بررسی اثر ارزش درک شده بر مقاومت در برابر پذیرش فناوری بلاکچین در صنعت گردشگری
محورهای موضوعی : مديريت تکنولوژي
یزدان شیرمحمدی
1
(دانشیار، گروه مدیریت بازرگانی، دانشگاه پیام نور، تهران، ایران)
محدثه کوکبی
2
(کارشناس ارشد، دانشگاه پیام نور، گروه مدیریت جهانگردی، تهران، ایران)
سیدمحمدهادی قاضی طباطبایی
3
(دانشگاه علم و صنعت ایران، تهران، ایران)
کلید واژه: گردشگری, بلاکچین, ارزش درک شده, مقاومت, پذیرش فن‌آوری,
چکیده مقاله :
این مطالعه قصد دارد مدلی مفهومی جهت درک عوامل تاثیر گذار بر مقاومت در برابر پذیرش فن آوری بلاکچین در صنعت گردشگری را با تلفیق و توسعه مدل های قبلی ارائه تبیین و تشریح نماید. این تحقیق از روش نظرسنجی با استفاده از ابزار پرسشنامه برای گردآوری اطلاعات استفاده نموده و همچنین از مدلسازی معادلات ساختاری با رویکرد حداقل مربعات جزئی (SEM-PLS) برای تجزیه و تحلیل آماری بهره جسته است. یافته های این پژوهش اولا رابطه نظری و تئوریک بین مفاهیمی مانند ارزش درک شده و مقاومت را توسعه داده و ثانیا این رابطه را در بستری که مدیران و فعالان بخش گردشگری در آن فعالیت دارند، مورد بررسی و مطالعه قرار دهد. مدل مفهومی این پژوهش در مرحله نخست بر پایه مدل ارائه شده توسط والش و همکاران ]1[ پی ریزی گشته است. بحث در مورد یافته های تحقیق نشان می دهد که مزایای جایگزینی در ذهن مدیران و فعالان بخش گردشگری بسیار پررنگ تر از هزینه های جایگزینی فن آوری بلاکچین قابل درک است. همچنین سادگی زیرساخت یکی از عوامل کلیدی می باشد که تاثیر بالایی در درک مزایای جایگزینی و همچنین ارزش درک شده دارد. بررسی نقش مزیت عملکردی درک شده و تصویر درک شده در شکل گیری مزایای جایگزینی از جمله نوآوری های این پژوهش در تحقیقات مربوط به بکارگیری فن آوری بلاکچین در بخش گردشگری می باشد. پس می توان چنین نتیجه گیری کرد که جهت کاهش مقاومت در برابر فن آوری بلاکچین مدیران باید بیشتر بر روی مزیت های این فن آوری تاکید نمایند.
This study aims to provide a conceptual model to understand the factors affecting the resistance to the adoption of blockchain technology in the tourism industry by combining and developing previous models. This research used survey method using questionnaire tool to collect information and also used structural equation modeling with Partial Least Squares (SEM-PLS) approach for statistical analysis. The findings of this research first develop the theoretical and theoretical relationship between concepts such as perceived value and resistance and secondly examine and study this relationship in the context in which managers and activists of the tourism sector operate. In the first stage, the conceptual model of this research is based on the model presented by Walsh et al [1]. The discussion about the research findings shows that the benefits of substitution in the minds of managers and activists of the tourism sector are much stronger than the costs of substitution of blockchain technology. Also, the simplicity of the infrastructure is one of the key factors that has a high impact on understanding the benefits of replacement as well as the perceived value. Examining the role of perceived functional advantage and perceived image in the formation of alternative benefits is one of the innovations of this research in the research related to the application of blockchain technology in the tourism sector. So it can be concluded that in order to reduce resistance to blockchain technology, managers should emphasize more on the advantages of this technology.
1- Walsh, Clara, Philip O’Reilly, Rob Gleasure, John McAvoy, and Kevin O’Leary. “Understanding Manager Resistance to Blockchain Systems.” European Management Journal 39, no. 3, 353–65, 2021.
2- Colombo, Edoardo, and Rodolfo Baggio. “Tourism Distribution Channels: Knowledge Requirements.” In Bridging Tourism Theory and Practice, edited by Noel Scott, Marcella De Martino, and Mathilda Van Niekerk, 8:289–301, 2017.
3- Valeri, Marco, and Rodolfo Baggio. “A Critical Reflection on the Adoption of Blockchain in Tourism.” Information Technology & Tourism 23, no. 2, 121–32, 2021.
4- Mougayar, William. The Business Blockchain: Promise, Practice, and Application of the next Internet Technology. Hoboken, New Jersey: John Wiley & Sons, Inc, 2016.
5- Gibson, Cyrus F. “IT-Enabled Business Change: An Approach to Understanding and Managing Risk.” SSRN Electronic Journal, 47-50, 2004.
6- Khan, Kamran, and Kim Hyunwoo. “Factors Affecting Consumer Resistance to Innovation.” Master, JÖNKÖPING UNIVERSITY, 2009.
7- Chen, Chien-fei, Xiaojing Xu, and Laura Arpan. “Between the Technology Acceptance Model and Sustainable Energy Technology Acceptance Model: Investigating Smart Meter Acceptance in the United States.” Energy Research & Social Science 25, 93–104, 2017.
8- Treiblmaier, Horst. “Blockchain and Tourism.” In Handbook of E-Tourism, edited by Zheng Xiang, Matthias Fuchs, Ulrike Gretzel, and Wolfram Höpken, 1–21, 2020.
9- Calvaresi, Davide, Maxine Leis, Alevtina Dubovitskaya, Roland Schegg, and Michael Schumacher. “Trust in Tourism via Blockchain Technology: Results from a Systematic Review.” In Information and Communication Technologies in Tourism 2019, edited by Juho Pesonen and Julia Neidhardt, 304–17, 2019.
10- Revfine. “How Blockchain Technology Is Transforming the Travel Industry,” 2022.
11- Mendling, Jan, Ingo Weber, Wil Van Der Aalst, Jan Vom Brocke, Cristina Cabanillas, Florian Daniel, Søren Debois, et al. “Blockchains for Business Process Management - Challenges and Opportunities.” ACM Transactions on Management Information Systems 9, no. 1, 1–16, 2018.
12- Zeithaml, Valarie A. “Consumer Perceptions of Price, Quality, and Value: A Means-End Model and Synthesis of Evidence.” Journal of Marketing 52, no. 3, 2–22, 1988.
13- Adams, J. Stacy. “Towards an Understanding of Inequity.” The Journal of Abnormal and Social Psychology 67, no. 5, 422–36, 1963.
14- Liljander, Veronica, and Tore Strandvik. “Estimating Zones of Tolerance in Perceived Service Quality and Perceived Service Value.” International Journal of Service Industry Management 4, no. 2, 6–28, 1993.
15- Ulaga, Wolfgang, and Samir Chacour. “Measuring Customer-Perceived Value in Business Markets.” Industrial Marketing Management 30, no. 6, 525–40, 2001.
16- Rivière, Arnaud. “Towards a Model of the Perceived Value of Innovation: The Key Role of Perceived Benefits Ahead of the Adoption Process.” Recherche et Applications En Marketing (English Edition) 30, no. 1, 5–27, March 2015.
17- Kim, Moon-Koo, Jeesun Oh, Jong-Hyun Park, and Changlim Joo. “Perceived Value and Adoption Intention for Electric Vehicles in Korea: Moderating Effects of Environmental Traits and Government Supports.” Energy 159, 799–809, 2018.
18- Rosenberg, Nathan. “Factors Affecting the Diffusion of Technology.” Explorations in Economic History 10, no. 1, 3–33, 1972.
19- Rogers, Everett M. Diffusion of Innovations. 1st ed. NY: Free press, 1962.
20- Talwar, Shalini, Manish Talwar, Puneet Kaur, and Amandeep Dhir. “Consumers’ Resistance to Digital Innovations: A Systematic Review and Framework Development.” Australasian Marketing Journal 28, no. 4, 286–99, 2020.
21- Kendall, Kenneth E. “The Significance of Information Systems Research on Emerging Technologies: Seven Information Technologies That Promise to Improve Managerial Effectiveness.” Decision Sciences 28, no. 4, 775–92, 1997.
22- Laumer, Sven. “Resistance to IT-Induced Change - Theoretical Foundation and Empirical Evidence.” Phd, University of Bamberg, 2012.
23- Samhan, Bahae, and K.D. Joshi. “Understanding Electronic Health Records Resistance: A Revealed Causal Mapping Approach” 9, no. 2–3, 2017.
24- Samhan, Bahae. “Revisiting Technology Resistance: Current Insights and Future Directions.” Australasian Journal of Information Systems 22, 24-32, 2018.
25- Rogers, Everett M. Diffusion of Innovations. 1st ed. NY: Free press, 1962.
26- Fishbein, Martin, and Icek Ajzen. Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Addison-Wesley Series in Social Psychology. Reading, Mass: Addison-Wesley Pub. Co, 124-125, 1975.
27- Ajzen, Icek. “From Intentions to Actions: A Theory of Planned Behavior.” In Action Control, edited by Julius Kuhl and Jürgen Beckmann, 11–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985.
28- Davis, Fred D. “A Technology Acceptance Model for Empirically Testing New End-User Information Systems: Theory and Results.” Massachusetts Institute of Technology, 25-26, 1986.
29- Venkatesh, Viswanath, and Fred D. Davis. “A Model of the Antecedents of Perceived Ease of Use: Development and Test.” Decision Sciences 27, no. 3, 451–81, 1996.
30- Venkatesh, Viswanath, and Fred D. Davis. “A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies.” Management Science 46, no. 2, 186–204, 2000.
31- Venkatesh, Morris, Davis, and Davis. “User Acceptance of Information Technology: Toward a Unified View.” MIS Quarterly 27, no. 3, 425, 2003.
32- Kim, Hee-Woong, Hock Chuan Chan, and Sumeet Gupta. “Value-Based Adoption of Mobile Internet: An Empirical Investigation.” Decision Support Systems 43, no. 1, 111–26, 2007.
33- Venkatesh, Viswanath, and Hillol Bala. “Technology Acceptance Model 3 and a Research Agenda on Interventions.” Decision Sciences 39, no. 2, 273–315, 2008.
34- Markus, M. Lynne. “Power, Politics, and MIS Implementation.” Communications of the ACM 26, no. 6, 430–44, 1983.
35- Klaus, Timothy Paul. “An Examination of User Resistance in Mandatory Adoption of Enterprise Systems.” Phd, University of South Florida, 2005.
36- Samuelson, William, and Richard Zeckhauser. “Status Quo Bias in Decision Making.” Journal of Risk and Uncertainty 1, no. 1, 7–59, 1988.
37- Joshi, Kailash. “A Model of Users’ Perspective on Change: The Case of Information Systems Technology Implementation.” MIS Quarterly 15, no. 2, 229, 1991.
38- Kim, Hee-Woong, and Atreyi Kankanhalli. “Investigating User Resistance to Information Systems Implementation: A Status Quo Bias Perspective.” MIS Quarterly 33, no. 3, 567, 2009.
39- Ajzen, Icek. “The Theory of Planned Behavior.” Organizational Behavior and Human Decision Processes 50, no. 2, 179–211, 1991.
40- Samuelson, William, and Richard Zeckhauser. “Status Quo Bias in Decision Making.” Journal of Risk and Uncertainty 1, no. 1, 7–59, 1988.
41- Joshi, Kailash. “A Model of Users’ Perspective on Change: The Case of Information Systems Technology Implementation.” MIS Quarterly 15, no. 2, 229, 1991.
42- Joshi, Kailash. “Understanding User Resistance and Acceptance during the Implementation of an Order Management System: A Case Study Using the Equity Implementation Model” Journal of Information Technology Case and Application Research 7, no. 1, 15, 2005.
43- Kim, Hee-Woong, and Atreyi Kankanhalli. “Investigating User Resistance to Information Systems Implementation: A Status Quo Bias Perspective.” MIS Quarterly 33, no. 3, 567, 2009.
44- Walsh, Clara, Philip O’Reilly, Rob Gleasure, John McAvoy, and Kevin O’Leary. “Understanding Manager Resistance to Blockchain Systems.” European Management Journal 39, no. 3, 353–65, 2021.
45- VanderStoep, Scott W., and Deirdre D. Johnston. Research Methods for Everyday Life: Blending Qualitative and Quantitative Approaches. 1st ed. Research Methods for the Social Sciences. San Francisco, CA: Jossey-Bass, 2009.
46- Astrachan, Claudia Binz, Vijay K. Patel, and Gabrielle Wanzenried. “A Comparative Study of CB-SEM and PLS-SEM for Theory Development in Family Firm Research.” Journal of Family Business Strategy 5, no. 1, 116–28 2014.
47- Hair, Joseph F, William C Black, Barry J Babin, and Rolph E Anderson. Multivariate Data Analysis. 8th ed. United Kingdom: Cengage Learning, EMEA, 234-240, 2019.
48- Hair, Joseph F, William C Black, Barry J Babin, and Rolph E Anderson. Multivariate Data Analysis. 8th ed. United Kingdom: Cengage Learning, EMEA, 234-240, 2019
49- Hair, Joseph F., 2nd ed. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Second edition. Los Angeles: Sage, 2017.