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This study aims to examine the impact of digital transformation on firms’ value and explore the mediating impact of ESG performance and moderating impact of information interaction.

Data was collected from companies listed on the Shanghai and Shenzhen stock exchange between 2012 and 2020 with 21,488 observational samples, featuring a selection of 3,348 companies. Panel data regression techniques were used to test the mediating role of ESG performance and the moderating role of information interaction.

The study found that digital transformation can improve firms’ ESG performance, which in turn positively affects their value. The firms that engage in more interaction with outsiders benefit more from digital transformation and have a higher value.

This study provides new theoretical insight into improving firms’ value through digital transformation and ESG performance. It is the first to discuss and study the moderating role of information interaction in the relationship between digital transformation and firms’ value.

This research aims to examine how decision-makers’ demographic traits affect the integration of foreign workforce into Tirolean (Austria) companies. With continuous world migrations, Tirol experiences a great inflow of foreign workforce. While integrating into the workforce, the foreign workers interact with various decision-makers whose demographic traits (e.g., age, gender, nationality) potentially influence the success of the integration process. To gather data on the integration levels of a foreign workforce, the author conducted a questionnaire. Furthermore, several statistical analyses were run to determine if the relationship between demographic characteristics and integration success exists. The study reveals that demographic characteristics influence decision-makers’ acceptance of expatriates, as well as their recruitment, integration, and training and development outcomes. The empirical results indicate the strength of relationships identified through analyses. The study is limited to geographical, as well as the scope of the sample size, as the data are obtained from Tirol only. In addition, the results from the study serve as a basis for future discussions and research.

Big data capabilities have the potential to completely transform conventional methods of doing business. Nevertheless, the role of big data capabilities in fostering green marketing capabilities and improving green competitive advantage is still not fully understood. To add new knowledge, this paper aims to propose a moderated mediation model to strengthen green competitive advantage in a big data environment. The model introduces both the mediating role of green marketing capabilities and the moderating role of big data capabilities. We developed and empirically tested a moderated mediation model.

In this study, we have adopted a survey-based methodology. The study collected data from 337 managers and empirically analyzed it to test the theoretical model of moderated mediation. We employed structural equation modeling for empirical analysis.

The findings revealed that organizational learning improves green marketing capabilities, whereas the relationship between organizational learning and green competitive advantage is insignificant. The mediating role of green marketing capabilities in the relationship between organizational learning and green competitive advantage was statistically significant, indicating that green marketing capabilities serve as a bridge between organizational learning and green competitive advantage. Big data capabilities moderate the relationship between organizational learning and green marketing capabilities. The moderated mediation was also significant, highlighting that big data capabilities further strengthen the indirect effects of organizational learning on green competitive advantage via green marketing capabilities.

This paper delivers theoretical and practical understandings of the importance of organizational learning and big data capabilities. Similarly, it extends current knowledge and provides key insights for managerial decision-making.

This study aims to investigate the interplay between a proactive attitude towards disruptions – supply chain disruption orientation – and supply chain resilience, increasing our understanding of their influence on reducing the impact of supply chain disruptions within the B2B context.

As unexpected disruptions are closely related to a dynamic and changing perception of the environment, this research is framed under the dynamic capabilities lens, consistent with existing resilience literature. The authors used partial least squares-path modeling (PLS-PM) to empirically test the proposed research model using survey data from 216 firms.

Results show that a proactive approach to disruptions alone is insufficient in mitigating their negative impact. Instead, a firm’s disruption orientation plays a crucial role in boosting its resilience, which acts as a mediator, reducing the impact of disruptions.

This paper sheds light on the mechanisms by which firms can mitigate the effects of supply chain disruptions and offers insights into how certain capabilities are needed so that firms’ attitudes can effectively impact firm performance. This research thus suggests that dynamic capabilities, traditionally perceived as being enabled by other elements, act themselves as enablers. Consequently, they have the potential to translate strategic orientation or attitudes into tangible effects on performance, enriching our understanding of how firms combine their internal attitudes and capabilities to achieve sustained competitive advantage.

Extended reality (XR) is an emerging technology, with its popularity rising in different industry sectors, where its application has been recently considered in construction safety. This study aims to investigate the applications of XR technologies in the safety of construction through projects lifecycle perspective.

Scientometric analysis was conducted to discover trends, keywords, contribution of countries and publication outlets in the literature. The content analysis was applied to categorize previous studies into three groups concerning the phase of lifecycle in which they used XR.

Results of the content analysis showed that the application of XR in the construction safety is mostly covered in two areas, namely, safety training and risk management. It was found that virtual reality was the most used XR tool with most of its application dedicated to safety training in the design phase. The amount of research on the application of augmented reality and mixed reality in safety training, and risk management in all phases of lifecycle is still insignificant. Finally, this study proposed three main areas for using the XR technologies regarding the safety issues in future research, namely, control of safety regulations and safety coordination in construction phase, and safety reports in the operation phase.

This paper inspected the utilization of all types of XR for safety in each phase of construction lifecycle and proposed future directions for research by addressing the safety challenges in each phase.

This study aims to explore the intricate relationship between financial literacy, digital transformation, Fintech adoption, competitiveness and sustainable firm performance, particularly focusing on how financial literacy empowers firms in the evolving digital landscape. Leveraging technological innovation systems (TIS) and resource-based view (RBV), this research suggests a model that incorporates these concepts, focusing on the moderating role of financial literacy in essential interactions.

This study employed a survey-based methodology, collecting data from employees across five major Pakistani banks. The survey yielded 426 responses, from which 387 valid ones were selected for analysis. The analysis utilized partial least squares-structural equation modeling (PLS-SEM), complemented by the Hayes Process Model for moderated mediation analysis. This approach ensured robust examination of the relationships between the constructs of the proposed model.

The study's findings validate that digital transformation significantly enhances sustainable performance, with Fintech adoption and competitiveness acting as crucial mediators. Financial literacy is highlighted as a key moderator, influencing the effects of digital transformation on Fintech adoption and competitiveness, although its direct impact on sustainable performance is less pronounced. This comprehensive analysis underscores the complex interplay among these factors in driving sustainable performance in the banking sector.

This research enriches the theoretical and practical comprehension of how digital transformation and Fintech integration, underpinned by financial literacy, bolster sustainable business outcomes. It sheds light on the synergy between technology, strategy and organizational success, offering key insights for the banking industry's navigation through the digital era's challenges.

The association between e-government and trust in government has been extensively examined, even though empirical works have returned conflicting conclusions about this association. This study aims to revisit the relationship between e-government and trust in government using macrolevel data from Organisation for Economic Co-operation and Development (OECD) member countries. It also addresses whether or not e-government should contribute to the quality of governance to improve trust in government.

Archival data from 36 OECD members over five years (2012–2015) were used to achieve the study objectives.

Results indicate that e-government does not directly affect trust in government, although there is an indirect association.

This implies that e-government systems will not significantly impact trust in government unless they contribute to the quality of governance that offers a basis for granting trust in government. Accordingly, value creation, including trust, should be considered during the design of e-government systems to achieve the desired outcomes.

This paper employs bibliometric analysis to explore transformative trends and emerging research patterns in the intersection of digital technology and tourism. The goal is to provide insights into the evolving landscape, identify key themes, influential authors and prominent journals, contributing to a nuanced understanding of this dynamic field.

This study rigorously employs bibliometric analysis from 2002 to 2023, using Scopus and Web of Science databases, yielding 2,040 Scopus and 1,389 Web of Science documents. After meticulous data selection, 575 articles were refined. Utilising VOSviewer and Biblioshiny, the analyses focus on publication trends, citations, authorship, collaborations and thematic clusters, addressing four research questions.

This bibliometric analysis reveals a significant growth in annual scientific production on digital technology and tourism, peaking at 100 articles in 2023. Key contributors include LAW R., CHUNG N. and KOO C. China leads in research output, and influential journals include “Current Issues in Tourism” and “Information Technology and Tourism.” Noteworthy trends emphasise the centrality of “smart tourism” and the interconnectedness of technology, hospitality and management in shaping future research directions.

Stakeholders should focus on integrating emerging technologies in tourism, emphasising areas with high research activity. Professionals must stay updated on evolving trends, adopt innovative digital solutions and collaborate to navigate the dynamic landscape.

This study pioneers the first bibliometric analysis on digital technology in the tourism industry, providing a unique and comprehensive exploration of the scholarly landscape from 1998 to 2023, shedding light on key contributors, influential journals and emerging trends.

Project scheduling plays an essential role in the implementation of a project due to the limitation of resources in practical projects. However, the existing research tend to focus on finding suitable algorithms to solve various scheduling problems and fail to find the potential scheduling rules in these optimal or near-optimal solutions, that is, the possible intrinsic relationships between attributes related to the scheduling of activity sequences. Data mining (DM) is used to analyze and interpret data to obtain valuable information stored in large-scale data. The goal of this paper is to use DM to discover scheduling concepts and obtain a set of rules that approximate effective solutions to resource-constrained project scheduling problems. These rules do not require any search and simulation, which have extremely low time complexity and support real-time decision-making to improve planning/scheduling.

The resource-constrained project scheduling problem can be described as scheduling a group of interrelated activities to optimize the project completion time and other objectives while satisfying the activity priority relationship and resource constraints. This paper proposes a new approach to solve the resource-constrained project scheduling problem by combining DM technology and the genetic algorithm (GA). More specifically, the GA is used to generate various optimal project scheduling schemes, after that C4.5 decision tree (DT) is adopted to obtain valuable knowledge from these schemes for further predicting and solving new scheduling problems.

In this study, the authors use GA and DM technology to analyze and extract knowledge from a large number of scheduling schemes, and determine the scheduling rule set to minimize the completion time. In order to verify the application effect of the proposed DT classification model, the J30, J60 and J120 datasets in PSPLIB are used to test the validity of the scheduling rules. The results show that DT can readily duplicate the excellent performance of GA for scheduling problems of different scales. In addition, the DT prediction model developed in this study is applied to a high-rise residential project consisting of 117 activities. The results show that compared with the completion time obtained by GA, the DT model can realize rapid adjustment of project scheduling problem to deal with the dynamic environment interference. In a word, the data-based approach is feasible, practical and effective. It not only captures the knowledge contained in the known optimal scheduling schemes, but also helps to provide a flexible scheduling decision-making approach for project implementation.

This paper proposes a novel knowledge-based project scheduling approach. In previous studies, intelligent optimization algorithm is often used to solve the project scheduling problem. However, although these intelligent optimization algorithms can generate a set of effective solutions for problem instances, they are unable to explain the process of decision-making, nor can they identify the characteristics of good scheduling decisions generated by the optimization process. Moreover, their calculation is slow and complex, which is not suitable for planning and scheduling complex projects. In this study, the set of effective solutions of problem instances is taken as the training dataset of DM algorithm, and the extracted scheduling rules can provide the prediction and solution of new scheduling problems. The proposed method focuses on identifying the key parameters of a specific dynamic scheduling environment, which can not only reproduces the scheduling performance of the original algorithm well, but also has the ability to make decisions quickly under the dynamic interference construction scenario. It is helpful for project managers to implement quick decisions in response to construction emergencies, which is of great practical significance for improving the flexibility and efficiency of construction projects.

The study investigates the influence of perceived security (PS) on behavioral intention (BI) via the trust attitude process and explores the moderating effects of gender. PS in mobile wallets enhances user trust (TR), attitude (ATT) and intention (INT). Using a multiple and serial mediation model, both TR and ATT were found to mediate the relationship between PS and BI.

Drawing on the stimulus-organism-response (S-O-R) theory, the proposed conceptual model comprises PS, TR, ATT and BI. An online survey was conducted with a cross-sectional sample of 744 mobile wallet users in India. Partial least squares structural equation modeling (PLS-SEM) was used to analyze the hypothesized relationships and test the mediation effects.

Results show that the stimulus, PS, has a positive and significant influence on TR and ATT, which eventually has a positive influence on BI. The research model explains 64.4 percent of the variance in BI. Further, both TR and ATT independently and parallelly mediate the relationship PS and BI. Lastly, gender is found to moderate the relationship between TR and BI and ATT and BI.

The research showed the importance of PS, TR and ATT towards mobile wallet adoption INTs. Further, the findings support the idea that developing TR and ATT is essential for shaping INTs. This suggests that mobile wallet service providers should invest in methods that not just enhance user TR but also reinforce a positive ATT towards the platform. To demonstrate TR, mobile wallet providers must ensure the confidentiality and privacy of user data, keep customer interests in mind and fulfill commitments. Lastly, for strengthening customer TR, excellent customer support is extremely important.

While prior researchers have majorly used technology acceptance model (TAM) and unified theory of acceptance and use of technology (UTAUT) models to explain adoption INTs, this study examines the relationship between PS, TR, ATT and BI through the lens of the SOR framework.