PurposeThe primary objective of this study is to prioritize the main intentions behind investment in cryptocurrency, in spite of its volatile nature and no regulatory framework.Design/methodology/approachThis research paper has worked on collective constructs of the unified theory of acceptance and use of technology (UTAUT), the technology acceptance model (TAM) and social support theory with an added construct of financial literacy. A fuzzy analytical framework has been applied to prioritize the intentions of investors.FindingsThe result indicates that “Social Influence (SI)” is the most influencing factor, while “Effort Expectancy (EE)” is the least influencing factor considered by investors. The subdimensions ranked in the top priority by investors are as follows: “I want to invest in cryptocurrencies because I have a good level of financial knowledge (FL1)”; “The people who are important to me will think that I should use cryptocurrencies (SI2)”; “I have the necessary resources to use cryptocurrencies (FC2).” The least importance is given to “It will be easy for me to become an expert in the use of cryptocurrencies (EE3).”Research limitations/implicationsFew of the constructs of the UTAUT, the TAM and social support theory have been considered while prioritizing intentions. Different other intentions also prevail under different theories that need to be researched further.Practical implicationsUnlike previous studies, this research adds the archetype of social commerce, social support and utility theories to analyze and prioritize the behavioral perspective of using cryptocurrencies in digital transactions.Originality/valueThis paper fills the gap in the research study, along with assisting the regulators and cryptocurrency practitioners to widen their knowledge base and to recognize the prioritized intentions.

The paper evaluates the levels of smartphone addiction among students using statistical and fuzzy analysis approaches. By using Smartphone Addiction Test (SAT) scores, the data were treated using fuzzy operators, and the relationships were analyzed by applying the statistical techniques. The respondent's addiction level scores were converted into a fuzzy environment through the triangular approach, and demographic characteristics of the respondents were compared to levels of smartphone addiction. The percentage of respondents with mild and moderate levels of addiction to smartphones was found to be high in the study. Gender, age groups, and years of usage were also associated with addiction to smartphones. However, the level of smartphone addiction was not much different based on whether the students are staying with parents or away from their parents. Among the dimensions measuring smartphone addiction, “lack of control” was ranked first. The dimension, “excessive use,” was found to be the second‐highest influencer of smartphone addiction level. Respondents, to no small extent, were students pursuing undergraduate and graduate programs. Adolescents and other demographic groups can be considered for future studies. Future studies can also focus on using other fuzzy approaches to evaluate the data on smartphone addiction. The study offers prioritized dimensions to be focused on containing the levels of addiction among individuals. Moreover, the ranking of items helps in guiding individuals to look into the problem areas and take the appropriate actions. Measurement of levels of smartphone addiction using a novel hybrid combination of statistical approach and triangular fuzzy approach was carried out in the study. The critical dimensions and items showing the influence on smartphone addiction were ranked.

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The paper examines the performance of Indian private sector banks based on various combinations of multi‐criteria decision‐making techniques. Annual reports of the respective banks were used to capture the data and measure the performance by considering multiple inputs and outputs. The data are analyzed through notable multiple criteria decision‐making (MCDM) techniques, CRITIC, TOPSIS, and grey relational analysis (GRA). The study reveals that HDFC is the best performing bank among other private sector banks and creates a benchmark. Applying the combination of MCDM techniques, namely CRITIC‐TOPSIS and CRITIC‐GRA, HDFC is ranked first followed by Bandhan Bank as the second. Other banks are ranked differently due to methodological differences. After obtaining the ranks by CRITIC‐TOPSIS and CRITIC‐GRA, the ranks are tested using the Wilcoxon signed‐rank test. Only private sector banks are considered for the current study. Future studies on the performance of banks can be taken up by comparing different types of banks, targeting an extension of the time frame studied. The findings suggest that the private sector banks need to increase their performance by investing in income‐generating areas. Improving the performance will help them in surviving in the market as well as competing with the top public sector and foreign banks. The findings can also be useful to various stakeholders and, in particular, to investors to know the value of the banks for future investments. For the first time, researchers have used a combination of MCDM techniques such as CRITIC‐TOPSIS and CRITIC‐GRA. Selected inputs and outputs have been studied for the private sector banks using MCDM techniques to measure the performance.

PurposeThe main objective of this study is to compare the service quality of two retail chains of hypermarkets, namely, Big Bazaar and Spencer's, using the trapezoidal fuzzy approach.Design/methodology/approachCustomers from Big Bazaar and Spencer's of Andhra Pradesh, India, have been surveyed through a well-designed questionnaire. The study attempts to compare the service quality of two major retail giants (Spencer's and Big Bazaar) in Andhra Pradesh by using the trapezoidal fuzzy approach to prioritize the attributes of service quality of retail outlets.FindingsThe result of the study indicates that the expectations of Big Bazar customers are higher as compared to Spencer's. Further, the study reveals – that Spencer's need to improve in the dimension of tangibility while Big Bazar needs to focus more on responsiveness.Research limitations/implicationsAs the data taken for the study are primary in nature, chances of bias may arise on the part of respondents, which may affect the validity of results. Further, the study is confined to two retail stores in Andhra Pradesh, India only, which may not reflect the broader picture.Practical implicationsRetailers may provide more importance to two major service quality dimensions, i.e. tangibility and responsiveness while preparing for their service and marketing strategies.Originality/valueAs the study relates to the comparative analysis of service quality of Big Bazar and Spencer's, the findings will be of additional value to these specific retailers. Therefore, it is expected that this study will fill the gap in the literature by prioritizing the expectations and perceptions of customers of Big Bazar and Spencer's.