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This study aims to develop an assessment scale for university service quality (university SQ) and examine University Service Quality assessment model (UNIQUAL) of higher education during the pandemic.

Two studies applied a mixed-method design to develop and validate the UNIQUAL scale. In-depth interviews and literature reviews were conducted to refine the initial dimensions and items of UNIQUAL in Study 1. Item analysis, EFA and CFA were then conducted to purify item refinement, scale refinement, purification and validation in Study 2. Finally, a confirmed UNIQUAL model was analyzed via partial least squares structural equation modeling (PLS-SEM) using Smart-PLS 4.0.

The research confirms the four-factor structure of UNIQUAL, with a total of 16 items, to be a valid and reliable scale for the assessment of the service quality (SQ) of universities. Having adopted the bias-corrected and accelerated (BCa) bootstrap approach to study 5,000 subsamples from 27 countries, the authors found “responsiveness” and “empathy” to be significantly associated and have positive relationships with students' satisfaction with university SQ. Furthermore, university SQ and satisfaction were mediated by “health and safety” concerns.

The newly developed UNIQUAL scale would be of value to educators and authorities of higher education to assess the SQ of their universities to enhance the effectiveness of student learning. The improvement in satisfaction with higher education's SQ ultimately helps in retaining both international and local students amidst concerns about traveling and studying during the pandemic.

COVID-19 has affected the private and public sectors worldwide. Millions of students have been affected by schools being shut down and substituted with distance-learning programs. Thus, the assessment of the quality of university services has become an important support mechanism for retaining the sustainability of higher education.

The UNIQUAL scale provides a conceptual model and validates an assessment tool. The research hypotheses confirm the relationship between university SQ and satisfaction from the perspective of international students.

The incentive cost of enterprises increases significantly with the rapid growth of the social commerce (SC) market. In this context, enterprises need to develop the optimal strategy to improve incentive effectiveness and reduce cost. Different types of consumers’ responses to incentives bring different values to enterprises. Hence, this paper proposes the social commerce value network (SCVN) to help enterprises study the contributions of different types of consumers to the network.

Based on the graphical evaluation and review technique (GERT), the authors construct the social commerce value GERT (i.e. SCV-GERT) network and design three progressive experiments for estimating the value contributions of “network stage”, “consumer type”, and “resource type” to the SCVN under the same incentives. The authors initialize the SCV-GERT model with consumer data in SC and distinguish the most valuable consumers by adjusting the incentive parameters.

The results show that the SCV-GERT model can well describe the value flow of SCVN. The incentive on forwarding consumers brings the greatest value gain to the SCVN, and social trust contributes the most to forwarding consumers.

Under the guidance of the results, platforms and enterprises in SC can select the optimal type of consumers who bring the maximum network value so as to improve the effectiveness of incentive strategy and reduce marketing costs. A four-level incentive system should be established according to the ranking of the corresponding value gains: forwarding consumers > agent consumers > commenting consumers > potential consumers. Enterprises also need to find ways to improve the social resource investments of consumers participating in SC.

This paper investigates the incentive problem in SC grounded in the SCVN and uses the GERT method to construct the SCV-GERT model, which is the first attempt to introduce GERT into the SC context. This study also makes up for the lack of comparative research on different types of consumers in SC and can provide support for enterprises’ customer relationship management and marketing decisions.

Using features of social media, peer-to-peer (P2P) mobile payment enables users to foster social interaction every time transactions are made. Given the increasing popularity of social features in P2P mobile payment applications, it is worth understanding how these components contribute to users’ switching behavior between conventional mobile payment and P2P mobile payment services. By treating sociability of P2P mobile payment as a pull factor, this study aims to extend the push–pull–mooring framework in the context of P2P mobile payment.

A questionnaire survey was conducted to obtain data. Respondents from the USA were exclusively selected due to the emerging number of P2P mobile payment users and the volume of transactions in this country. Based on a sample of 232 Amazon Mechanical Turk mobile payment users, the authors tested the hypotheses using the partial least squares structural equation model technique with SmartPLS software version 3.

The finding reveals that sociability is triggered by social presence, social benefit and social support within the P2P mobile payment platform. Moreover, dissatisfaction with perceived enjoyment of conventional mobile payment (push factor), customer innovativeness (mooring factor) and sociability of P2P mobile payment (pull factor) jointly influence users’ intention to switch to P2P mobile payment services, and subsequently drive their migration behavior.

Unlike past research that mainly focuses on utilitarian-related factors, to the best of the authors’ knowledge, this study is among the first to thoroughly examine the sociability features of P2P mobile payment service as a form of a social-centric system.

In recent years, the proliferation of social commerce (s-commerce) has attracted many researchers to investigate the drivers of individuals' intentions. However, the empirical results reported in these studies were fragmented and inconsistent. This has led various meta-analyses to synthesize these findings, but without including a large number of s-commerce studies. In addition, investigating meta-analytically the effects of moderators such as the six dimensions of Hofstede's national culture is still lacking.

Drawing on nine theories and models, this meta-analysis aims to summarize the findings reported in 109 s-commerce studies published between 2011 and 2021 and to examine the moderating role of national culture. The correlation coefficient (r) has been used as the main effect size for this study. Based on the random-effects method, the CMA V3 software has been employed to calculate the weighted mean effect sizes.

The meta-analysis results showed that all the 11 hypothesized direct relationships are positive and significant. The moderator results also revealed that five out of six cultural dimensions significantly moderate the examined associations.

This research serves to enrich the existing s-commerce literature by addressing contradictory and mixed results reported in the empirical studies. This study is one of the first of its kind to investigate the role of Hofstede's six cultural dimensions as moderators in the field of s-commerce using the meta-analytic techniques.

This study aims to contribute to the ongoing discussion of how consumers approach shopping directly via social media by explicating decisions related to trust on social media and how various types of electronic word of mouth (eWOM; ratings and reviews, recommendations and referrals and social media communities) contribute to these decisions.

A total of 198 respondents with experience shopping directly via social media were instructed to select the platform on which they had the most shopping experience (Facebook, Instagram, TikTok, Pinterest, etc.) to answer relevant survey questions. Structural equation modeling (SEM) was used for analyzing the results.

Findings reveal that eWOM provided by strong ties (recommendations and referrals; social media communities) is superior to weak ties (ratings and reviews). Surprisingly, ratings and reviews do not significantly relate to trust in the retailer, suggesting that this information may be necessary but not sufficient for trusting retailers on social media.

This research distinguishes trust decisions related to shopping on social media and clarifies how each type of eWOM uniquely influences trust in the retailer by using tie strength theory.

Based on uncertainty reduction theory, this study explores how perceived information transparency mitigates users' perceived uncertainty, which in turn influences their actual purchase behavior. In addition, the moderating effects of cultural tightness on the relationship between perceived information transparency and perceived uncertainty are also considered.

Users with the shopping experience on Xiaohongshu are invited to participate in the survey. Finally, 355 valid longitudinal data are collected.

The results indicate that the three dimensions of perceived information transparency (i.e. perceived product transparency, perceived seller transparency and perceived transaction transparency) can reduce users' perceived uncertainty significantly. Besides, the negative impacts of perceived product and seller transparency on users' perceived uncertainty are stronger when cultural tightness is higher. However, cultural tightness does not moderate the relationship between perceived transaction transparency and users' perceived uncertainty.

First, the authors' research extends the uncertainty reduction theory to the context of social commerce. Second, the authors' research explores the boundary condition under which perceived information transparency varies by identifying cultural tightness as the moderator of the relationship between perceived information transparency and uncertainty. Third, the authors' research enriches the understanding of the cultural tightness of China.

Despite being one of the most used dependent variables in expatriate management research, no clear-cut understanding exists of what expatriate success means. Thus, this study aims to propose an integrative definition of expatriate success by providing an overview of expatriate success's dimensions, antecedents, and their interplay.

A systematic literature review (SLR) was conducted to achieve the purpose. A total of 249 empirical studies (quantitative 111, qualitative 50, mixed-methods 17), literature reviews (67) and meta-analyses (4) on expatriate success were reviewed from Web of Science and Scopus databases published from 1990 until December 2021. The study selection criteria followed the PRISMA flowchart steps, and then descriptive and network analyses were performed to identify expatriates' success dimensions, antecedents and their interplay.

The findings show the interplay among antecedents and dimensions of expatriate success across three levels (individual, interpersonal and organisational) to clarify the concept of expatriate success. Also, the study offers a comprehensive definition of expatriate success based on the dimensions identified.

The suggested definition of expatriate success elucidates the “atheoretical”, multidimensional and socially constructed nature of the construct and hence, calls for more “theoretical”, multidimensional and subjective considerations of the term to ground human resource management practices addressed to attain expatriates' success.

This paper provides an integrative definition of expatriate success, giving greater insight into the construct, in addition to critically reflecting on it.

The recent failures and insolvencies of organisations related to the modern methods of construction (MMC) have gained increased attention and controversy across the UK construction sector. Such failures are linked to their inability to achieve an economy of scale and drive key clients to accept the MMC as an alternative to traditional methods. This paper aims to unravel whether a phenomenon of “innovation negativism” has manifested and is contributing to public clients' indecision towards broader MMC, whether this is only linked to past negative experiences formed after the Second World War or whether additional contributing reasons exist to influence adoption.

This study focusses on exploring the decision-making of the UK public construction sector; therefore, this paper adopts a qualitative approach, utilising interviews with 14 carefully selected MMC experts, government advisors and public clients. The phenomenological stance adopted herewith enables the authors to make better sense of the perceptions of the interviewees, leading to the conceptualisation of the innovation negativism phenomenon.

The paper identifies nine themes that may be argued to promote a profound understanding of the MMC negativism influencing public clients' decision-making. The study has found that more than just the previous negative perceptions formulated post Second World War are driving innovation negativism in the UK public sector. Notably, the emerging themes are incomprehension, lacking evidence, communication, relationship history, bad experiences, uncertainty, inadequate experimentation, the business case and localism.

This study is the first construction management research that acts as a fair departure point to conceptualise the reasoning behind innovation negativism in the construction setting. Through mirroring demand's unipolarity for traditional methods, policy and decision-makers can now rely on the conceptualised reasoning to determine practical solutions to overcome clients' indecisions towards MMC.

The defect detection problem of color-patterned fabric is still a huge challenge due to the lack of manual defect labeling samples. Recently, many fabric defect detection algorithms based on feature engineering and deep learning have been proposed, but these methods have overdetection or miss-detection problems because they cannot adapt to the complex patterns of color-patterned fabrics. The purpose of this paper is to propose a defect detection framework based on unsupervised adversarial learning for image reconstruction to solve the above problems.

The proposed framework consists of three parts: a generator, a discriminator and an image postprocessing module. The generator is able to extract the features of the image and then reconstruct the image. The discriminator can supervise the generator to repair defects in the samples to improve the quality of image reconstruction. The multidifference image postprocessing module is used to obtain the final detection results of color-patterned fabric defects.

The proposed framework is compared with state-of-the-art methods on the public dataset YDFID-1(Yarn-Dyed Fabric Image Dataset-version1). The proposed framework is also validated on several classes in the MvTec AD dataset. The experimental results of various patterns/classes on YDFID-1 and MvTecAD demonstrate the effectiveness and superiority of this method in fabric defect detection.

It provides an automatic defect detection solution that is convenient for engineering applications for the inspection process of the color-patterned fabric manufacturing industry. A public dataset is provided for academia.

This paper aims to measure the extent of digital financial inclusion (DFI) and examine the effect of socioeconomic characteristics on using government remittances and the adoption of digital financial services (DFS) during the COVID-19 pandemic.

The World Bank Global Financial Inclusion (Global Findex) database 2021 is used in this study, with a sample size of 3,000 Indian individuals. The study measured the demand-side analysis of DFI, namely, accessibility and usage of DFS with selected socioeconomic characteristics such as gender, age, income, education, being in the workforce and residential status of respondents. The dependent variable is binary in nature; therefore, the logistic regression model is used for the data analysis.

The results of the study reveal that individuals’ socioeconomic factors, such as female, all the age groups, tertiary education, third- and fourth-income quintile and workforce, are found to have a significant association with “accessibility,” an exogenous variable of DFS. Besides, respondents’ socioeconomic attributes, namely, female, tertiary education, income for all quintiles and workforce, are more likely to use DFSs in the COVID-19 pandemic. The study also finds the residential status of individuals is influencing the accessibility and usage of DFS.

The findings of the study provide valuable insights to the service providers and policymakers regarding the rapid expansion of DFS by digital infrastructure, simplifying the banking procedures and highlighting the importance of digital financial literacy to accomplish government goals through serving the unbanked population and also design strategies for achieving the objectives of Digital India: “Faceless, Paperless, and Cashless” of DFI across the country.

Notable studies used World Bank Findex survey data to explore the determinants of financial inclusion in general. This research is one among the few studies to explore the determinants of India’s DFI. Moreover, this study measured the effect of individual socioeconomic attributes on the adoption of DFSs during the COVID-19 pandemic, which has not been included in prior studies. Therefore, this study has added value to the existing literature on financial technology innovation and DFS for the sustainable development of emerging nations.