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The purpose of this study is to investigate important factors that help explain customer willingness to adopt mobile banking (M-banking). To this end, the unified theory of acceptance and use of technology 2 (UTAUT2) was applied and to more accurately predict customer behavioral intentions, it was attempted to extend it.

The research data were collected from 396 customers of Iranian private banks who had the experience of using M-banking. The structural equation modeling technique was used to test the research hypotheses.

Findings suggest that performance expectancy, effort expectancy, social influence, facilitating conditions, habit, hedonic motivation, perceived value and trialability are endorsed as proponents of M-banking adoption intention. On the other hand, M-banking adoption intention has also had a significant positive effect on actual use behavior and word-of-mouth (WOM). WOM has also influenced actual use behavior and mediated the relationship between M-banking adoption intention and actual use behavior.

The present study focuses on private banks, therefore, although it is sufficient, it is limited to private cases. This study contributes to the literature on M-banking services and actual use behavior. By appropriately focusing on M-banking adoption intention and the service quality provided, banks can strengthen their relationships with customers, thereby stimulating actual customer behavior such as actual use behavior and WOM.

From theoretical and managerial aspects, this study has particular value for the literature on M-services’ intention in general and banking in particular. The present study provides a conceptual framework for M-banking adoption intention, which could be used in M-banking services. In addition, this study sought to extend UTAUT2 and to examine the mediating role of WOM in actual use behavior motivation as well.

This study aims to investigate the motives of mobile payment adoption from both customers' and retailers' perspectives in Sri Lanka during the COVID-19 pandemic period. It also aims to compare the motives of mobile payment adoption across rural and urban contexts.

The study employs a mixed-method approach with a concurrent research design. Both a survey of customers and in-depth interviews of managers in retail companies are used.

The study discloses that performance expectancy and facilitating conditions (PEFC), Hedonic motivation (HM) and perceived technology security (PTS) as significant motives for customers to adopt mobile payment during this pandemic period. Such findings are confirmed by the four challenges disclose by the retailers. The unfamiliarity of customers, lack of employees' knowledge on mobile payment systems, poor management orientation and lack of computer literacy of customers are the main challenges from the retailers' perspectives. Further, it shows, though PEFC is a common motive, other motives are different across rural and urban.

The findings of the study are helpful for retailers and policymakers. Retailers can develop strategies to enhance mobile payment adoption through PEFC, HM and PTS by giving special attention to the rural community. The main motive possible to use in both rural and urban contexts is PEFC. Further, retailers should take the initiatives to uplift the technological know-how of their employees while inculcating supportive management orientation. Policymakers can use this study to develop policies to enhance the community's familiarity with mobile payment technology and computer literacy.

To the best of the authors’ knowledge, this is the first study to investigate motives for adopting mobile payments from both customers' and retailers' perspectives while being the first scrutiny to compare rural and urban scenarios. The use of mixed methods with concurrent research design also contributes to originality.

This paper aims to analyze the adoption of social media platforms by tourists in Pakistan. Based on an adaptation of the technology acceptance model (TAM), this study assesses the factors that lead users to adopt these platforms.

A survey was administered to a convenience sample of 399 travelers who use social media in Pakistan. A Confirmatory factor analysis was conducted using AMOS to evaluate convergent and discriminant validity as well as composite reliability. Structural equation modeling was applied to examine the causal relationship among all proposed constructs.

The findings reveal that the perceived usefulness (PU) and perceived ease of use (PEoU) of a social media platform positively impact the behavioral intention of its users. The proposed constructs of compatibility, enjoyment, user expertise and e-trust all demonstrated their crucial roles in the adoption of a social media platform for tourism-related activities by enhancing the platform's PEoU and usefulness.

This research validates the relationship between PEoU and PU of a social media platform in the hospitality industry. Interestingly, this study has expanded TAM by validating the addition of four more constructs, (1) compatibility, (2) enjoyment, (3) e-trust, and (4) expertise, to add worth to this model regarding the understanding of social media usage in this specific industry. The findings are valuable both for managers and policymakers in the tourism sector in Pakistan, as the latter can utilize the results to entice a larger segment of social media users to the tourism industry.

This study analyzes the impact of the COVID-19 pandemic on the consumption of locally produced food. In particular, it examines an extended model of the theory of planned behavior, with the addition of the perceived risk of becoming infected with the disease, locavorism and internal locus of control.

The study employs data collected from an online panel of consumers from the five largest cities in Spain (n = 1,000). It uses partial least squares structural equation modeling (PLS-SEM) to test and validate the proposed theoretical model.

The results indicate that the perceived risk of COVID-19 drives consumers to embrace locavorism more and, although locavorism is a strong predictor of attitude, internal locus of control also has a strong impact on attitude and switching intentions. Subjective norm and attitude are strong predictors of switching intentions and purchase intentions, and switching intentions also have a powerful impact on purchase intentions.

This study extends previous research on locally produced food consumption in that it has proposed and tested a new conceptual model with the inclusion of the perceived risk of COVID-19, locavorism, internal locus of control and switching intentions, which were found to have an influence on purchasing behavior.

This paper aims to examine the impact of environmental, social and governance (ESG) disclosure and firm value (FV), as well as, pinpoints the role of the audit quality (AQ) as a moderating variable on such impact; where the authors hypothesize that AQ modulates the relationship between ESG disclosure and the FV.

Data of a sample of firms listed on the Egyptian Stock Exchange Market (EGX) were collected over the period of 2017–2021 and analyzed using the regression and 2SLS models.

The results suggested that: (1) the ESG has a significant positive impact on the FV in the EGX, and (2) AQ has a significant impact, as a moderating variable, on the relationship between ESG disclosure and FV.

The findings would help the Egyptian market authorities in realizing the importance of integrating ESG information within the financial reports of the listed firms. The findings could also help in developing effective disclosure procedures to provide shareholders with useful information.

This paper contributes to the literature regarding the ESG disclosure components and the FV value by considering AQ in testing such relationship.

This research should help determine whether development should focus on individual firms or will raising the national development level act like a rising tide and raise the performance of all corporations.

The comparative data used in this study come from 150 Australian (ASX200 index listed) firms and 150 Sri Lankan (Colombo Stock Exchange listed) firms. The research questions are answered via a quantitative research design that uses primary and secondary data.

The findings demonstrate that capital budgeting practices are more influenced by contingency features and sophistication in Australia and Sri Lanka. Also, Australian firms tend to use capital budget models with good-to-strong predictive power (except for ROE) and Sri Lankan firms tend to use capital-budget models with fair-to-poor predictive power. Further, the analysis of Australian firms yielded much stronger and more statistically significant results than the analysis of Sri Lankan firms.

In complex real-world situations, reconciling the outputs of a multifaceted approach to capital budgeting methods is more likely to give the depth and width of input needed to achieve an optimal capital investment plan.

The results of this study can provide rich information for stakeholders about new findings in capital budgeting (CB) practices and their contributions to firm performance in a comparative perspective.

The research identifies the predictors of Islamic mobile banking (IMB) smart services adoption and usage in Jordan.

Based on the Unified Theory of the Acceptance and Use of Technology (UTAUT) and the Unified Theory of the Acceptance and Use of Technology 2 (UTAUT2), an extended and modified model that encompasses perceived trust was developed. The sample comprised 358 customers from Islamic banks (IBs) in Jordan, and structural equation modelling was applied to examine data drawn from the sample.

The research framework presented 0.728% of the behavioural intention variance and 0.455% of the use behaviour. Results discovered that performance expectancy, perceived trust and hedonic motivation have significant relations with behavioural intention. The finding that effort expectancy has an insignificant effect and that social influence has a significant negative influence on behavioural intention was unexpected.

The research has successfully verified the effect of performance expectancy, perceived trust and hedonic motivation on the customer's intention to use IMB smart services. However, the research data findings are based on the cross-sectional design.

The outcomes hold implications for marketing strategy makers who are responsible for promoting IMB smart services in IBs.

This research presents a deeper insight into IMB adoption and use. The research employed UTAUT and UTAUT2 as the baseline model and incorporates perceived trust to estimate behavioural intention. To the best of the authors' knowledge, this could be the first inquiry that examines IMB smart services adoption and use in Jordan.

The purpose of this paper is to provide a historical analysis of the disaster management structure, policies and institutions in Pakistan between 1947 and 2005, and highlights the contemporary challenges in view of the learning from the past.

The paper uses a historic-integrative case study approach to disaster management and risk reduction policy, planning and practice. Qualitative data were collected through purposive sampling and a case study design was adopted. A broad range of actors was recruited as research participants. In total, 22 semi-structured in-depth interviews were conducted in relation to this study in six different districts of Pakistan to achieve insight into the role of different institutions and stakeholders.

Overall, the post-colonial flood-centric policy framework and fragmented responsibilities of different disaster management institutions show the lack of an effective institutional structure for disaster management and mitigation in Pakistan, particularly at the local level. Until the event of the 2005 earthquake, policies heavily relied on attaining immediate and short-term goals of response and relief while ignoring the long-term objectives of strategic planning for prevention and preparedness as well as capacity building and empowerment of local institutions and communities.

The analysis explains, in part, why disaster planning and management needs to be given due attention in the developing countries at different policy scales (from local to national) especially in the face of limited resources, and what measures should be taken to improve effectiveness at different phases of the disaster management cycle.

The paper advances the importance of a historical case study approach to disaster management and mitigation. The empirical work provides original research evidence about the approaches to dealing with disasters in Pakistan and thus enriches existing knowledge of disaster management policy and planning about the country.

This study aims to provide a picture of the current state of art in the use of big data for decision-making processes for the management of health-care organizations.

A systematic literature review was carried out. The research uses two analyses: descriptive analysis, describing the evolution of citations; keywords; and the ten most influential papers, and bibliometric analysis, for content evaluation, for which a cluster analysis was performed.

A total of 48 articles were selected for bibliographic coupling out of an initial sample of more than 5,000 papers. Of the 48 articles, 29 are linked on the basis of their bibliography. Clustering the 29 articles on the basis of actual content, four research areas emerged: quality of care, quality of service, crisis management and data management.

Health-care organizations believe strongly that big data can become the most effective tool for correctly influencing the decision-making processes. Thus, more and more organizations continue to invest in big data analytics, and the literature on this topic has expanded rapidly. This study seeks to provide a comprehensive picture of the different streams of literature existing, together with gaps in research and future perspectives. The literature is mature enough for an analysis to be made and provide managers with useful insights on opportunities, criticisms and perspectives on the use of big data for health-care organizations. However, to date, there is no comprehensive literature review on the big data analysis in health care. Furthermore, as big data is a “sexy catchphrase,” more clarity on its usage may be needed. It represents an important tool to be investigated and its great potential is often yet to be discovered. This study thus sheds light on emerging issues and suggests further research that may be needed.

Social networks have changed the communication patterns significantly. Information available from different social networking sites can be well utilized for the analysis of users opinion. Hence, the organizations would benefit through the development of a platform, which can analyze public sentiments in the social media about their products and services to provide a value addition in their business process. Over the last few years, deep learning is very popular in the areas of image classification, speech recognition, etc. However, research on the use of deep learning method in sentiment analysis is limited. It has been observed that in some cases the existing machine learning methods for sentiment analysis fail to extract some implicit aspects and might not be very useful. Therefore, we propose a deep learning approach for aspect extraction from text and analysis of users sentiment corresponding to the aspect. A seven layer deep convolutional neural network (CNN) is used to tag each aspect in the opinionated sentences. We have combined deep learning approach with a set of rule-based approach to improve the performance of aspect extraction method as well as sentiment scoring method. We have also tried to improve the existing rule-based approach of aspect extraction by aspect categorization with a predefined set of aspect categories using clustering method and compared our proposed method with some of the state-of-the-art methods. It has been observed that the overall accuracy of our proposed method is 0.87 while that of the other state-of-the-art methods like modified rule-based method and CNN are 0.75 and 0.80 respectively. The overall accuracy of our proposed method shows an increment of 7–12% from that of the state-of-the-art methods.