CC BY
62
6 • Маркетинговые стратегии и практики
2 DOI: 10.29141/2218-5003-2023-14-1-5 EDN:YZGDDD
3 JEL Classification: G21, M31 s
£
I Analysis of consumer behaviour variables § influencing the adoption of mobile banking
| Nigar Huseynli1, Gozde Kandemir2, Bahman Huseynli1,3
* 1 Azerbaijan State University of Economics (UNEC), Baku, Azerbaijan
2 Marmara University, Istanbul, Turkey
3 Azerbaijan Public Employment Agency, Baku, Azerbaijan
Abstract. Mobile technology advances have created additional opportunities for the banking sector and benefits for consumers. The paper examines the relationship between customer experience and satisfaction, usage intention and brand loyalty in the context of mobile banking. In particular, customer experience covers three aspects, namely enjoyment, personalization, and practicality of mobile applications. The methodological basis includes the theoretical principles of marketing and service management, as well as planned behavior theory and the technology acceptance model. The research methods of factor, linear and regression anayses were applied. The empirical basis of the study are the results of a survey of clients in ten Turkish banks. The initial sample was 411 respondents, of which 327 completed questionaires were received. The authors put forward a number of hypotheses about the impact of customer experience in mobile banking on the retention of banking services users. According to the results, all the hypotheses were supported. It was found that each of the three aspects of user experience, and practicality of mobile banking applications in particular, has a significant impact on customer satisfaction, intention to use, and brand loyalty. The findings demonstrate that mobile banking usage intention and customer satisfaction affect loyalty to the bank brand, and customer satisfaction influences mobile banking usage intention. Increasing the number of respondents and including more banks in the research are among the directions for future research.
Keywords: mobile banking; consumer behaviour; customer satisfaction; enjoyment; practicality; brand loyalty. Article info: received November 3, 2022; received in revised form December 18, 2022; accepted December 23, 2022 For citation: Huseynli N., Kandemir G., Huseynli B. (2023). Analysis of consumer behavior variables influencing the adoption of mobile banking. Upravlenets/TheManager, vol. 14, no. 1, pp. 60-73. DOI: 10.29141/2218-5003-2023-14-1-5. EDN: YZGDDD.
Влияние потребительского поведения на внедрение мобильного банкинга
Н. Гусейнли1, Г. Кандемир2, Б. Гусейнли1,3
1 Азербайджанский государственный экономический университет (UNEC), г. Баку, Азербайджан
2 Университет Мармара, г. Стамбул, Турция
3 Государственное агентство занятости Азербайджана, г. Баку, Азербайджан
Аннотация. Развитие мобильных технологий привело к появлению дополнительных возможностей в сфере банковских услуг и увеличению выгоды потребителей. Исследование направлено на установление взаимосвязи между удержанием клиентов мобильного банкинга и их пользовательским опытом, удовлетворенностью этим опытом и лояльностью бренду. Методология работы представлена теоретическими постулатами маркетинга, менеджмента услуг, а также теорией запланированного поведения и моделью принятия технологий. Применялись методы факторного, линейного и регрессионного анализа. Информационной базой послужили результаты опроса 327 пользователей мобильных приложений 10 банков Турции. Авторы выдвинули гипотезы о том, что такие аспекты опыта клиентов, как практичность банковских приложений, удовольствие от их применения и возможность персонализации, стимулируют к продолжению использования сервисов банков. Результаты проверки гипотез подтвердили их состоятельность: каждый из указанных аспектов, и в особенности практичность мобильного приложения, оказывает значительное влияние на удовлетворенность клиентов, их лояльность бренду и возможность их удержания. Установлено, что применение мобильного банковского сервиса и общее положительное впечатление от его функционала способствуют росту лояльности клиентов к банку, а удовлетворенность пользователей влияет на их намерение повторно обращаться к данному сервису. Возможным направлением дальнейшей работы может служить расширение выборки исследования.
Ключевые слова: мобильный банкинг; потребительское поведение; удовлетворенность потребителя; удовольствие от использования; практичность; верность бренду.
Информация о статье: поступила 3 ноября 2022 г.; доработана 18 декабря 2022 г.; одобрена 23 декабря 2022 г.
Ссылка для цитирования: Huseynli N., Kandemir G., Huseynli B. (2023). Analysis of consumer behaviour variables influencing the adoption of mobile banking // Управленец. Т. 14, № 1. С. 60-73. DOI: 10.29141/2218-5003-2023-14-1-5. EDN: YZGDDD.
INTRODUCTION
Mobile banking [Barnes, Corbitt, 2003] is the delivery of banking and financial services through mobile telecommunication devices. It is defined as a channel through which the customer communicates with the bank to conduct banking transactions using a mobile device (mobile phone, tablet, pocket computer, etc) [Tiwari, Buse, 2007]. By bridging the gap between clients and banks, mobile banking offers users a lot of ease. During the transition to digital banking, information technology advancements produced a competitive marketing climate in the banking industry. As a result of the adoption of a customer-focused strategy, banks started using apps that offered significant speed, cost, and time advantages. By focusing on the processes of utilizing the Internet and mobile banking more effectively and diversifying banking products, it hoped to serve a much larger customer base.
M-banking was originally used in the late 1990s by the German business Paybox in association with Deutsche Bank. It was initially implemented and tested in a number of European nations, including Germany, Spain, Sweden, Austria, and the United Kingdom. Kenya was the first developing nation to introduce M-Pesa, a text-based mobile banking service, in 2007.
M-banking is described as "a product or service given by a bank or microfinance institution to complete financial and non-financial transactions utilizing a mobile device, i.e. a mobile device such as a smartphone or tablet" by Shaikh and Karjaluoto [2015]. Compared to conventional banking channels (such as automated teller machines, telephone banking, and non-mobile internet banking), m-banking may offer additional characteristics (such as ubiquity, flexibility, and mobility) [Lin, 2013].
The creation of new goods, processes, and business models is made possible by digital technologies, which also cause drastic transformations in the existing products, processes, and business models. Since the late 1990s, the number of mobile users has significantly expanded, reaching more than one subscription per person in several nations. In Turkey, the first half of the 2000s saw a boom in subscribers, but after 2008, the pace of growth in the number of mobile users fell. Despite recent growth in subscriber numbers, China and India still have a very low number compared to other nations1. In light of technology advancements, the banking industry also makes considerable expenditures in the creation of mobile applications. The total number of consumers enrolled in the system to use the mobile banking service and who have logged in to the system at least once as of March 2020 is roughly 83 million, according to the March 2020 report of the Banks Association of Turkey2.
1 TUSIAD. (2018). Digital technologies and economic growth. Ya-yin No: TUSIAD-T/2018,10-600. (in Turkish)
2 Turkiye Bankalar Birligi (TBB). (2020). Bankalarimiz 2019. htt-ps://www.tbb.org.tr/Content/Upload/Dokuman/7678/Bankalari-miz_2019.pdf. (in Turkish)
Along with all of these, other research have been car- 3
ried out to identify the variables influencing mobile bank- I
ing and its adoption in Turkey, such as usage intention g
[Pa^an Ozcan, Sabah £elik, Ozer, 2019; Kaplan, Korkmaz, £
2020; Gursel, Yanartas, 2021; Erdogan, Eti, 2021] and at- 3
titude [Pa^an Ozcan, Sabah £elik, Ozer, 2019; Soylemez, |
Ta§kin, 2020; Gursel, Yanartas, 2021; Erdogan, Eti, 2021]. jji
Studies involving young people in banking [Can, 2019] ¡2'
U
and economic growth [Bulut, Cizgi Akyuz, 2020] were also S undertaken in addition to those on customer loyalty [Oz- it kan, Al-Futaih, 2020]. I
Additionally, no research on the aspects of fun, personalisation, and usability in mobile banking could be discovered. Accordingly, the purpose of this study is to ascertain the impact of perceived enjoyment, personalisation, and usability on customer satisfaction, usage intention, and brand loyalty in the context of mobile banking.
LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT
Mobile banking
Pa^an Ozcan, Sabah £elik, and Ozer [2019] performed a study to examine the variables influencing retail banking customers' inclinations to use mobile banking in Turkey. The research's findings indicate that the component with the greatest impact on people's intentions to use mobile banking is their attitude towards using it. It is clear that perceived utility, trust, and ease of use are each major influences on people's attitudes.
Karata§ and Bozkurt [2019] conducted a study to understand the link and impact between the factors that enable clients utilizing mobile banking to be happy with the service they receive. According to the findings, the ease of use, support offered for the service, security, performance, persuasion that the transactions are completed correctly, content of the service provided, and transaction efficiency are the factors that have the greatest impact on customer satisfaction with mobile banking.
Can [2019] did a study with the goal of examining the factors affecting young people's bank choices and making recommendations that can help banks get more market share based on their clientele. The study's findings indicate that using marketing-sales strategies rather than price policies as a competitive weapon for banks in order to rank among the top preferred banks is the most appropriate strategy.
Bulut and Cizgi Akyuz [2020] looked at the connection between online banking and economic development in Turkey. The research results show that digital banking has statistically significant short- and long-term benefits for economic growth.
Soylemez and Ta§kin [2020] seek to identify the variables influencing customers' sentiments regarding mobile banking applications and their desire to use them frequently in Turkey. The desire to use mobile banking applications consistently was shown to be positively in-
fluenced by subjective norms, attitudes, and perceived 3 behavioral control, which were experimentally evaluated £ with 466 individuals from Turkey and England. § Kaplan and Korkmaz [2020] did a research with the £ goal of determining what affects bank customers' deci-£ sion to adopt digital banking. The findings prove that < such factors as perceived trust, perceived utility, and per-S ceived usefulness significantly influence the inclination to adopt digital banking.
Gursel and Yanartas [2021] conducted a study that indicated the behavioral intentions of bank customers to accept and embrace mobile banking, employing the mobile application acceptance model. The research revealed a strong and favorable association between needs and perceived usefulness, perceived usefulness and attitude towards use, performance expectation and perceived benefit, needs and performance expectation, attitude towards use and behavioral intention.
In an investigation by Erdogan and Eti [2021], it was determined what elements could be important in influencing people's decisions to utilize mobile banking services. The study's findings revealed that attitudes towards mobile banking, convenience, and usefulness had an impact on users' intentions to use mobile banking, and that while perceived risk had an adverse effect on these variables, perceived trust had a favorable impact on convenience, usefulness, and attitude.
In a research published in 2020, Ozkan and Al-Futaih examined the elements that influence bank customers' satisfaction with mobile banking services. Consumer satisfaction with mobile banking is found to be positively correlated with customer loyalty, the benefits of using mobile banking, favorable word-of-mouth advertising about mobile banking, and customer service.
Technology acceptance model in mobile banking
Practicality (ease of use and usefulness). In order to profit from the advantages of m-banking, the system has to provide easy usage for its consumers [Ahad, Dyson, Gay, 2015, p. 5]. A person's perception that using a given system will not need any effort is referred to as perceived ease of use. The word "easy" is defined as "relief from difficulty or significant effort" in the dictionary. A system has a high perceived usefulness when a user thinks there is a good use-performance connection [Davis, Bagozzi, Warshaw, 1989]. It is more likely to be accepted if it is not viewed as being technologically difficult, according to Bryson and Atwal [2013].
Mobile money transfers include login capabilities, convenient screen sizes, simple payment processes, fast access to customer support, and few steps needed to complete a transaction. Additionally, the accessibility of mobile money transfer services will raise user perceptions of simplicity of use. It needs to be usable on mobile devices with the most fundamental capabilities and applications [Tobbin, 2010]. The phrase "degree to which a person feels that utilizing a certain system would increase
work performance" is applied to characterize perceived usefulness in this context. This is derived from the helpful word's meaning, which reads "may be utilized to benefit" [Davis, 1989].
Liao and Cheung [2008] proposed and experimentally evaluated ease of use as a measure of customer satisfaction with online banking, while Abdinnour-Helm, Chaparro and Farmer [2005] postulated and empirically tested that ease of use has a direct and positive influence on satisfaction with a commercial website. Therefore, consumer satisfaction and online banking are preceded by simplicity of use [Yoon, 2010].
Perceived usefulness is the most suitable construct in assessing the intention to use mobile Internet banking services, according to Bryson et al. [2015, p. 218], who also noted that perceived usefulness is positively associated to the intention to use mobile internet banking services. They demonstrated that perceived utility and simplicity of use were substantially linked with intention to use, supporting other research [Davis, 1989; Mahmood, Hall, Sw-anberg, 2001; Pijpers et al., 2001].
User attitudes have a major impact on people's intentions to use a gadget or service, according to Bruner and Kumar's 2003 research. Perceived utility, perceived fun, and ease of use were favorably connected with user intentions in the mobile setting, but they did not precisely assess loyalty. Similarly, Lee, Kim, and Moon [2010] found that actual use had an impact on attitudes regarding mobile Internet use and that perceived utility and convenience of use accounted for a sizable portion of that shift. These results have led to the following ideas being proposed:
H1: The perceived practicality of mobile banking applications affects customer satisfaction.
H2: The perceived practicality of mobile banking applications affects mobile banking usage intention.
H3: The perceived practicality of mobile banking applications affects loyalty to the bank brand.
Enjoyment. Enjoyment captures the enjoyable aspects of each technology as customers utilize it. The more consumers appreciate a product or service, the more likely they are to be happy and develop a habit of utilizing it [Chou et al., 2013]. Consumption, particularly in the case of hedonic applications, is a significant factor in determining technology adoption decisions, according to Ven-katesh [2012]. However, enjoyment is more noticeable during the early stages of product adoption; when the product has gained a lot of traction, utility overrides enjoyment and loses importance [Chtourou, Souiden, 2010].
The results of the study by Lin, Wu and Tsai [2005] revealed a positive relationship between users' perceived enjoyment and their contentment with a web portal. According to Davis, Bagozzi and Warshaw's [1992] research, users' workplace computer usage practices are influenced by their innate motives. Perceived entertainment has an impact on a user's attitude towards and intention to uti-
lize a mobile device, according to Moon and Kim [2001]. According to several research, the most important factor influencing technology adoption is perceived enjoyment [Dickinger, Arami, Meyer, 2008]. So it makes sense to think that one of the things motivating users' loyalty and postadoption intents for mobile app services is amusement [Lee, 2011]. The following three theories have been put up in accordance with these findings:
H4: The perceived enjoyment of mobile banking applications affects customer satisfaction.
H5: Perceived enjoyment from mobile banking applications affects mobile banking usage intention.
H6: The perceived enjoyment of mobile banking applications affects loyalty to the bank brand.
Personalization. The ability to automatically adjust (tailor) the mobile interface is made possible by the user's understanding of mobile editing, and some of these changes are linked to the content. Customization eases the limitations of the constrained visual display by filtering out extraneous information and reducing the information load. Additionally, because mobile devices always have the user-assigned identity, m-commerce offers the possibility for customisation [Lee, Benbasat, 2003]. Due to the near universal adoption of mobile commerce, many previously unsupported real-time business tasks are now accessible, and they can also be readily adapted to fit specific circumstances [Barati, Mohammadi, 2009].
Several e-commerce studies [e.g., Barkhi, Belanger, Hicks, 2008; Srinivasan, Anderson, Ponnavolu, 2002; Tarafdar, Zhang, 2008] have demonstrated that website customization provides customers with a number of advantages, including more efficient web browsing and better product-to-customer matching. These benefits have a positive impact on the website's quality, which in turn results in recurring visits from happy customers (i.e. increased customer loyalty). Based on this, personalisation has been recommended as a crucial factor for assessing loyalty [Choi et al., 2008].
Other aspects that may be customized include the colors of mobile app pages, the name they want to be welcomed by, and the order of search results depending on their past searches and purchases. For clients to customize, buy and pay for items online, businesses like Dell and Gateway provide customized page views [Chel-lappa, Sin, 2005]. Thus, it was shown that the consumers' usage intentions were impacted. The following theories have been advanced in light of these investigations in the literature:
H7: Perceived personalization from mobile banking applications affects customer satisfaction.
H8: Perceived personalization from mobile banking applications affects mobile banking usage intention.
H9: Perceived personalization from mobile banking applications affects loyalty to the bank brand.
Intention to use. Hew et al. [2016] wanted to investigate whether or not the intention of using mobile social
commerce affects brand loyalty among customers and °
the preventive role that privacy concern plays in mobile 3
social commerce usage intention in light of the gaps that I
exist in the curent literature. When the findings were ana- g
lyzed, it was shown that a person's intention to continue £
participating in mobile social commerce activities had a <
positive effect on the loyalty they felt towards a particular |
brand. In light of these findings, a hypothesis was devel- x
oped as follows: ¡2'
u
H10: Mobile banking usage intention affects loyalty to S the bank brand. H
ee
Customer satisfaction and brand loyalty £
At the beginning of the 1970s, the concept of customer enjoyment began to be recognized as a valid subject of study. Pfaff's [1972] Consumer Satisfaction Index for the United States Department of Agriculture was the first research to communicate directly to lawmakers information on the level of satisfaction felt by consumers. Olshavsky and Miller [1972] and Anderson [1973] investigated the rejected expectations of consumers and the consequences those expectations had on the assessments of the products they used. These two investigations, in conjunction with an experiment conducted by Cardozo [1964], served as the foundation for further theory testing and experimental study. In the context of this concept, satisfaction is the outcome of the buyer's purchase and use as a consequence of evaluating the returns and costs of the purchase in proportion to the expected results. In other words, satisfaction is the result of the buyer's purchase and usage. In practical terms, satisfaction is analogous to attitude in the sense that it may be seen as the aggregate level of contentment with numerous aspects of the product or service in question [Churchill, Surprenant, 1982]. In the field of marketing, the expectation validation theory proposes that customer satisfaction is a psychological state that results from a contrast between a customer's preexisting expectations and the actual performance of a product or service. This theory contends that customer satisfaction is a psychological state. Customers are considered happy with the product or service when it lives up to or exceeds their expectations. If the performance is not up to par, this will not be proven, and customers will not be happy [Jia-bao, 2011].
Oliver [1999] offered the following definition of customer loyalty: "a commitment to repurchase or become a repeat customer of a consistently favored product or service in the future, even while situational impacts and marketing efforts have the ability to change behavior."
Historically, a few authors have made the observation that contentment is an essential factor in determining whether or not a customer will repurchase or reuse a product or service (see, e.g., Anderson, Sullivan, 1993; Oliver, 1980]). Bhattacherjee [2001] focused on new technologies and found that satisfaction with past usage was the biggest predictor of users' intentions to stay with the
technology. Following are some theories that were developed on the basis of these findings:
Hn: Customer satisfaction affects mobile banking usage intention.
H12: Customer satisfaction affects loyalty to the bank brand.
RESEARCH METHODOLOGY
Purpose, variables and model of the research
The purpose of this study is to determine which elements—enjoyment, personalisation, and practicality— allow clients to be pleased with the banking industry, as well as whether they stick with these bank apps and their bank brands. The study aims to ascertain the impact of perceived enjoyment, personalisation and practicality on customer satisfaction, usage intention, and brand loyalty in mobile banking. There are six variables in the study: enjoyment, personalization, practicality, customer satisfaction, intention of use, and brand loyalty. The research has 12 hypotheses. The model of the research is given in Figure.
Practicality
Enjoyment
H 2,
H6,
H7
V..
Personalization
Customer satisfaction
H10 1
-S\-* Intention use
H11
Brand loyalty
H12
Suggested research model Модель исследования
Research design
The Technology Acceptance Model developed by Davis [1989] and the planned behavior theory developed by Ajzen [1991] were used in the creation of the theoretical model to be used in the study. The design of the research was produced and implemented in accordance with the methodology and the purpose of the research. The study was designed specifically for the banking sector. Accordingly, the statements in the scales were translated from their original language into Turkish in the context of the banking sector.
After the scale items used in the research were adapted to Turkish and the banking sector, two pre-tests were conducted. In the first pre-test, there were translation errors and disagreements during the translation into Turkish. In the pre-test performed for the second time, the relevant errors were rectified and no disagreements established, which allowed starting data collection. In the final version of the questionnaire, there were a total of 45 questions.
The online survey method was used as the data collection method in the research. A survey format was created in Google forms and the link was shared for individual customers of these banks to answer the surveys via the Internet.
Population and sample of the study
Individual consumers of the participating banks (Ak-bank, Denizbank, Garanti Bank, Halkbank, I§bankasi, QNB Finansbank, Turkiye Ekonomi Bankasi (TEB), VakifBank, YapiKredi Bankasi, and Ziraat Bankasi) make up the bulk of the research. Since it was impossible to reach every single bank client, it was unknown how many people made up the population, therefore, data collecting was done by sampling.
The research's sample procedure employed the convenience sampling approach. Only those who are easily available are included in the sample when using convenience sampling [Gegez, 2019]. Additionally, convenience sampling tries to include people from whom data and information may be conveniently gathered [Kurtulu§, 2010].
The study included a total of 411 participants, and 84 of them said, "I do not use the mobile banking application of any of these banks". As a result, 327 relevant questionnaires were completed within the parameters of the investigation.
Measures
The scales used within the scope of the research were previously accepted in the literature for validity and reliability and used in previous studies. The enjoyment scale was taken from Davis, Bagozzi and Warshaw's [1992] study and personalization scales were borrowed from the study by Harris and Goode [2010], and the loyalty scale was taken from the study of Zeithaml, Berry and Paras-uraman [1996]. To measure the practicality variable, the expressions from Davis's [1989] studies adapted by Ar-cand et al. [2017] were used. To measure satisfaction, the expressions were taken from the study by Arcand et al. [2017] based on the studies of Liang and Chen [2009] and Ping [1993]. In order to measure the intention to use, statements were formed by making use of the study by Ajzen [1991].
Analysis of data
After finishing up with the data gathering portion of the study, the next step was to evaluate the information. 327 complete and accurate questionnaires were collected as part of the study project in its entirety. The statistical tool SPSS 25.0 was used to analyze the data.
FINDINGS OF THE RESEARCH
Socio-demographic characteristics of the participants in the study
According to their gender, 58.4 % (191 people) of the respondents were men, and 41.6 %o (136 people) were women. According to their marital status, 69.4 % (227 people) were married, 28.1 % were single and 2.4 % (8 people) were others.
By age, 49.2 % (161 people) of the participants were in the age range of 26-35, 36.7 % (120 people) were between the ages of 18-25, 11.0 % (36 people) were aged 36-45, 1.5 % (5 people) were between the ages of 46-55 and 1.5 % (5 people) were between the ages of 56-65.
As for their occupations, 37.9 % (124 people) of the participants are students, 28.4 % (93 people) are public sector employees, 25.1 % (82 people) are private sector employees, 5.5 % are occupational groups consisted of 18 people, 1.2 % (4 people) retired, 1.2 % (4 people) housewives, and 0.6 % (2 people) not working.
According to their income, 27.5 % of the participants (90 people) earned 1000 TL or less, 19.3 % (63 people) had 1001-2000 TL, 14.1 % (46 people) - 2001-3000 TL, 14.1 % (46 persons) - 4001-5000 TL, 12 % (46 persons) - 40015000 TL, 5.5 % (18 persons) - 5001-6000 TL, 4.3 % (14 persons) - 7000 TL and above, 2.4 % (8 persons) declared that they were in the 6001-7000 TL income group.
According to the educational level, 46.8 % (153 people) of the participants had university degree, 27.8 % (91 people) - graduate, 12.8 % (42 people) - high school, 9.2 % - doctorate, 2.8 % (9 people) were high school graduates, and 0.6 % were primary school graduate education groups.
Validity and reliability analysis of research variables
Since all variables were taken from different scales, factor analysis was performed one by one. The results of the factor and reliability analyses are shown in Table 1.
Hypothesis tests °
It is expected that the mobile banking services offered 3 by the banks will affect the satisfaction, usage intention I and loyalty to the bank brand. As a result of the factor g analysis, multiple regression analysis was used to test the £ effects of the sub-variables (practicality, enjoyment and < personalization) of the technology acceptance model in | mobile banking on the satisfaction, intention to use and x loyalty variables. Linear regression analysis was applied ¡2' to test the effect of customer satisfaction on intention to 5 use and loyalty, as well as the effect of intention to use on it loyalty (Table 2). I
It was found that the sub-dimensions of the mobile technology acceptance model explained customer satisfaction (H1, H4, H7), intention to use (H2, H5, H8) and loyalty (H3, H6, H9) at a high level. Practicality was the most revealing among the sub-dimensions of the mobile technology acceptance model. Practicality dimension explained satisfaction (beta = 0.552), intention to use (beta = 0.537), and loyalty (beta = 0.648) at high levels.
Along with these, intention to use is expected to affect loyalty to the bank brand (H10). Linear regression analysis was performed to test the accuracy of this expectation. It was concluded that intention to use affects loyalty by 68 %. Customer satisfaction is expected to affect mobile banking usage intention (H11) and loyalty to the bank brand (H12). Linear regression analyses were performed to test the accuracy of these expectations. As a result of
Variables Items KMO Chi square df Sig. Reliability
Practicality Practicality 2 Practicality 4 Practicality 5 Practicality 3 Practicality 1 G.868 2866.782 55 G.921
Enjoyment Enjoyment 2 Enjoyment 3 Enjoyment 1 G.955
Personalization Personalization 2 Personalization 4 Personalization 3 G.8G9
Satisfaction Satisfaction 3 Satisfaction 2 Satisfaction 1 Satisfaction 5 Satisfaction 4 G.89G 1198.G83 Ю G.926
Intention use Intention use 1 Intention use 3 Intention use 2 G.765 1GG6.643 3 G.951
Loyalty Loyalty 1 Loyalty 3 Loyalty 2 G.741 632Ю62 3 G.9G2
Table 1 - Results of factor and reliability analyses Таблица 1 - Результаты факторного анализа и анализа надежности переменных
Table 2 - Results of regression analysis
g Таблица 2 - Результаты регрессионного анализа
s
Variables Dependent variables Beta t-value Sig. VIF F R R2
Enjoyment Satisfaction 0.245 5.910 0.000 1.544 192.617 0.801 0.641
Intention use 0.194 3.964 0.000 1.544 107.721 0.707 0.500
Loyalty 0.162 3.716 0.000 1.376 201.936 0.745 0.555
Practicality Satisfaction 0.552 14.005 0.000 1.402 192.617 0.801 0.641
Intention use 0.537 11.524 0.000 1.402 107.721 0.707 0.500
Loyalty 0.648 14.891 0.000 1.376 201.936 0.745 0.555
Personalization Satisfaction 0.178 4.750 0.000 1.261 192.617 0.801 0.641
Intention use 0.104 2.358 0.019 1.261 107.721 0.707 0.500
Loyalty* - - - - - - -
Satisfaction Intention use 0.823 26.159 0.000 1.000 684.270 0.823 0.678
Loyalty 0.747 20.238 0.000 1.000 409.564 0.747 0.558
Intention use Loyalty 0.684 16.910 0.000 1.000 285.937 0.684 0.468
Note. (*) As a result of the regression analysis applied to test the effect of the variables of the mobile technology acceptance model on loyalty, the p-value was greater than 0.05 (0.524), and it was excluded from the analysis on the grounds that it would not make a significant contribution to the model.
the analysis, it was found that customer satisfaction affects intention to use by 68 % and loyalty by 74 %.
Difference tests
Independent groups t-test. Independent groups t-test was applied to test whether there was a difference in the variables of practicality, enjoyment, personalization, satisfaction, intention to use and loyalty according to the gender of the participants (Table 3).
When the first column of the Levene test was checked, it was concluded that the enjoyment, practicality, personalization, satisfaction, intention to use and loyalty of women and men were equal because the p-value was greater than 0.05.
Kruskal-Wallis one-way analysis of variance. Kruskal-Wallis one-way analysis of variance was applied to test whether there was a difference in the variables of practi-
cality, enjoyment, personalization, satisfaction, intention to use and loyalty according to the participants' income, occupation, age, marital status, and frequency of use (Table 4).
Since the value is greater than 0.01 (0.060 > 0.01; 0.129 > 0.01; 0.052 > 0.01; 0.091 > 0.01; 0.028 > 0.01), the H0 hypothesis is accepted. In other words, it is accepted that there is no difference between the variables of practicality, enjoyment, personalization, intention to use and loyalty among income groups. However, according to the results of Kruskal-Wallis H test, it was seen that there was a difference between income groups and satisfaction (0.009 < 0.01). As a result of the analysis, it has been determined that income groups in the 2001-3000 TL range have a higher level of satisfaction.
Table 3 - Independent groups t-test results Таблица 3 - Результаты t-теста Стьюдента независимых выборок
Variables Participants N Mean Std. Deviation t-value p-value
Enjoyment Female 191 3.37 1.095 -.990 0.323
Male 136 3.49 1.033
Practicality Female 191 4.05 0.793 0.020 0.984
Male 136 4.05 0.934
Personalization Female 191 2.47 1.027 -1.674 0.095
Male 136 2.66 1.051
Satisfaction Female 191 3.65 0.92 -.526 0.599
Male 136 3.71 0.90
Intention use Female 191 3.82 1.007 -1.435 0.152
Male 136 3.97 0.888
Loyalty Female 191 3.98 0.928 0.046 0.963
Male 136 3.98 0.833
Table 4 - Kruskal-Wallis one-way analysis of variance results £ Таблица 4 - Результаты однофакторного дисперсионного анализа Краскела - Уоллиса 3
15
Variables Indicators Enjoyment Practicality Personalization Satisfaction Intention use Loyalty
Chi-Square 13.541 11.218 13.967 18.687 12.3G3 15.738
Income df 7 7 7 7 7 7
Asymp. Sig. .G6G .129 .G52 M9 Ю91 Ю28
Chi-Square 4.97G 1.911 3.531 5.26G 2.854 6.233
Profession df 6 6 6 6 6 6
Asymp. Sig. .548 .928 .74G .511 .827 .398
Chi-Square 5.587 2.268 3.987 2.531 2.654 7.292
Age df 4 4 4 4 4 4
Asymp. Sig. .232 .687 .4G8 .639 .617 .121
Marital status Chi-Square .44G 1.472 1.658 .96G 1.6G6 .321
df 2 2 2 2 2 2
Asymp. Sig. .8G2 .479 .436 .619 .448 .852
Chi-Square 15.144 15.876 17.G9G 12.229 21.327
Frequency df 4 4 4 4 4 4
Asymp. Sig. ^4 M3 Ю29 M2 .G16
The significance of the Kruskal-Wallis H test for profession. The H0 hypothesis was accepted because the value was greater than 0.01 (0.548 > 0.01; 0.928 > 0.01; 0.740
> 0.01; 0.511 > 0.01; 0.827 > 0.01; 0.398 > 0.01). In other words, it is accepted that there is no difference between practicality, enjoyment, personalization, satisfaction, intention to use and loyalty according to occupational groups.
The significant of the Kruskal-Wallis H test for age. The H0 hypothesis was accepted because the value was greater than 0.01 (0.232 > 0.01; 0.687 > 0.01; 0.408 > 0.01; 0.639 > 0.01; 0.617 > 0.01; 0.121 > 0.01). In other words, it is accepted that there is no difference between practicality, enjoyment, personalization, satisfaction, intention to use and loyalty according to age groups.
The significant of the Kruskal-Wallis H test for marital status. The H0 hypothesis was accepted because the value was greater than 0.01 (0.802 > 0.01; 0.479 > 0.01; 0.436
> 0.01; 0.619 > 0.01; 0.448 > 0.01; 0.852 > 0.01). In other words, it has been proven that there is no difference between practicality, enjoyment, personalization, satisfaction, intention to use and loyalty according to marital status.
The significant H0 hypothesis is accepted because its value is greater than 0.01 in the variables of practicality (0.003 > 0.01) and enjoyment (0.004 > 0.01). In other words, it is accepted that there is no difference between the variables of practicality and enjoyment among income groups. However, the applied Kruskal-Wallis H test sig. H0 hypothesis is rejected because the customization value is large (0.029 > 0.01). In other words, it is accepted that there is a difference between the personalization variable according to the frequency of use. Sig. of the Kruskal-Wallis H test. Since its value is less than 0.01 (0.002 < 0.01;
0.002 < 0.01; 0.016 > 0.01), the H0 hypothesis is rejected. Thus, it was found that there was a difference between the variables of enjoyment, practicality, satisfaction and usage intentions according to the frequency of use. It is seen that individuals who use mobile applications every day perceive more enjoyment and practicality and also have a higher level of satisfaction and loyalty.
DISCUSSION AND CONCLUSION
Factor analysis was conducted to test the sub-dimensions of the technology acceptance model. KMO sample adequacy test and Bartlett sphericity test were applied to test the suitability of the data set for factor analysis. Since the KMO value was above 0.50 and the Bartlett test was significant at 0.05 significance level, the data set was found suitable for factor analysis (KMO = 0.868; Bartlett test (55) = 2866,782; p = 0.000). As a result of factor analysis, three factors consisting of 11 questions were obtained. The total explained variance was found to be 79.994 %. The factors were named as practicality, enjoyment and personalization, respectively.
As a result of the factor analysis performed separately for customer satisfaction, intention to use and loyalty question groups, it was revealed that this question group consisted of a single dimension (Customer satisfaction KMO = 0.890, Bartlett test (10) = 1198.083, p = 0.000; Intention to use KMO = 0.795, Bartlett test (3) = 1006.643, p = 0.000; Loyalty KMO = 0.741, Bartlett test (3) = 632.062, p = 0.000).
Regression analysis was applied to see the effect of practicality, enjoyment and personalization variables, which are sub-dimensions of the mobile technology acceptance model obtained as a result of factor analysis, on customer satisfaction and intention to use variables.
It was found that the technology acceptance satisfaction 3 sub-dimensions (practicality, enjoyment and personaliza-£ tion) explained customer satisfaction and usage intention g at a high level. Regression analysis was applied to see the £ effect of the variables of practicality, enjoyment and per-£ sonalization, which are the sub-dimensions of the mobile | technology acceptance model obtained as a result of the Is factor analysis, on the loyalty variable. The loyalty variable was found to explain the technology acceptance satisfaction sub-dimensions (practicality and enjoyment) at a high level. As a result of the regression analysis applied to test the effect of the variables of the mobile technology acceptance model on loyalty, the p-value was greater than 0.05 (0.524), and it was excluded from the analysis on the grounds that it would not make a significant contribution to the model. Personalization variable has no effect on loyalty. Regression analysis was performed for each of the 12 hypotheses. According to the results of the analyses, each of the 12 hypotheses was supported.
Users aim to reach brief information about their individual needs with mobile technologies and are offered a quick solution opportunity depending on these needs in businesses. For this reason, mobile banking applications are frequently preferred when it comes to performing bank transactions. Since mobile banking applications can be accessed anytime and anywhere, their practicality should be emphasized. The fact that an application does not contain complex menus and steps, adapts to the mobile devices used, and provides maximum efficiency
by not spending much time while using the application causes a willingness to utilize the application and a tendency to maintain these behaviors.
It is revealed that personalization of the offered mobile banking applications makes users satisfied with the application and encourages its further use. At this point, it is important for businesses to achieve positive results by presenting an integrated system with the big data obtained in order to make choices suitable for the information from the users, their needs or their tastes. Since all operations can be done through these applications, the fact that the applications that are spent a lot of time give pleasure to the user increases the satisfaction and creates a positive effect on their usage intentions, and in this way, they tend to continue these behaviors in the future. Hence, it is possible to say that the results of this study provide valuable information for banks to improve customer relations, mobile marketing strategies and services.
The study was prepared in compliance with the research technique and does have certain restrictions and limitations. The research is only undertaken for individual consumers in the banking industry, thus it does not include the entire population of bank customers. This is the first restriction of the research, which is also the most significant. In addition, when the total number of branches in the banking industry was taken into account, the study was carried out on ten different banks; nevertheless, it does not cover all banks in Turkey.
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Источники
Abdinnour-Helm S.F., Chaparro B.S., Farme S.M. (2005). Using the end-user computing satisfaction (EUCS) instrument to measure satisfaction with a web site. Decision Sciences, vol. 36, pp. 341-364. DOI: 10.1111/j.1540-5414.2005.00076.x Ahad M.T., Dyson L.E., Gay V. (2012). An empirical study of factors influencing the SME's intention to adopt m-banking in rural Bangladesh. Journal of Mobile Technologies, Knowledge and Society, vol. 2012, 508433, pp. 1-16. DOI: 10.5171/2012.508433 Ajzen I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, vol. 50, no. 2, pp. 179-211.
https://doi.org/10.1016/0749-5978(91)90020-T Anderson E.W., Sullivan M.W. (1993). The antecedents and consequences of customer satisfaction for firms. Marketing Science, vol. 12, no. 2, pp. 125-143.
Anderson R.E. (1973). Consumer dissatisfaction: The effect of disconfirmed expectancy on perceived product performance.
Journal of Marketing Research, vol. 10, no. 1, pp. 38-44. https://doi.org/10.2307/3149407 Arcand M., PromTep S., Brun I., Rajaobelina L. (2017). Mobile banking service quality and customer relationships. International
Journal of Bank Marketing, vol. 35, no. 7, pp. 1068-1089. https://doi.org/10.1108/IJBM-10-2015-0150 Barati S., Mohammadi S. (2009). An efficient model to improve customer acceptance of mobile banking. In: World Congress on Engineering and Computer Science, 2, pp. 20-22.
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Barnes S.J., Corbitt B. (2003). Mobile banking: Concept and potential. International Journal of Mobile Communications, vol. 1, no. 3, § pp. 273-288. DOI: 10.1504/IJMC.2003.003494 «
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Bruner G., Kumar A. (2003). Explaining consumer acceptance of handheld internet devices. Journal of Business Research, vol. 58, | no. 5, pp. 115-120. J
Bryson D., Atwal G. (2013). Antecedents of attitude towards the adoption of internet banking in Senegal. Journal of innovation SE Economics & Management, vol. 1, no. 11, pp. 33-54. https://doi.org/10.3917/jie.011.0033 |
Bryson D., Atwal G., Chaudhuri H.R., Dave K. (2015). Understanding the antecedents of intention to use mobile internet bank- u ing in India: Opportunities for microfinance institutions. Strategic Change, vol. 24, no. 3, 207-224. https://doi.org/10.1002/ Sc jsc.2005 I
Bulut E., Cizgi Akyüz G. (2020). The relationship between digital banking and economic growth in Turkey. Marmara Üniver-sitesi iktisadi ve idari Bilimler Dergisi - Marmara University Journal of Economic and Administrative Sciences, vol. 42, no. 2, pp. 223-246. https://doi.org/10.14780/muiibd.854325 Can Y. (2019). Causes of bank preferences of university students in Turkey and suggestions to the banks to be the bank of
youth. BDDKbankacilik ve finansalpiyasalar dergisi/Journal of BRSA Banking and Financial Markets, vol. 13, no. 1, pp. 1-36. Cardozo R. (1964). Customer satisfaction: Laboratory study and marketing action. Journal of Marketing Research, vol. 2, pp. 244-249.
Chellappa R.K., Sin R.G. (2005). Personalization versus privacy: An empirical examination of the online consumer's dilemma.
Information Technology and Management, vol. 6, no. 2, pp. 181-202. Choi J., Seol H., Lee S., Cho H., Park Y. (2008). Customer satisfaction factors of mobile commerce in Korea. Internet Research,
vol. 18, no. 3, pp. 313-335. https://doi.org/10.1108/10662240810883335 Chou C.H., Chiu C.H., Ho C.Y., Lee J.C. (2013, June). Understanding mobile apps continuance usage behavior and habit:
An expectance-confirmation theory. In: PACIS (pp. 132-142). Chtourou S., Souiden N. (2010). Rethinking the TAM model: Time to consider fun. Journal of Consumer Marketing, vol. 27, no. 4,
pp. 336-344. https://doi.org/10.1108/07363761011052378 Churchill Jr G.A., Surprenant C. (1982). An investigation into the determinants of customer satisfaction. Journal of Marketing
Research, vol. 19, no. 4, pp. 491-504. https://doi.org/10.2307/3151722 Davis F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly,
vol. 13, no. 3, pp. 319-340. https://doi.org/10.2307/249008 Davis F.D., Bagozzi R.P., Warshaw P.R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, vol. 35, no. 8, pp. 982-1003. Davis F.D., Bagozzi R.P., Warshaw P.R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, vol. 22, no. 14, pp. 1111-1132. https://doi.org/10.1111/j.1559-1816.1992.tb00945.x Dickinger A., Arami M., Meyer D. (2008). The role of perceived enjoyment and social norm in the adoption of technology with network externalities. European Journal of information Systems, vol. 17, no. 1, pp. 4-11. https://doi.org/10.1057/palgrave. ejis.3000726
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Gursel E., Yanartas M. (2021). The use of mobile application acceptance model in mobile banking. Research Journal of Business
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Information about the authors Информация об авторах
Nigar Huseynli
PhD (Finance), Lecturer of Business Administration Dept. Azerbaijan State University of Economics (UNEC), Baku, Azerbaijan. E-mail: [email protected]
Gözde Kandemir
PhD (Marketing), Free Researcher. Marmara University, Istanbul, Turkey. E-mail: [email protected]
Bahman Huseynli
PhD candidate (Marketing), Lecturer of Business Administration Dept. Azerbaijan State University of Economics (UNEC), Baku, Azerbaijan; Chief Specialist of Labour Market Analysis Dept. Azerbaijan Public Employment Agency, Baku, Azerbaijan. E-mail: bahmanhuseynli@ gmail.com
о
Гусейнли Нигар ^
PhD (Финансы), преподаватель кафедры управления бизнесом. ^
Азербайджанский государственный экономический универ- о
ситет (UNEC), г. Баку, Азербайджан. E-mail: [email protected] се
Кандемир Гёзде |
PhD (Маркетинг), научный сотрудник. Университет Мармара, s
г. Стамбул, Турция. E-mail: [email protected] х
Гусейнли Бахман ЦЦ
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Соискатель степени PhD (Маркетинг), преподаватель кафедры ¡IS
управления бизнесом. Азербайджанский государственный |
экономический университет (UNEC), г. Баку, Азербайджан; глав- S^ ный специалист отдела анализа рынка труда. Государственное агентство занятости Азербайджана, г. Баку, Азербайджан. E-mail: [email protected]
