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The technology adoption propensity (TAP) index is a parsimonious yet robust tool for measuring an individual’s willingness to accept new technologies. However, further investigation is still needed to evaluate its merits in non-Western countries. With this task in mind, this study assesses the psychometric properties and predictive capabilities of TAP in Turkey.

The researchers utilized an iterative process of forward-backward translation and three rounds of piloting to ensure the cross-cultural, conceptual and linguistic equivalence for the chosen tool. The final survey was conducted online on a convenience sample of 352 academics and in person for 2 other convenience samples that comprised a total of 259 municipal officers and 300 individuals.

TAP’s four-subscale structure was confirmed by a principal component analysis (PCA), and A confirmatory factor analysis (CFA) showed an acceptable fit across all samples. The total composition of the optimism, proficiency, dependence and vulnerability subscales was found to have adequate internal consistency and discriminant validity. Measurement invariance testing further demonstrated that TAP’s factorial organization was invariant across gender, age and income at configural, metric, scalar and strict levels. Finally, logistic and ordinary leas squares (OLS) regression analyses revealed that the TAP scores were predictive of prior technology adoption and use frequency.

Although these results are still preliminary, the confirmation and replication of TAP in Turkey clearly suggest that TAP is a dependable tool for assessing technology readiness that can be utilized well across different cultures.

Fuzzy-set qualitative comparative analysis (fsQCA) is explored as a transformative tool rooted in complexity theory, shedding light on uncertainties shaping real-world decisions in tourism, with a focus on its application in the hospitality domain.

This study systematically evaluates fsQCA’s application in hospitality and tourism research, employing bibliometric analysis to scrutinize the published literature since its induction in 2011. The research seeks to understand the evolving usage by qualitatively reviewing impactful studies based on total citations.

The study reveals the ascendancy of fsQCA as a predominant approach in hospitality and tourism studies, particularly in illuminating decision-making paradigms in key sectors like destination and hotel selections and entrepreneurial orientations. However, an absence of fsQCA applications in gastronomy and wine tourism is identified, signaling uncharted territories for future inquiry.

Theoretical implications include paradigm shifts to complexity theory, configural analysis and asymmetric algorithms. Practical implications involve improved decision-making and tailored marketing, benefiting industry practitioners. Limitations include potential academic bias, while future research suggests exploring sub-sectors, sustainability and emerging technologies.

This study identifies gaps in the fsQCA application and pioneers its examination within the hospitality domain, offering a unique perspective on understanding intricate relationships and configurations among variables. The study emphasizes the efficacy of asymmetric methodologies in elucidating behavioral nuances in hospitality and tourism, providing a foundation for future inquiries to expand horizons and unravel the nuanced applications of fsQCA in this research domain.

Smartphone apps collect users' personal information, which triggers privacy concerns for app users. Consequently, app users restrict apps from accessing their personal information. This may impact the effectiveness of in-app advertising. However, research has not yet demonstrated what factors impact app users' decisions to use apps with restricted permissions. This study is aimed to bridge this gap.

Using a quantitative research method, the authors collected the data from 384 app users via a structured questionnaire. The data were analysed using AMOS and fuzzy-set qualitative comparative analysis (fsQCA).

The findings suggest privacy concerns and risks have a significant positive effect on app usage with restricted permissions, whilst reputation, trust and perceived benefits have significant negative impact on it. Some app-related factors, such as the number of apps installed and type of apps, also impact app usage with restricted permissions.

Based on the findings, the authors provided several implications for app stores, app developers and app marketers.

This study examines the factors that influence smartphone users' decisions to use apps with restricted permission requests. By doing this, the authors' study contributes to the consumer behaviour literature in the context of smartphone app usage. Also, by explaining the underlying mechanisms through which the principles of communication privacy management theory operate in smartphone app context, the authors' research contributes to the communication privacy management theory.

This study aims at assessing item fairness in students' evaluation of teaching based on students' academic college using measurement invariance analysis (MI).

The sample of this study consists of 17,270 undergraduate students from 12 different academic colleges. SET survey consists of 20 Likert-type items distributed to four factors: planning, instruction, management and assessment was used to collect the data. The Lavaan R package with confirmatory factor analysis (CFA) was used to evaluate measurement invariance (MI). Four models of CFA were investigated and assessed: the configural model, the metric model, the scalar model and the residual invariance model. ANOVA was used to test the differences in SET according to academic colleges.

MI analysis showed that the four levels of MI models are supported. ANOVA test showed that means of SET total scores are statistically different according to students' academic colleges. College of “Education” has the highest SET mean (88.64 out of 100), and all the differences between the College of Education’s SET mean and other colleges' SET means are statistically significant.

The study recommends that higher education institutions test the MI of SET according to academic colleges and then use colleges with the highest SET at the university level as internal benchmarking to develop and enhance their teaching practices.

This study is probably the only study that tested MI according to students' colleges before testing the differences between colleges in SET. If MI is not supported, then the comparisons between academic colleges are not applicable.

Health misinformation on social media threatens public health. A critical question that sheds light on the propagation of health misinformation across social media platforms revolves around identifying the specific types of social media users susceptible to this issue. This study provides an initial insight into this matter by examining the underlying psychological mechanism that renders users susceptible to health misinformation.

In this study, we developed an integrated model of susceptibility to health misinformation, drawing on the motivation-opportunity-ability theory and the elaboration likelihood model. We collected the data from a sample of 342 social media users in China. Furthermore, the fuzzy-set qualitative comparative analysis was adopted to examine the proposed model and uncover the causal recipes associated with susceptibility to health misinformation.

The results indicated that there are three configural types of users that are susceptible to health misinformation: the health-consciousness core-driven type, the popularity-driven core type and the dual-driven type characterized by both high health consciousness and information popularity. Among these, high health-consciousness and the reliance on information popularity-based pathways emerge as pivotal factors influencing susceptibility to health misinformation.

This study contributes to the social media literature by identifying various psychological traits that lead to social media users’ susceptibility to health misinformation. Additionally, the study provides comprehensive guidance on how to mitigate the spread of health misinformation.

The flagship traceability technology is increasing transparency, social benefit and economic value particularly after the pandemic. There has not been much research on how information quality in transparency affects information usefulness and trust. The research model is built on the framework of transparency requirements and incorporates the usefulness of traceability information and trust.

Questionnaire survey was used for data collection. To evaluate the research model, structural equation modeling (SEM) was employed. Measurement invariance analysis was used to investigate variations in trust between groups.

The results show that transparency requirements including information relevancy, ease of manipulation and value-added information affect information usefulness. The usefulness of traceability information positively affects trust in producers. Information receivers who believe in the credibility of traceability information have a higher level of trust than those who do not.

The results have important theoretical and practical implications for academia and industry to devise strategies and policies on data-centric traceability systems.

This study aims to examine a common failure in knowledge sharing, called disengagement from knowledge sharing (DKS), and investigates how technostress may contribute to this unintentional withholding of knowledge for knowledge workers. The authors apply the Job Demands-Resources (JD-R) model to explain the dual path of technostress creators and inhibitors on DKS via burnout and job engagement. The authors also examine how the pandemic and the changes in remote work and information and communication technology (ICT)-related stress may have impacted DKS.

Using a time-lag survey, two independent samples of knowledge workers who use information and communication technologies for their jobs were surveyed during early 2020 and mid-2021. Analyses were completed with partial least squares-structural equation modelling.

Technostress (via the JD-R model) explained DKS. Technostress creators were positively associated with burnout, which was in turn positively related to DKS. Technostress inhibitors were positively associated with job engagement, which in turn was also positively related to disengagement to knowledge sharing. Technostress inhibitors were negatively associated with burnout. Results from the multigroup analysis indicated that technostress inhibitors had a stronger relationship with engagement pre-pandemic than mid-pandemic.

This research addresses a more common source of knowledge sharing failures and illustrates how ICTs may impact this DKS via burnout and job engagement. In addition, this research captures a change in relationships associated with the pandemic.

The purpose of this study is to explore the relationship between digital transformation and excess employees. This research investigates the questions of when human−machine synergy can be achieved after a firm goes through digital transformation and whether there will be excess employees in the interim.

This paper takes A-share listed companies as research object in the period of 2011−2020 and a total of 24,718 samples are obtained. Hypothesis testing and regression analysis are performed in STATA.

This paper finds a human−machine mismatch in the short term, as evidenced by an increase in the rate of excess employees; however, with the progress of digital transformation, it can drive the achievement of human−machine synergy in the long term, and management efficiency plays a mediating role in this process. Further research showed that the effects of digital transformation on the number of employees, revenue generation per capita and profit generation per capita varied in both the short and long term. In addition, the characteristics of the company affect the relationship between digital transformation and excess employees.

This paper contributes to the understanding of the impacts of digital transformation on the human capital structure of companies at a micro level. It also provides insights into how to improve human capital demand structure through digitalization, thus providing insights into labor market changes.

Although (tele)work is increasingly being adopted and employees’ behavior is recognized to be key for organizational outcomes (e.g. performance), the current literature on counterproductive work behaviors (CWB) in telework settings is fragmented, and a valid measurement instrument is lacking. This study aims to address this gap and starts by presenting a review of the current literature on counterproductive work behavior in flexible work arrangements (i.e. telework). Based on this study, eight categories of work behavior that appear to be frequent under telework settings were identified.

Next, four studies aimed at developing and validating a measurement instrument that captures employee counterproductive work behavior when teleworking: the counterproductive [tele]work behavior scale (CTwBS).

In Study 1, the CTwBS was created, and in Study 2, its factorial validity was examined (N = 350). In Study 3, using a sample of teleworkers (N = 289), the convergent and discriminant validity of the CTwBS was tested using self-ratings of (positive and negative) affect, attitudes toward telework and frequency of CWB in general. In Study 4, a daily-diary study across five workdays (N = 232 × 5 = 1,160) examined the criterion validity of the CTwBS.

The results indicated that the CTwBS is a valid and reliable instrument for capturing employee counterproductive work behavior in telework settings. Theoretical and practical implications are discussed.

To the best of the authors’ knowledge, CTwBS is the first measure aimed at assessing counterproductive work behavior in telework settings.

Whereas job crafting and idiosyncratic deals (i-deals) have primarily been studied in white-collar jobs, there is a lack of research on job design in less skilled and highly structured work. Our study addresses this gap by analyzing the effects of transformational leadership on job crafting and i-deals in blue- and white-collar jobs.

To test our hypotheses, we surveyed 285 employees (31.9% blue-collar employees and 68.1% white-collar employees) in the German craft industry, using structural equation modeling for data analysis.

Our results show that transformational leadership is a strong predictor of job crafting and i-deals but that its influence depends on the occupational group. More specifically, the moderating role of the occupational group in the relationship between transformational leadership and job crafting differs regarding job crafting’s dimensions. Concerning i-deals, transformational leadership’s influence on both development and task i-deals is stronger in white-collar jobs than it is in blue-collar jobs.

The study provides new insights into the important role of the work context in which leadership takes place. In particular, these insights can guide leaders in how to manage different occupational groups to engage them in proactive behaviors.

This study is the first to compare the contextual roles of blue- and white-collar jobs with regard to job design. By examining the influence of transformational leadership on job crafting and i-deals in two occupational groups, our study contributes to research on the role of work context in the effectiveness of transformational leadership in encouraging employees to engage in proactive behaviors.