Purpose: Using the theory, construct, method, moderator (TCMM) format, this framework-based review critically analyses the mobile financial services (MFSs) field through a detailed synthesis and analysis of a sample of mainstream empirical research published in various scientific journals within the period 2009–2020. Design/methodology/approach: The authors followed a three-step structured approach suggested by Webster and Watson (2002) to search for the literature to synthesise the global perspectives on MFSs and their associated applications and systems. The literature research resulted in the identification of 115 most relevant articles. Findings: The authors identified three major categories or domains within the MFSs comprising the entire spectrum of digital financial services. To facilitate the literature analysis, TCMM is developed and proposed as an organising framework. Moreover, the authors also developed and presented the comprehensive framework of MFS domains and explicitly identified 14 different research themes for future research in MFSs. Originality/value: Prior attempts to synthesise and analyse mainstream academic research in MFSs have been scant and limited to a specific MFS domain: mobile banking or mobile payment. The authors synthesised a more extensive body of knowledge and provided a global perspective on the MFS field. Unlike the past literature reviews which followed traditional frameworks such as antecedents, decisions and outcome (ADO); TCCM; and 6 W Framework (who, when, where, how, what and why), the authors developed and proposed TCMM as organising framework.

Organizations are integrating big data technologies with Enterprise Resource Planning (ERP) systems with an aim to enhance ERP responsiveness (i.e., the ability of the ERP systems to react towards the large volumes of data). Yet, organizations are struggling to manage the integration between the ERP systems and big data technologies, leading to lack of ERP responsiveness. For example, it is difficult to manage large volumes of data collected through big data technologies and to identify and transform the collected data by filtering, aggregating and inferencing through the ERP systems. Building on this motivation, this research examined the factors leading to ERP responsiveness with a focus on big data technologies. The conceptual model which was developed through a systematic literature review was tested using Structural equation modelling (SEM) performed on the survey data collected from 110 industry experts. Our results suggested 12 factors (e.g., big data management and data contextualization) and their relationships which impact on ERP responsiveness. An understanding of the factors which impact on ERP responsiveness contributes to the literature on ERP and big data management as well as offers significant practical implications for ERP and big data management practice.

Purpose: This study uses the S-O-R theory to examine how trust and outcome expectancy influence usage and advocacy. It also analyzes the moderating role of trust in a low-trust society and the differences between low-trust and high-trust consumer segments. Design/methodology/approach: Our analysis was based on a sample of 400 responses collected using a pre-tested survey during January–March 2023. Purposeful and snowball sampling techniques were used to select the study participants. The research model was estimated using the SmartPLS 4.0 statistical application. Findings: The findings revealed that outcome expectancy strongly predicts consumer trust. While word of mouth was impacted more by trust than usage behavior, trust was found to be a stronger predictor of recommendation behavior of FinTech digital payment systems than usage behavior. We distinguished between consumers with low trust (“misbelievers”) and those with high trust (“believers”). We found that the effect of outcome expectancy on usage behavior was more substantial for “misbelievers” than “believers.” Practical implications: The study’s findings have practical implications for business and marketing executives, regulators, FinTech companies and the banking and payment industry in designing strategies for gaining consumer trust, promoting consumer recommendation behavior and using FinTech innovations. Originality/value: This research’s originality lies in applying the S-O-R theory to the digital payment context within FinTech. It examined consumer trust dynamics and outcome expectancy dynamics, particularly in the underresearched setting of developing countries known for their low-trust environments. Also, the study introduces a methodological innovation by employing multigroup analysis to “decompose” moderation associations, offering more profound insights into the influence of trust on the adoption and sustained use of FinTech services.

Tourism has always been one of the most profitable service industries. But because of the COVID-19 pandemic, the tourism industry is facing some big problems. As a result, the tourism industry lost a lot of money. This paper aims to find and rank recovery solutions to help the tourism industry. This article investigates two key areas: firstly, how government aid can best be prioritised among the various subsectors of the hospitality and tourism industry, and secondly, whether public assessment of the measures the US government took against the pandemic is related to the outlook for recovery, including the role played by perceptions of government performance and efficacy at handling the crisis and self-efficacy in terms of avoiding infection. Two studies were conducted among US consumers, using different methods of data collection and analysis. The first study utilised an open market valuation technique to explore how governmental aid might be prioritised among the tourism and hospitality industries. The second study used AMOS/SEM to examine travellers’ positive perceptions of the likelihood of hospitality and tourism industry recovery. Study 1 found that all six industry subsectors investigated (hotels, airlines, restaurants, car rentals, casinos and cruise lines) had been influenced negatively by COVID-19, with the heaviest impact felt by hotels and cruise lines. Study 2 indicated that the level of public satisfaction with the US government’s performance in addressing the pandemic was positively related to expectations of hospitality and tourism industry recovery. The findings could guide policymakers in deciding how best to allocate public funds between the different subsectors of the hospitality and tourism industry.

In this era of omni-channels, firms are increasingly relying on online channel strategies to manage customer interactions (Cui et al., 2022). However, the implications of this transition for the socio-relational structure of the marketplace remain insufficiently understood, with prior studies reporting mixed findings on customer–firm relationships across channels (Cambra-Fierro et al., 2020). This study investigates how firms’ online channel strategies reshape the socio-relational structure of the marketplace using a relational norms perspective. Relationships are conceptualized as customer journeys initiated by self-construal and mediated by channel usage and service quality. Using data from 245 banking customers (Age: x̄ = 29.15 years; s = 9.88 years), results show that offline service quality develops communal and exchange relational norms. However, online channel usage negatively affects both norms, indicating detrimental effects on customer-relationship development. Communal relational norms better predict pro-firm behaviors (word of mouth (WOM) and switching intentions) than exchange norms. The study concludes that greater reliance on online channels may undermine long-term customer relationship development despite their operational benefits. The findings provide theoretical insight and strategic guidance for managers by showing that effective channel strategy should balance operational efficiency and cost-saving initiatives with long-term customer-firm relationships.