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Влияние цифровой компетентности потребителей на принятие цифровых экосистем (на примере Сбера)
Цель: исследовательская работа направлена на выяснение роли цифровых компетенций в поведении потребителей в контексте моделей принятия технологий.
Задачи (сформулированы как исследовательские вопросы):
• Какова роль цифровой компетенции в процессе принятия решений цифровой экосистемы?
• Насколько конкретно цифровая компетенция участвует в процессе принятия решений цифровой экосистемы?
Результаты:
• Доказано, что уровень цифровой компетентности потребителей влияет на такие конструкции, как ожидаемая производительность, ожидаемая работа, предполагаемый риск и благоприятные условия, что позволяет получить ответы на поставленные вопросы исследования.
• Доверие и безопасность вместе с ожидаемой производительностью были определены как наиболее влиятельные движущие силы для поведенческого намерения использовать технологическое решение, добавляя существенный эффект посредничества отношения к бренду.
• На основе результатов эмпирических исследований были разработаны и рассмотрены дальнейшие теоретические и практические выводы
INTRODUCTION …………………………………………………………………………………………………………… 7 Research gap…………………………………………………………………………………………………………….7 Research problem ……………………………………………………………………………………………………..8 Research questions ……………………………………………………………………………………………………9
CHAPTER 1. ECOSYSTEM PHENOMENON IN BUSINESS. ………………………………………….10
1.1 Defining the digital business ecosystem phenomenon. ……………………………………………….10
Business ecosystem evolution …………………………………………………………………………………..10
Digital business ecosystem……………………………………………………………………………………….15
Non-financial industries form ecosystems ………………………………………………………………….15
1.2 Technology acceptance models’ overview………………………………………………………………..17
The Technology Acceptance Model (TAM) overview …………………………………………………17
The Unified Theory of Acceptance and Usage of Technology (UTAUT) overview …………20
1.3 Digital Competence. ………………………………………………………………………………………………21 Summary of Chapter 1…………………………………………………………………………………………………27 CHAPTER 2. DEVELOPMENT OF THE RESEARCH MODEL. ………………………………………30
Sber ecosystem as a phenomenon in the banking industry ……………………………………………30 2.1 Development of the theoretical framework and research propositions. …………………………32 Dimensions of UTAUT 2 …………………………………………………………………………………………32 Additional factors influencing the Sber’s digital ecosystem acceptance. ………………………..36 2.3 Methodology…………………………………………………………………………………………………………37 Research design and development……………………………………………………………………………..37 Questionnaire development ………………………………………………………………………………………38 Sampling method and sample size……………………………………………………………………………..41 Data collection and analysis ……………………………………………………………………………………..42 Summary of Chapter 2…………………………………………………………………………………………………43 CHAPTER 3. DATA ANALYSIS AND PRACTICAL IMPLICATIONS…………………………….45 3.1 Data analysis…………………………………………………………………………………………………………45 Obtained sample. …………………………………………………………………………………………………….45 Descriptive statistics ………………………………………………………………………………………………..46 3.2 Research model and hypothesis testing…………………………………………………………………….47 Model fit and quality indexes. …………………………………………………………………………………..49 3.3 Discussion of the results…………………………………………………………………………………………53 Data-based model and its interpretation ……………………………………………………………………..53 Further theoretical implication ………………………………………………………………………………….55
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Further practical implication …………………………………………………………………………………….55
Limitations and further research………………………………………………………………………………..56 Summary of Chapter 3…………………………………………………………………………………………………57 CONCLUSION ……………………………………………………………………………………………………………..58 REFERENCES ………………………………………………………………………………………………………………59 APPENDICES ………………………………………………………………………………………………………………. 66 Appendix 1. Questionnaire…………………………………………………………………………………………..66 Appendix 2. List of Hypotheses. …………………………………………………………………………………..75 Appendix 3. Descriptive statistics …………………………………………………………………………………77 Appendix 4. Correlation matrix…………………………………………………………………………………….79 Appendix 5. Model fit and quality output from WarpPLS 7.0…………………………………………..79
Research gap
Consumer behavior is a widely studied topic in the marketing area since the 1930s. It is defined as the study of individuals, groups of people, or organizations and the activities associated with the purchase, use, and elimination of goods and services. This study includes consumers’ emotional, mental, and behavioral responses (Fullerton, 2013). So, consumer behavior is a concept that provides for different aspects and theories.
With the advancement of technologies, the business map has inevitably been changed. The introduction of the Internet has impacted the process of decision-making by consumers. The buying process has changed, pushing back the face-to-face communication with sellers, and replacing it with dynamic information presentation through digital channels (Suleman, et al., 2019). What is more important, recent events show how fast the traditional ways of doing business are replaced with new digital solutions (McKinsey & Co., 2020). These changes have dramatically affected both businesses and customers. Thus, consumer behavior has also changed, and new models for predicting consumer behavior have appeared since then.
Moreover, there are several models for understanding and predicting consumer behavior. Most of the studies of the use of technological products are based on several theories. The Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT) are the most popular of them. Thus, it is vital to investigate these models to choose the most appropriate one for further research.
Furthermore, consumer behavior differs in different contexts. Such variables as age, gender, and nationality play a significant role in making decisions (Williams, et al., 2015). On the other hand, the variables mentioned are not the only influencers. There are many factors affecting decision-making by target consumers, such as the confident usage of digital technologies for information, communication, and basic problem-solving in different aspects of real life. From this point, this research suggests investigating the role of one of the eight key competencies, according to The European Parliament and the Council as of December 30, 2006, on the technology acceptance by consumers – digital competence.
Digital competence is often described as “a set of knowledge, skills, and attitudes associated with the use of digital technology in individual’s goals fulfillment” (Baartman & de Bruijn, 2011; Ferrari et al., 2012). It is significant to focus on the current continuously changing digital environment and learn to choose and use technological solutions appropriately. Different researchers insist that ICT skills are the basic need to function in society, the crucial condition for life, and even substantial background to survive (Ferrari, 2012).
Currently, there is a lack of investigations on the impact of the individual level of digital competence on consumer acceptance of digital solutions. The research mainly focused on the influence of personal innovativeness, digital self-efficacy, or digital savviness (Jin, 2013; Sell et al., 2014; McDonald, Uncles, 2007). Therefore, the possible impact of digital competence on the overall model of technology acceptance has to be investigated. Existent literature lacks the possible linkage between digital competence and any of the technology acceptance models. Thus, the lack of knowledge on the relationships between digital competence and the consumers’ digital technology acceptance represents this master’s thesis research gap.
Research problem
Nowadays technology is rapidly evolving bringing up new innovative solutions for everyday interactions. Businesses do not only compete with each other but cooperate and share customers to provide greater value in the end (Tikhonova, 2019; Zakharov, et al., 2019; Dneprov & Mikhaylyuk, 2019; RBC Trends, 2021). Therefore, the phenomenon of business ecosystems appeared. The business ecosystem was defined as “a network of organizations and individuals in the business community which together create a system of mutual support as well as evolve together” (Moore, 1993).
There are a lot of notions and concepts the business ecosystem phenomenon contains (Rong, et al., 2017; Wulf & Butel, 2017; Iansiti & Levien, 2004; Kapoor & Agarwal, 2017; Lee, et al., 2017; Joo, et al., 2017; Valkokari, 2015; Gomes, et al., 2018; Attour & Lazaric, 2020). What is more, due to the global transition towards the digital economy, the phenomenon changed its paradigm. Digital intelligence along with the support of big data becomes the central factor of production fostering the switch from traditional resources to intellectual ones (Dneprov & Mikhaylyuk, 2019). Digital platforms became the core for the development of digital business ecosystems. Various industries’ organizational forms are digital platforms (Faik & Asadullah, 2018).
What is more, the financial industry is one of the first industries affected by new technology because of rapidly developing non-financial platforms (Bubnova, 2020; RBC Trends, 2021). These platforms forced banks to rethink their core businesses. Currently, banks are on the way to change their business model offering not only financial services but extending their operations to adjacent industries and far more (Kleiner, et al., 2020; Bubnova, 2020).
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Sber is the largest Russian financial institution that historically offered mainly banking services. However, it has recently transformed into a huge ecosystem with more than fifty non- financial companies included in it (RBC Trends, 2021; Sberbank.ru). Sber’s case is considered special because it is the only banking company that offers such a huge and complex business ecosystem solution extending its operations to food delivery, entertainment offering, and more other services consolidated under the brand of Sber (RBC Trends, 2021).
As the user’s level of digital competence is expected to affect consumer perception of highly technological goods and services, the recently formed digital business ecosystem solution provided by Sber is the most appealing case to study. Therefore, the research problem of this master’s thesis is investigating the impact of consumer digital competence on acceptance of digital ecosystem solutions using the case of Sber.
Research questions
As this paper aims to find out the role of digital competence on consumer behavior in the context of the technology acceptance models, the following research questions are set:
RQ1: What is the role of digital competence in the digital ecosystem solutions acceptance process?
RQ2: How specifically does digital competence participate in the digital ecosystem solutions acceptance process?
The study investigates the digital competence effect on the technology acceptance model by investigating Sber’s digital ecosystem solutions acceptance. Moreover, the research integrates digital competence with the technology acceptance model to explore the area of interest. Therefore, the findings expected as the result of the study are of exploratory type.
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