in online shopping: a test of alternative models. 
    Information Systems Journal, 19(4), 369–390. https://doi.org/10.1111/j.1365-2575.2007.00292.x.
Hair, J., Ringle, C., & Sarstedt, M. (2011). PLS-SEM: Indeed, a silver bullet. Journal of Marketing Theory 
    and Practice, 19(2), 139-152. https://doi.org/10.2753/MTP1069-6679190202
Hayes, A.W. (2018). Introduction to mediation, moderation, and conditional process analysis: A 
    regression-based approach (2nd Edition). Guilford Publications.
Hinds, P.J., Carley, K.M., Krackhardt, D., & Wholey, D. (2000). Choosing work group 
    members:balancing similarity, competence, and familiarity. Organizational Behavior and Human 
    Decision Processes, 81(2), 226-251.https://doi.org/10.1006/obhd.1999.2875
Hossain, M.S., Rahman, M.F., & Zhou, X. (2021). Impact of customers' interpersonal interactions in 
    social commerce on customer relationship management performance. Journal of Contemporary 
    Marketing Science, 4(1), 161-181. https://doi.org/10.1108/JCMARS-12-2020-0050
Hossain, M.S., Zhou, X., & Rahman, M.F. (2018). Examining the impact of QR codes on purchase 
    intention and customer satisfaction on the basis of perceived flow. International Journal of 
    Engineering Business Management, 10, 1-11. 
    https://journals.sagepub.com/doi/10.1177/1847979018812323
Hsu, C. L., Chang, K. C., & Chen, M. C. (2012). Flow experience and internet shopping behavior: 
    Investigating the moderating effect of consumer characteristics. Systems Research and Behavioral 
    Science, 29(3), 317-332. https://onlinelibrary.wiley.com/doi/10.1002/sres.1101
Hu, X., Huang, Q., Zhong, X., Davison, R.M., & Zhao, D. (2016). The influence of peer characteristics 
    and technical features of a social shopping website on a consumers purchase intention. 
    International Journal of Information Management, 36(6), 1218–1230. 	 
    https://doi.org/10.1016/j.ijinfomgt.2016.08.005
Huang, L.T. (2016). Flow and social capital theory in online impulse buying. Journal of Business 
    Research, 69(6), 2277-2283. 	https://doi.org/10.1016/j.jbusres.2015.12.042
Hyun, H., Thavisay, T., & Lee, S.H. (2021). Enhancing the role of flow experience in social media usage 
    and its impact on shopping. Journal of Retailing and Consumer Services, in press. 
    https://doi.org/10.1016/j.jretconser.2021.102492
Kelman, H.C. (1961). Processes of opinion change. Public Opinion Quarterly, 25(1), 57-78. 
    https://doi.org/10.1086/266996
Kim, T.Y., Aryee, S., Loi, R., & Kim, S.P. (2013). Person–organization fit and employee outcomes: test of 
    a social exchange model. The International Journal of Human Resource Management, 24 (19), 3719 
    –3737. https://doi.org/10.1080/09585192.2013.781522
Kim, Y.G., & Li, G. (2009). Customer satisfaction with and loyalty towards online travel products: a 
    transaction cost economics perspective. Tourism Economics, 15(5), 825- 
    846.https://doi.org/10.5367/000000009789955125
Komiak, S. Y., & Benbasat, I. (2006). The effects of personalization and familiarity on trust and 
    adoption of recommendation agents. MIS Quarterly, 30(4), 941-960. 
    https://doi.org/10.2307/25148760
Korzaan, M.L., & Boswell, K.T. (2008). The influence of personality traits and information privacy 
    concerns on behavioral intentions. Journal of Computer Inform action Systems, 48(4), 15-24. 
Lee, C.H., & Wu, J.J. (2017). Consumer online flow experience: the relationship between utilitarian and 
    hedonic value, satisfaction and unplanned purchase. Industrial Management & Data Systems, 
    177(10), 2452–2467. https://doi.org/10.1108/IMDS-11-2016-0500
Lee, S., & Kim, B.G. (2017). The impact of qualities of social network service on the continuance usage 
    intention. Management Decision, 55 (4), 701–729. https://doi.org/10.1108/md-10-2016-0731
Li, M. Choi, T.Y., Rabinovich, E., & Crawford, A. (2003). Self-service operation at retail stores: the role of 
    inter-customer interactions. Production & Operations Management, 22(4), 888-914. 
    https://doi.org/10.1111/j.1937-5956.2012.01321.x
Lim, W.M. (2014). Understanding the influence of online flow elements on hedonic and utilitarian 
    online shopping experience: a case of online group buying. Journal of Information Systems, 28(2), 
    287–306. https://doi.org/10.2308/isys-50773
Lin, J., Li, T., & Guo, J. (2021). Factors influencing consumers’ continuous purchase intention on fresh 
    food e-commerce platforms: An organic foods-centric empirical investigation. Electronic 
    Commerce Research and Applications, 50, 101103. https://doi.org/10.1016/j.elerap.2021.101103
Lin, J., Lin, S., Turel, O., & Xu, F. (2020). The buffering effect of flow experience on the relationship 
    between overload and social media users’ discontinuance intentions. Telematics and Informatics, 
    49, 101374. https://doi.org/10.1016/j.tele.2020.101374
Lin, J., Yan, Y., Chen, S., & Luo, X. (2017). Understanding the impact of social commerce website 
    technical features on repurchase intention: a Chinese guanxi perspective. Journal of Electronic 
   Commerce Research, 18 (3), 225–244.
Liu, C., Zheng, Y., & Cao, D. (2021). Similarity effect and purchase behavior of organic food under the 
    mediating role of perceived values in the context of COVID-19. Frontiers in Psychology, 12, 628342. 
    https://doi.org/10.3389/fpsyg.2021.628342
Liu, H., Chu, H., Huang, Q., & Chen, X. (2016). Enhancing the flow experience of consumers in China 
    through interpersonal interaction in social commerce. Computers in Human Behavior, 58, 306-314. 
    https://doi.org/10.1016/j.chb.2016.01.012
Liu, Y., Luo, X., & Cao, Y. (2018). Investigating the influence of online interpersonal interaction on 
    purchase intention based on stimulus-organism-reaction model. Human-Centric Computing and 
    Information Sciences, 8, 37. https://doi.org/10.1186/s13673-018-0159-0
Macias, W. (2003). A preliminary structural equation model of comprehension and persuasion of 
    interactive brand web sites. Journal of Interactive Advertising, 3(2), 36-48. 
    https://doi.org/10.1080/15252019.2003.10722072
MacKenzie, S.B., & Lutz, R.J. (1989). An empirical examination of the structural antecedents of 
    attitude toward the ad in an advertising pretesting context. The Journal of Marketing, 53, 48-65. 
    https://doi.org/10.2307/1251413
Mehrabian, A., & Russell, J.A. (1974). An approach to environmental psychology. MIT Press, 
    Cambridge, MA.
Ng, C.S.P. (2013). Intention to purchase on social commerce websites across cultures: a cross-regional 
    study. Information and Management, 50(8), 609-620. https://doi.org/10.1016/j.im.2013.08.002
Ning, L., & Wang, W. (2016). Factors affecting the loyalty of spontaneous group-buying users in 
    virtual communities from the perspective of customer experience. Journal of Northeastern 
    University (social science version), 18, 36-41.
Novak, T.P., Hoffman, D.L., & Yung, Y.F. (2000). Measuring the customer experience in online 
    environments: a structural modeling approach. Marketing Science, 19(1), 22–42. 
    https://doi.org/10.1287/mksc.19.1.22.15184
Osatuyi, B., Qin, H., Osatuyi, T., & Turel, O. (2020). When it comes to Satisfaction... it depends: an 
    empirical examination of social commerce users. Computers in Human Behavior, 111. 106413. 
    https://doi.org/10.1016/j.chb.2020.106413
Ozkara, B.Y., Ozmen, M., & Kim, J.W. (2017). Examining the effect of flow experience on online 
    purchase: a novel approach to the flow theory based on hedonic and utilitarian value. Journal of 
    Retailing and Consumer Services, 37, 119–131. https://doi.org/10.1016/j.jretconser.2017.04.001
Pavlou, P.A., & Fygenson, M. (2006). Understanding and predicting electronic commerce adoption: an 
    extension of the theory of planned behavior. MIS Quarterly, 30(1), 115-143. 
    https://doi.org/10.2307/25148720
Petty, R. E., Cacioppo, J. T., & Goldman, R. (1981). Personal involvement as a determinant of 
    argument-based persuasion. Journal of Personality and Social Psychology, 41(5), 847-855. 
    https://doi.org/10.1037/0022-3514.41.5.847
Rehman, Z.U., Baharun, R., & Salleh, N.Z.M. (2020). Antecedents, consequences, and reducers of 
    perceived risk in social media: a systematic literature review and directions for further research. 
    Psychology & Marketing, 37 (1), 74–86. https://doi.org/10.1002/mar.21281
Rockett, T.L., & Okhuysen, G.A. (2002). Familiarity in groups: exploring the relationship between inter- 
    member familiarity and group behavior. Research on Managing Groups and Teams, 4, 173-201. 
    https://doi.org/10.1016/s1534-0856(02)04008-2
Roscoe, J.T. (1975). Fundamental research statistics for the behavioral sciences. 2nd Ed. New York: 
    Holt, Rinehart and Winston. 
Preacher, K.J., & Hayes, A.F. (2008). Asymptotic and resampling strategies for assessing and 
    comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879- 
    891. https://doi.org/10.3758/BRM.40.3.879
Sadler, P., Ethier, N., & Woody, E. (2011). Interpersonal complementarity. In L. M. Horowitz & S. Strack 
    (Eds.), Handbook of interpersonal psychology: Theory, research, assessment, and therapeutic 
    interventions (pp. 123–142). John Wiley & Sons, Inc.
Sekaran, U., & Bougie, R. (2010). Research methods for business: A skill-building approach (5th ed.). 
    Haddington: John Wiley & Sons.
Shen, Y.C., Huang, C.Y., Chu, C.H., & Liao, H.C. (2010). Virtual community loyalty: an interpersonal 
    interaction perspective. International Journal of Electronic Commerce, 15(1), 49-74. https 
    ://doi.org/10.2753/JEC1086-4415150102
Sheth, J. (2020). Impact of Covid-19 on consumer behavior: will the old habits return or die? Journal 
    of Business Research, 117, 280–283. https://doi.org/10.1016/j.jbusres.2020.05.059
Shrout, P. E., & Bolger, N. (2002). Mediation in experimental and nonexperimental studies: new 
     procedures and recommendations. Psychological Methods, 7(4), 422-445. 
    https://doi.org/10.1037/1082-989X.7.4.422
Statista (2021). Penetration of leading social networks in Taiwan as of 3rd quarter 2020. Social Media 
    & User-Generated Content. https://www.statista.com/statistics/295611/taiwan-social-network- 
    penetration/. Internet Date：December 24, 2021
Tajvidi, M., Wang, Y., Hajli, N., & Love, P.E.D. (2021). Brand value co-creation in social commerce: the 
    role of interactivity, social support, and relationship quality. Computers in Human Behavior, 115, 
    105238. https://doi.org/10.1016/j.chb.2017.11.006
Tamilmani, K., Rana, N.P., Prakasam, N., & Dwivedi, Y.K. (2019). The battle of brain vs. heart: A 
    literature review and meta-analysis of ‘hedonic motivation’ use in UTAUT2. International Journal 
    of Information Management, 46, 222-235. https://doi.org/10.1016/j.ijinfomgt.2019.01.008
Teng, C.I., Huang, L.S., Jeng, S.P., Chou, Y.J., & Hu, H.H. (2012). Who may be loyal? Personality, flow 
    experience and customer e-loyalty. International Journal of Electronic Customer Relationship 
   Management, 6 (1), 20–47. https://doi.org/10.1504/IJECRM.2012.046468
Tong, Z., Xie, Y., & Xiao, H. (2021). Effect of CSR contribution timing during COVID-19 pandemic on 
    consumers’ prepayment purchase intentions: evidence from hospitality industry in China. 
    International Journal of Hospitality Management, 97, 102997. 
    https://doi.org/10.1016/j.ijhm.2021.102997
Wen, H., Jiang,Y., Wang, L., Zhou, Y., & Ma, Y. (2021). Community groupon: Interaction makes 
    customers more willing to re-purchase. 2021 10th International Conference on Industrial 
    Technology and Management (ICITM), 8-13. https://doi.org/10.1109/ICITM52822.2021.00009
Wu, I.L., Chiu, M.L., & Chen, K.W. (2020). Defining the determinants of on LINE impulse buying 
     through a shopping process of integrating perceived risk, expectation-confirmation model, and 
     flow theory issues.  International Journal of Information Management, 52, 102099. 
     https://doi.org/10.1016/j.ijinfomgt.2020.102099
Yuan, C., Moon, H., Wang, S., Yu, X., & Kim, K.H. (2021). Study on the influencing of B2B parasocial 
     relationship on repeat purchase intention in the online purchasing environment: an empirical 
     study of B2B E-commerce platform. Industrial Marketing Management, 92, 101–110. 
     https://doi.org/10.1016/j.indmarman.2020.11.008
Zhang, H., Lu, Y., Gupta, S., & Zhao, L. (2014). What motivates customers to participate in social 
     commerce? the impact of technological environments and virtual customer experiences. 
     Information and Management, 51(8),1017-1030. https://doi.org/10.1016/j.im.2014.07.005
Zhou, T. (2013). An empirical examination of continuance intention of mobile payment services. 
     Decision Support Systems, 54(2), 1085-1091. https://doi.org/10.1016/j.dss.2012.10.034
Zhu, B., Kowatthanakul, S., & Satanasavapak, P. (2020). Generation Y consumer online repurchase 
     intention in Bangkok: Based on Stimulus-Organism-Response (SOR) model. International Journal 
    of Retail & Distribution Management, 48(1), 53-69. https://doi.org/10.1108/IJRDM-04-2018-0071