全通路整合對品牌態度、行為意圖影響之研究：以人格特質為調節變數

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黃珉郁(Ming-Yu Huang) 查詢紙本館藏

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資訊管理學系

論文名稱

全通路整合對品牌態度、行為意圖影響之研究：以人格特質為調節變數

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至系統瀏覽論文 (2027-7-11以後開放)

摘要(中)

全通路 (omni-channel) 零售是以消費者為中心出發，將多個通路進行協同管理，提供跨通路服務的商業模式。由於透過多通路整合，可使商家在提升跨通路消費者體驗的同時，提升營運效率及績效，全通路零售逐漸受到重視。根據過去研究，高程度全通路整合所擁有的資訊一致性及服務完整性可降低消費者購物時的不安全感，並加強消費者參與，但對於全通路整合程度和消費者品牌態度間關連，以及消費者人格特質不同所造成的影響卻較少討論。因此本研究欲探討消費者於全通路商家消費時的感知資訊整合及服務整合程度是否會對其品牌態度造成影響，並觀察消費者人格特質在全通路消費環境中的調節作用，針對擁有全通路購物經驗的消費者進行問卷調查，有效樣本為252份，並運用 PLS-SEM 結構方程模式進行檢驗。研究結果顯示消費者的「感知資訊整合」及「感知服務整合」程度對消費者購物時的「心流體驗」有正向影響，而「心流體驗」會正面影響消費者對購物商家的「品牌態度」，且良好「品牌態度」會使消費者產生良好「行為意圖」。另外，本研究發現消費者的「感知資訊整合」的影響程度大於「感知服務整合」，而消費者的「神經質」人格會增強「感知服務整合對感知風險」的影響。本研究結果揭示消費者對不同全通路整合形式感受程度的差別，並呈現全通路整合程度與消費者品牌態度的關係，也揭示人格特質會部分影響其對全通路服務感受的不同，給予全通路商家提升品牌態度及發展全通路相關決策時些許參考。

摘要(英)

Omni-channel is a consumer-centric business model that integrates consumer data from multiple channels, and provides cross-channel services. Since retailers can improve cross-channel consumer experience and operational performance through omni-channel integration, omni-channel has gradually attracted attention. According to past studies, the information consistency and service integrity possessed by a high degree of omni-channel integration can reduce consumers’ insecurity when shopping and enhance consumer engagement, but few of them discuss the relationship between the degree of omni-channel integration and consumers’ brand attitudes and the impact of different consumer personality. This research intends to explore whether consumers’ perceived information integration and fulfillment integration when shopping in an omni-channel merchant will affect their brand attitudes, and to observe the impact of consumers’ personal characteristics in the omni-channel consumption environment. We conducted a questionnaire survey on consumers with omni-channel shopping experience, and there were 252 valid samples, which were tested using the PLS-SEM structural equation model. The research results show that consumers’ “perceived information integration” and “perceived fulfillment integration” have positive impact on consumers′ “flow experience” when shopping, and “flow experience” can positively affect consumer’s “brand attitude”, and good “brand attitude” lead to consumers’ good “behavioral intentions”. In addition, we found that the impact of “perceived information integration” is greater than that of “perceived fulfillment integration”, and that “neurotic” personality will enhance the impact of “perceived service integration on perceived risk”. The results of this research reveal the differences in consumers′ perceptions of different forms of omni-channel integration, and show the relationship between the degree of omni-channel integration and consumers’ brand attitudes. It also reveals that personality traits may partially affect their perceptions of omni-channel services. Retailers can make some reference when developing omni-channel related decisions so as to improve the brand attitude and operational performance of merchants.

關鍵字(中)

★ 全通路整合
★ 品牌態度
★ 心流體驗
★ 感知風險
★ 人格特質

關鍵字(英)

★ omni-channel integration
★ brand attitude
★ flow
★ perceived risk
★ personality

論文目次

摘要 ii
Abstract iii
目錄 iv
圖目錄 vi
表目錄 vii
一、緒論 1
1-1、研究背景與動機 1
1-2、研究目的 2
1-3、研究流程 2
二、文獻探討 3
2-1、全通路與全通路整合 3
2-2、心流體驗 (Flow) 4
2-3、感知風險 (Perceived risk) 6
2-4、品牌態度 (Brand attitude) 及行為意圖 (Behavioral intention) 7
2-5、人格特質 7
2-6、文獻探討統整 8
三、假說發展 10
3-1、研究模型 10
3-2、全通路整合與心流體驗 10
3-3、全通路整合與感知風險 11
3-4、心流體驗及感知風險與品牌態度 12
3-5、品牌態度及行為意圖 13
3-6、人格特質的調節作用 13
四、研究方法 16
4-1、研究設計 16
4-1-1、研究對象及抽樣方法 16
4-1-2、前測 16
4-1-3、問卷設計 20
4-1-4、問卷調查 24
4-2、資料分析方法 24
五、研究結果 25
5-1、樣本基本資料分析 25
5-2、衡量模型 26
5-2-1、信度分析 27
5-2-2、收斂效度分析 28
5-2-3、區別效度分析 31
5-2-4、共線性分析 33
5-3、結構模型及假說驗證 35
六、結論與建議 43
6-1、研究結果 43
6-2、理論及實務意涵 43
6-2-1、理論意涵 43
6-2-2、實務意涵 44
6-3、研究限制與未來研究方向 45
6-3-1、研究對象 45
6-3-2、研究內容 46
6-3-3、研究方法 46
參考文獻 47
附錄一研究問卷 52
附錄二調整後之信度分析結果 59
附錄三其餘人格調節結果 60

參考文獻

Ajzen, I. (2005). Attitudes, personality and behaviour: McGraw-hill education (UK).
Bakker, A. B., Van Der Zee, K. I., Lewig, K. A., & Dollard, M. F. (2006). The relationship between the big five personality factors and burnout: A study among volunteer counselors. The Journal of social psychology, 146(1), 31-50.
Bauer, R. A. (1960). Consumer behavior as risk taking.In Paper presented at the Proceedings of the 43rd National Conference of the June 15, 16, 17, Chicago, Illinois, 1960. American Marketing Assocation.
Chen, H., Wigand, R. T., & Nilan, M. (2000). Exploring web users’ optimal flow experiences. Information Technology & People.
Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern methods for business research, 295(2), 295-336.
Chin, W. W., & Newsted, P. R. (1999). Structural equation modeling analysis with small samples using partial least squares. Statistical strategies for small sample research, 1(1), 307-341.
Cloninger, C. R. (1999). The temperament and character inventory-revised. St Louis, MO: Center for Psychobiology of Personality, Washington University.
Cohen, J. (1988). Statistical power analysis Jbr the behavioral. Sciences. Hillsdale (NJ): Lawrence Erlbaum Associates, 18-74.
Cox, D. F., & Rich, S. U. (1964). Perceived risk and consumer decision-making—the case of telephone shopping. Journal of marketing research, 1(4), 32-39.
Csikszentmihalyi, M. (2000). Beyond boredom and anxiety: Jossey-Bass.
Csikszentmihalyi, M., & Csikzentmihaly, M. (1990). Flow: The psychology of optimal experience (Vol. 1990). New York:Harper & Row.
Csikzentmihalyi, M. (1975). Beyond boredom and anxiety: San Francisco: Jossey-Bass.
Davison, A. C., & Hinkley, D. V. (1997). Bootstrap methods and their application (No. 1). Cambridge university press.
Digman, J. M. (1990). Personality structure: Emergence of the five-factor model. Annual review of psychology, 41(1), 417-440.
Doll, W. J., Xia, W., & Torkzadeh, G. (1994). A confirmatory factor analysis of the end-user computing satisfaction instrument. MIS Quarterly, 18(4), 453-461.
Fang, Y., Qureshi, I., Sun, H., McCole, P., Ramsey, E., & Lim, K. H. (2014). Trust, satisfaction, and online repurchase intention. MIS Quarterly, 38(2), 407-A9.
Fishbein, M., Ajzen, I., & Belief, A. (1975). Intention and behavior: An introduction to theory and research.
Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics.
Forsythe, S. M., & Shi, B. (2003). Consumer patronage and risk perceptions in Internet shopping. Journal of Business research, 56(11), 867-875.
Gao, F., & Su, X. (2017). Omnichannel retail operations with buy-online-and-pick-up-in-store. Management Science, 63(8), 2478-2492.
Gefen, D. (2000). E-commerce: the role of familiarity and trust. Omega, 28(6), 725-737.
Gosling, S. D., Rentfrow, P. J., & Swann Jr, W. B. (2003). A very brief measure of the Big-Five personality domains. Journal of Research in personality, 37(6), 504-528.
Grewal, R., Cote, J. A., & Baumgartner, H. (2004). Multicollinearity and measurement error in structural equation models: Implications for theory testing. Marketing science, 23(4), 519-529.
Hair, J. F., Anderson, R. E., Babin, B. J., & Black, W. C. (2010). Multivariate data analysis: A global perspective (Vol. 7).
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19(2), 139-152.
Herhausen, D., Binder, J., Schoegel, M., & Herrmann, A. (2015). Integrating bricks with clicks: retailer-level and channel-level outcomes of online–offline channel integration. Journal of retailing, 91(2), 309-325.
Hoffman, D. L., & Novak, T. P. (1996). Marketing in hypermedia computer-mediated environments: Conceptual foundations. Journal of marketing, 60(3), 50-68.
Horton, R. L. (1976). The structure of perceived risk: Some further progress. Journal of the Academy of Marketing Science, 4(4), 694-706.
Houston, M. J., & Rothschild, M. L. (1977). A paradigm for research on consumer involvement: Graduate School of Business, University of Wisconsin-Madison.
Huang, H.-C., Huang, L.-S., Chou, Y.-J., & Teng, C.-I. (2017). Influence of temperament and character on online gamer loyalty: Perspectives from personality and flow theories. Computers in Human Behavior, 70, 398-406.
Huang, J.-H., & Yang, Y.-C. (2010). The relationship between personality traits and online shopping motivations. Social Behavior and Personality: an international journal, 38(5), 673-679.
Hui, B. S., & Wold, H. (1982). Consistency and consistency at large of partial least squares estimates. Systems under indirect observation, part II, 119-130.
Jeronimus, B. F., Riese, H., Sanderman, R., & Ormel, J. (2014). Mutual reinforcement between neuroticism and life experiences: a five-wave, 16-year study to test reciprocal causation. Journal of personality and social psychology, 107(4), 751.
Kim, M. J., Bonn, M., Lee, C.-K., & Kim, J. S. (2019). Effects of employees’ personality and attachment on job flow experience relevant to organizational commitment and consumer-oriented behavior. Journal of Hospitality and Tourism Management, 41, 156-170.
Kleinlercher, K., Emrich, O., Herhausen, D., Verhoef, P. C., & Rudolph, T. (2018). Websites as information hubs: how informational channel integration and shopping benefit density interact in steering customers to the physical store. Journal of the Association for Consumer Research, 3(3), 330-342.
Koufaris, M. (2002). Applying the technology acceptance model and flow theory to online consumer behavior. Information systems research, 13(2), 205-223.
Li, Y., & Gong, X. (2022). What drives customer engagement in omnichannel retailing? The role of omnichannel integration, perceived fluency, and perceived flow. IEEE Transactions on Engineering Management.
MacCallum, R. C., & Hong, S. (1997). Power analysis in covariance structure modeling using GFI and AGFI. Multivariate behavioral research, 32(2), 193-210.
Maignan, I., & Lukas, B. A. (1997). The nature and social uses of the Internet: A qualitative investigation. Journal of Consumer Affairs, 31(2), 346-371.
Major, D. A., Turner, J. E., & Fletcher, T. D. (2006). Linking proactive personality and the Big Five to motivation to learn and development activity. Journal of applied psychology, 91(4), 927.
Martensen, A., Grønholdt, L., Bendtsen, L., & Jensen, M. J. (2007). Application of a model for the effectiveness of event marketing. Journal of advertising research, 47(3), 283-301.
Mathwick, C., & Rigdon, E. (2004). Play, flow, and the online search experience. Journal of consumer research, 31(2), 324-332.
McDonald, R. P., & Ho, M.-H. R. (2002). Principles and practice in reporting structural equation analyses. Psychological methods, 7(1), 64.
Mitchell, A., & Olson, J. C. (1981). Are product a beliefs the only mediator of advertising effectiveness. Advances in Consumer Research, 526-531.
Novak, T. P., Hoffman, D. L., & Duhachek, A. (2003). The influence of goal‐directed and experiential activities on online flow experiences. Journal of consumer psychology, 13(1-2), 3-16.
Nunnally, J. C., & Bernstein, I. H. (1994). Psychological theory: New York: McGraw-Hill.
Reinartz, W., Haenlein, M., & Henseler, J. (2009). An empirical comparison of the efficacy of covariance-based and variance-based SEM. International Journal of research in Marketing, 26(4), 332-344.
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2017). Partial least squares structural equation modeling. Handbook of market research, 26(1), 1-40.
Song, P., Wang, Q., Liu, H., & Li, Q. (2020). The value of buy‐online‐and‐pickup‐in‐store in omni‐channel: evidence from customer usage data. Production and Operations Management, 29(4), 995-1010.
Spears, N., & Singh, S. N. (2004). Measuring attitude toward the brand and purchase intentions. Journal of current issues & research in advertising, 26(2), 53-66.
Teng, C.-I. (2011). Who are likely to experience flow? Impact of temperament and character on flow. Personality and Individual Differences, 50(6), 863-868.
Tian-Cole, S., Crompton, J. L., & Willson, V. L. (2002). An empirical investigation of the relationships between service quality, satisfaction and behavioral intentions among visitors to a wildlife refuge. Journal of Leisure research, 34(1), 1-24.
Trenz, M., Veit, D. J., & Tan, C.-W. (2020). Disentangling the impact of omni channel integration on consumer behavior in integrated sales channels. MIS Quarterly, 44(3).
Verhoef, P. C., Kannan, P. K., & Inman, J. J. (2015). From multi-channel retailing to omni-channel retailing: introduction to the special issue on multi-channel retailing. Journal of retailing, 91(2), 174-181.
Wang, S. W., Ngamsiriudom, W., & Hsieh, C.-H. (2015). Trust disposition, trust antecedents, trust, and behavioral intention. The Service Industries Journal, 35(10), 555-572.
Wu, W.-Y., & Ke, C.-C. (2015). An online shopping behavior model integrating personality traits, perceived risk, and technology acceptance. Social Behavior and Personality: an international journal, 43(1), 85-97.
Zarouali, B., Van den Broeck, E., Walrave, M., & Poels, K. (2018). Predicting consumer responses to a chatbot on Facebook. Cyberpsychology, Behavior, and Social Networking, 21(8), 491-497.
李仁豪, & 鍾芯瑜（2020）。中文版簡式 [五大人格量表] BFI 的發展。測驗學刊， 67（4），頁 271-299。
林少龍, & 紀婉萍（2018）。品牌態度及情緒依附對聯合品牌行為意圖之影響。全球管理與經濟， 14（2），頁 1-16。
楊文廣, 柳立偉, & 陳佳伶（2014）。心流體驗對觀光偏好與品牌忠誠度的影響。運動休閒餐旅研究， 9（4），頁 66-87。

指導教授

許文錦

審核日期

2022-7-15

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