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Online privacy pertains to an individual’s capacity to regulate and oversee the gathering and distribution of online information. Conversely, online privacy concern (OPC) pertains to the protection of personal information, along with the worries or convictions concerning potential risks and unfavorable outcomes associated with its collection, utilization and distribution. With a holistic approach to these relationships, this study aims to model the relationships between digital literacy (DL), digital data security awareness (DDSA) and OPC and how these relationships vary by gender.

The participants of this study are 2,835 university students. Data collection tools in the study consist of personal information form and three different scales. Partial least squares (PLS), structural equation modeling (SEM) and multi-group analysis (MGA) were used to test the framework determined in the context of the research purpose and to validate the proposed hypotheses.

DL has a direct and positive effect on digital data security awareness (DDSA), and DDSA has a positive effect on OPC. According to the MGA results, the hypothesis put forward in both male and female sub-samples was supported. The effect of DDSA on OPC is higher for males.

This study highlights the positive role of DL and perception of data security on OPC. In addition, MGA findings by gender reveal some differences between men and women.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-03-2023-0122

In this article, the authors draw-upon an extended unified theory of acceptance and use of technology (UTAUT) and propose a research model involving performance expectancy (PE), effort expectancy (EE), facilitating conditions (FC) and competitive pressure (CP) as potential salient factors explaining the adoption of digitalization in European SMEs. The authors also postulate that there may be cross-cultural differences, thereby leading us to include the country as a moderator in the model.

The authors validate this model with a cross-cultural sample involving 188 owner-managers from the Czech Republic and Slovakia and through the partial least square structural equation modeling (PLS-SEM) techniques as well as multi-group analysis.

The results using the study’s global dataset indicate that PE, FC and CP significantly affect owner-managers intentions toward digitalization in SMEs. The authors’ application of the multi-group analysis also suggests that although the two countries differ in digitalization adoption intention, the differences are statistically insignificant. In the conclusion, the authors highlight several implications these findings have for theory and practice.

The authors recommend that the providers of emerging digital technologies should improve on the performance features of those technologies and ensure they are relevant to the SMEs. By doing so, the adoption of digitalization will grow, because owner-managers of SMEs will have the confidence that adopting such technologies will improve their operations. Second, SMEs are required to provide adequate organizational and technical infrastructure to support digitalization adoption.

Aside from being among the few attempts to extend the explanatory power of UTAUT with PE, EE, FC and CP in investigating digitalization adoption in SMEs context, this study also validates its model with rigorous methodological approach as well as three datasets (global, Czech Republic and Slovakia) thereby strengthening the validity of the results.

The world of work is constantly changing. The COVID-19 pandemic has reinforced working from home, and there is an increasing demand for flexibility regarding the workplace. There is little empirical evidence on the mechanisms and factors that influence employee outcomes, such as productivity and turnover intention, at the workplace and at home. In addition, it is unclear whether the workplace characteristics that influence employee outcomes vary between different nations due to country-specific circumstances. The paper aims to address these two issues.

The research model applied in this study is based on the job demands-resources (JD-R) and environmental demands-resources models using German (n = 429) and USA (n = 507) survey samples. Partial least squares structural equation modelling is used to analyse the influence of workplace characteristics (isolation, family–work interference, equipment/facilities and skill variety) on employee outcomes (satisfaction, burnout, productivity and turnover intention). Additionally, a multi-group analysis is used to explore group differences in the factors influencing satisfaction, burnout, productivity and turnover intention between employees in Germany and the USA.

The results reveal that significant determinants of productivity and turnover intention include isolation, family–work interference, equipment/facilities and skill variety. Isolation and equipment/facilities are identified as the most important demands and resources of the home workplace. Some significant differences are found between Germany and the USA. The positive effect of isolation on burnout is significantly stronger in the USA than in Germany, whereas the positive effect of family–work inference on burnout is stronger in Germany than in the USA. The negative effects visible for the relations between burnout and satisfaction, and satisfaction and turnover intention are stronger in Germany than in the USA. The positive effect of burnout on turnover intention is stronger in the USA compared to Germany.

The study adds empirical evidence to the JD-R theory by analysing the influence of the home workplace characteristics on employee outcomes in different countries for the first time using a multi-group analysis. In addition, the study reveals new insights into the differences between the knowledge workforces in Germany and the USA by uncovering how key factors influence employee outcomes such as productivity and turnover intention, partially carried by varying length of experience in work from home between both of these countries. Insights from this study can support corporate real estate managers to make better decisions on the design of employees’ home workplaces and the integration of work from home into the company’s workspace concept.

This paper aims to create and evaluate a model for cryptocurrency adoption by investigating how age, education, and gender impact Behavioural Intention. A hybrid approach that combined partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) was used for the purpose.

This study uses a multi-analytical hybrid approach, combining PLS-SEM and ANN to illustrate the impact of various identified variables on behavioral intention toward using cryptocurrency. Multi-group analysis (MGA) is applied to determine whether different data groups of age, gender and education have significant differences in the parameter estimates that are specific to each group.

The findings indicate that Social Influence (SI) has the greatest impact on Behavioral Intention (BI), which suggests that the viewpoints and recommendations of influential and well-known individuals can serve as a motivating factor to invest in cryptocurrencies. Furthermore, education was found to be a moderating factor in the relationship found between behavioral intention and design.

Prior studies on technology adoption have utilized superficial SEM and ANN methods, whereas a more effective outcome has been suggested by implementing a dual-stage PLS-SEM and ANN approach utilizing a deep neural network architecture. This methodology can enhance the accuracy of nonlinear connections in the model and augment the deep learning capacity.

The research is based on the Unified Theory of Acceptance and Use of Technology (UTAUT2) and expands upon this model by integrating elements of design and trust. This is an important addition, as design can influence individuals' willingness to try new technologies, while trust is a critical factor in determining whether individuals will adopt and use new technology.

Cryptocurrencies are a relatively new phenomenon in India, and their use and adoption have grown significantly in recent years. However, this development has not been without controversy, as the implications of cryptocurrencies for society, the economy and governance remain uncertain. The results reveal that social influence is an important predictor for the adoption of cryptocurrency in India, and this can help financial institutions and regulators in making policy decisions accordingly.

Given the emerging nature of cryptocurrency adoption in India, there is certainly a need for further empirical research in this area. The current study aims to address this research gap and achieve the following objectives: (a) to determine if a dual-stage PLS-SEM and ANN analysis utilizing deep learning techniques can yield more comprehensive research findings than a PLS-SEM approach and (b) to identify variables that can forecast the intention to adopt cryptocurrency.

This study aims to address existing gaps in the literature and theories by investigating the influence of responsible leadership traits on employees’ sustainable performance (E-SuPer) in the Malaysian service sector. Specifically, the authors focus on three key responsible leadership traits: relationship building, relational governance and sharing orientation. Additionally, the authors explore how these traits interact with leader-member exchange (LMX) and whether gender plays a role in this relationship.

A total of 235 usable responses were analysed using partial least squares structural equation modelling. Multi-group analysis (MGA) was employed to examine the moderating impact of gender.

The results showed that both relationship building and relational governance significantly affect E-SuPer among organisations in the service industry. LMX was found to be a significant moderating condition influencing the association between responsible leaders’ sharing orientation and E-SuPer. Interestingly, the MGA results suggest that the effect on male employees was greater than on female employees across the relationships examined. The findings suggest that responsible leadership traits are essential for sustainable employee performance, but there is room for improvement in how these traits are perceived by female employees.

The present study contributes to gender equality agenda, supports the sustainable development goals, adds to the growing body of knowledge on the relationship between responsible leadership traits and E-SuPer within one of the most important economic sectors in Malaysia and sheds lights on the moderating effect of LMX.

This study investigates how responsible leadership traits affect E-SuPer in the service industry, particularly among male and female employees. Moreover, this study is one of the early investigations into the significance of responsible leadership within Malaysian service sector and offers valuable information for industry actors to improve their management approaches.

The purpose of this paper is to examine the resistance behavior of smart factories in small and medium-sized enterprises (SMEs). Drawing upon dual factor perspective, this study examines how two types of quality and perceived usefulness impact user resistance as enabling factors and how switching cost, skepticism, habit and inertia contribute to user resistance as inhibiting factors. Additionally, multi-group analysis is employed to compare small and medium enterprises.

Purposive sampling technique was employed to collect 460 Korean SMEs employees, consisting of 235 small enterprises and 225 medium enterprises. Partial least squares structural equation modeling was used for data analysis.

The results reveal that all three inhibiting factors, switching cost, skepticism and habit, are key antecedents of inertia. In small enterprises, skepticism has a greater impact on inertia, which in turn strongly affects resistance. Additionally, system quality is more crucial for small enterprises, whereas information quality holds more importance for medium enterprises in mitigating resistance. Moreover, when the implementation level of a smart factory is high, the effect of perceived usefulness on user resistance diminishes.

This study has revealed the importance of considering both enabling and inhibiting factors for the adoption of smart factory systems in the context of SMEs. Additionally, it has provided evidence that as the level of the smart factory system increases, the effect of perceived usefulness on user resistance decreases, thus making the transition to smart factory systems more challenging.

The study aims to identify the extent to which industry 4.0 (IR4.0) adoption impacts the sustainable manufacturing (SM) performance of the manufacturing industry, focusing on the comparative analysis between developed and developing economies amid coronavirus disease 2019 (COVID-19) pandemic.

The study proposes a conceptual model formed on seminal theories and literature using the cross-sectional design. For data collection, a purposive sampling method is used where 154 Malaysian (developing) and Australian (developed) manufacturing firms' data were collected. Partial least square-based structural equation modeling is employed to test the hypothesis and proposed research model.

This study finds that adoption of IR4.0 technologies does not directly influence the sustainability performance of the manufacturing industry, but rather the trajectories of SM (efficiency, flexibility, automation and big data and granularity) fully mediate the relationship between IR4.0 adoption and sustainability manufacturing performance. The comparative analysis between Australia and Malaysia shows no significant difference in the relationships or the framework; hence, the differences between developed and developing countries are not significant in this mechanism.

The study contributes to the insights of the managers regarding COVID-19 and the implementation of IR4.0 in the SM domain. The policymakers would further get better insights since the study pays attention to sustainable development goal, industry, innovation, infrastructure and responsible production.

This paper aims to examine project management segments based on customer satisfaction drivers and loyalty rather than traditional demographic or behavioural variables.

Data were gathered over 18 consecutive months, and 3,129 surveys were completed using a questionnaire. The statistical methods included partial least squares (PLS) structural equation modelling, finite mixture segmentation, prediction-oriented segmentation (PLS-POS) and multi-group analysis (PLS-MGA).

The findings indicate the existence of three segments among system delivery project customers based on the differences in the strengths of the path coefficients in the customer-centric structural model. In Segment 1, satisfaction based on the proposal was crucial for loyalty, with the value-for-money construct negatively impacting the repurchase intent construct. Segment 2 had a solid value-for-money orientation. In Segment 3, the critical path indicated that satisfaction drove repurchase intention, with satisfaction based mainly on the installation.

The research contributes to the segmentation theory by introducing a new way to segment the systems delivery projects customers based on the perceived strength of the relationships in a customer-centric structural model, which aligns with traditional segmentation theory in a way that most segmentation analyses do not. A new segmentation approach to the domain of project management theory is presented. Based on the results, treating the system delivery project customer base as a single homogenous group can lead to managerially misleading conclusions.

This study aims to understand the reasons for individuals switching from traditional banks to Internet-only banks and examine how switching intentions differ between Generation X and Generation Z. Notably, Generation Z, being digital natives, exhibits distinct characteristics compared to Generation X, who often referred to as digital immigrants. Given the technology-driven nature of Internet-only banks, a multi-group analysis between these two generations was conducted.

This study utilizes Bansal’s push–pull–mooring model as a framework to analyze switching intention. The study collected survey data from 383 Korean participants, consisting of 198 participants from Generation Z and 185 participants from Generation X.

The findings indicate that low satisfaction and discomfort are factors that push people to leave traditional banks. Specifically, Generation Z shows a significantly higher inclination to leave traditional banks due to discomfort. On the other hand, relative advantage, compatibility, observability and trialability are factors that pull people to switch to Internet-only banks. Generation X is more likely to consider adopting Internet-only banks when compatibility is high and complexity is low.

This study is the first to explore unique motivators for Generation Z, such as their discomfort with interpersonal interactions in the retail banking sector. These findings challenge earlier research emphasizing human interaction’s importance in technology adoption, offering insights into their future adoption of contactless services.

This study has two main objectives. First, to examine the indirect effects of digital platform capability and digital resilience on digital transformation (DT) outcomes for small- and medium-sized enterprises (SMEs), and second, to investigate how digital business model maturity influences these indirect effects.

The study adopts a quantitative design and collects data through a self-reporting survey from individuals in the technological industries. The Partial Least Squares-Structural Equation Modelling (PLS-SEM) and PLS multi-group analysis examine the measurement and structural models and the significance of differences in indirect paths based on the digital business model maturity level, serving as a moderator.

The findings of this study provide valuable insights into the internationalisation of digital SMEs. They indicate that digital platform capability and resilience fully mediate, connecting digital resources to SME growth. The study also confirms the digital business model maturity’s positive and significant moderating effect on these indirect relationships.

This research contributes to the existing literature by focusing on the international outcomes of platform ecosystems in developing markets. It explores how digital platform capability and resilience support the digital transformation of SMEs, considering their vulnerability due to their small size. The study also fills a research gap by investigating the relationship between big data, digital leadership and the international growth of digital platforms. Lastly, it explores the role of digital maturity in the relationships between antecedents, determinants and outcomes of digitalisation.