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Despite the growing use of Fourth Industrial Revolution (4IR) technologies in construction, the reasons behind adopting social media in this context and its real benefits for sustainable construction and productivity remain unclear. This study aims to examine how construction professionals perceive the impact of social media on sustainable construction and productivity in the industry.

This research used a mixed-methods research approach (qualitative and quantitative), resulting in the formulation of a well-structured questionnaire which was distributed to construction professionals. Ordinal regression and multinomial logistic regression were carried out to assess the impact of social media use on sustainable construction and productivity enhancement, as well as the extent of social media utilization in construction projects.

Through exploratory factor analysis, five distinct clusters of social media consequences were identified, namely: information and knowledge sharing, community engagement and morale, environmental and resource management, disruptive and stressful effects and communication and collaboration. Furthermore, the extent of social media usage is closely related to three key factors: community engagement and morale, environmental and resource management and communication and collaboration.

This study represents one of the pioneering research efforts in Nigeria to investigate the implications of social media usage in the construction industry. Thus, future studies can build upon this research to further contribute to the multifaceted dimensions of social media’s impact on the construction industry.

Employee green behavior (EGB) is a type of pro-environment behavior at the workplace strategized by organizations to attain sustainable development goals. While organizations have prioritized eco-friendly practices to attain sustainability objectives, EGB has emerged as an essential area of research. Considering the need for sustained employee green behavior, it is important to understand what stimulates such behaviors in an organization. Therefore, we propose a theoretical model grounded in social exchange theory to assess the effect of organizational commitment on employee green behavior, work-related use of social media, social well-being and psychological well-being.

A questionnaire-based survey approach was used to collect data from 203 employees of Indian manufacturing and service industries. Partial least square structural equation modeling (PLS-SEM) analysis was applied to examine the proposed hypothesis.

The results revealed positive and significant effects of organizational commitment on psychological well-being, social well-being, work-related social media use and employee green behavior. Further, psychological well-being mediates the association between work-related social media use and employee green behavior.

This is one of the first studies to examine the effect of organizational commitment on employee green behavior to the best of our knowledge. Additionally, the findings empirically establish organizational commitment, work-related social media use and psychological well-being as antecedents to employee green behavior, thus offering novel insights and theoretically contributing to the employee green behavior, well-being and organizational literature.

Although it is commonly agreed that prescriptive analytics can benefit organizations by enabling better decision-making, the deployment of prescriptive analytics tools can be challenging. Previous studies have primarily focused on methodological issues rather than the organizational deployment of analytics. However, successful deployment is key to achieving the intended benefits of prescriptive analytics tools. Therefore, this study aims to identify the enablers of successful deployment of prescriptive analytics.

The authors examine the enablers for the successful deployment of prescriptive analytics through five organizational case studies. To provide a comprehensive view of the deployment process, each case includes interviews with users, managers and top management.

The findings suggest the key enablers for successful analytics deployment are strong leadership and management support, sufficient resources, user participation in development and a common dialogue between users, managers and top management. However, contrary to the existing literature, the authors found little evidence of external pressures to develop and deploy analytics. Importantly, the success of deployment in each case was related to the similarity with which different actors within the organization viewed the deployment process. Furthermore, end users tended to highlight user participation, skills and training, whereas managers and top management placed greater emphasis on the importance of organizational changes.

The results will help practitioners ensure that key enablers are in place to increase the likelihood of the successful deployment of prescriptive analytics.

The emergence and rapid expansion of social media platforms have transformed the dynamics of consumer–brand communication. While consumer–brand relationship is driven on marketing campaigns in regular manner, social media (e.g. WeChat groups) creates boundaryless connections between external forces (e.g. customer community engagement) and internal resources (e.g. sales force). Therefore, this paper aims to provide a firm–customer synergistic perspective to explain service performance (SP) through service customization, which is significantly contributing to the research of consumer–brand relationship field in the current era.

A survey including two sets of questionnaires are employed for data collection, with one completed by frontline salesperson of Casarte and the other by their customers. A total of 242 dyadic-level data are adopted to test the hypothesis.

The findings shed light on the mechanism that drive SP by firm–customer interactions. These insights hold distinct managerial implications for firms seeking to leverage digital tools to efficiently meet customer demands.

This study contributes to understanding consumer–brand relationship within social media environment. It offers a synergistic perspective combined both outside-in and inside-out thinking, and explains how consumer–brand relationship can be fostered from both customers and salespeople. It highlights the significance of customer community engagement and salespeople dynamic learning capability as crucial factors in building strong consumer–brand relationship.

This study aims to examine organizational and social capital’s impact on adopting social media analytics (SMA) in the banking sector. It also explores the effects of SMA on competition analysis and Fintech innovation. Moreover, the study investigates the mediating impact of competition analysis on the relationship between SMA and Fintech innovation.

Data were collected from 284 respondents and analyzed using the SEM-PLS path modeling technique.

The findings confirm a significant role of organizational and social capital in adopting SMA. This study also indicates a significant impact of SMA on competition analysis and Fintech innovation. Moreover, it reveals a significant mediating impact of competition analysis on the association between SMA and Fintech innovation.

This study provides invaluable contributions for both academic and banking industry professionals. The study aimed to fill the literature gap, where there is a lack of an integrated framework examining how organizational capital, social capital, SMA and competition analysis interact to create Fintech innovations in the banking sector. It also offers novel implications to banking sector stakeholders, providing them with a thorough understanding of SMA’s importance and pivotal role and the drivers of finding innovative Fintech solutions.

Previous studies focus on the direct effects of marketing analytics on entrepreneurial performance, but few explore the underlying mechanisms. Drawing on affordance theory, this study explores pathways through new product innovation (NPI) for the effects of marketing analytics on business performance. NPI is a market-based innovation concept comprising customer- and competitor-driven NPD and incremental innovation.

Using survey data collected from UK-based entrepreneurial firms operating in the IT and telecoms industries, we apply confirmatory factor analysis and a sequential structural equation model to test the mediating role of NPI in the effect of marketing analytics on market performance and financial performance.

The results show that marketing analytics enhances business performance through competitor-driven but not customer-driven NPD. Although using marketing analytics to generate customer knowledge for existing product innovation may enhance market performance, this positive effect becomes negative when competitor-driven NPD is undertaken to improve existing product innovation.

This study makes significant contributions to the innovation and NPD literature. It delves deeper into the existing view on the positive contributions of customer engagement to business value creation, revealing the significance of competitor knowledge to enhance business performance through marketing analytics, particularly in the context of IT and telecoms entrepreneurial firms.

Recent technological turbulence stemming from Industry 4.0 provides managerial opportunities and challenges simultaneously. In this context, the purpose of this study is to explore the role of technological opportunism on innovativeness and discover the impact of innovativeness on new products performance in international markets.

To empirically test the hypotheses, the authors have collected survey data from 237 Korean exporting firms and applied structural equation modeling.

Empirical results indicate that technological opportunism, which represents technology sensing and responding capability, has a positive and significant influence on both exploratory and exploitative innovativeness. Also, explorative and exploitative innovativeness have positive and significant effects on new product performance in international markets.

This study highlighted the importance of technology sensing and responding capabilities to capture emerging opportunities, which may arise from Industry 4.0 technologies. In addition, sensing and responding capabilities will help a firm create a culture that values innovative proclivity, and in turn, will lead to superior new product performance in international markets.

Despite extensive scholarly interest in Industry 4.0, previous studies have neglected to address the potential impact of Industry 4.0 within the domain of new product development and its performance. Also, there have been several calls from the literature to address the managerial and strategic issues surrounding the Industry 4.0 phenomenon. In this study, the authors attempted to fill the research gaps in Industry 4.0 research studies through empirical examination.

This paper aims to unravel the relationship between market-driven impacts of climate change and firms’ deployment of renewable energy (RE) innovation. The purpose is to understand how market-related forces, influenced by uncertainty, shape firms’ behaviour in response to climate change challenges.

Drawing on the behavioural theory of the firm (BTOF), the paper develops a conceptual model to decode the relationship between each category of market-driven impacts and the resulting RE innovation within firms. The model takes into account the role of uncertainty and differentiates between multinational enterprises (MNEs) and domestic firms.

The analysis reveals five key sources of market-driven impacts: investor sentiment, media coverage, competitors’ adoption of ISO 14001, customer satisfaction and shareholder activism. These forces influence the adoption of RE innovation differently across firms, depending on the level of uncertainty and the discrepancy between environmental performance and aspiration level.

This paper contributes to the literature in four ways. Firstly, it emphasises the importance of uncertainty associated with market-driven impacts, which stimulates different responses from firms. Secondly, it fills a research gap by focusing on the proactivity of firms in adopting RE innovation, rather than just operational strategies to curb emissions. Thirdly, the paper extends the BTOF by incorporating the concept of uncertainty in explaining firm behaviour. Finally, it provides insights into the green strategies of MNEs in the face of climate change, offering a comprehensive model that differentiates MNEs from domestic firms.

This study aims to explore the role of the linguistic style used in the brand-posted social media content on consumer engagement in the Fintech domain.

A total of 3,286 tweets (registering nearly 1.35 million impressions) published by 10 leading Fintech unicorns in India were extracted using the Twitter API. The Linguistic Inquiry and Word Count (LIWC) dictionary was used to analyse the linguistic characteristics of the shared tweets. Negative Binomial Regression (NBR) was used for testing the hypotheses.

This study finds that using drive words and cognitive language increases consumer engagement with Fintech messages via the central route of information processing. Further, affective words and conversational language drive consumer engagement through the peripheral route of information processing.

The study extends the literature on brand engagement by unveiling the effect of linguistic features used to design social media messages.

The study provides guidance to social media marketers of Fintech brands regarding what content strategies best enhance consumer engagement. The linguistic style to improve online consumer engagement (OCE) is detailed.

The study’s findings contribute to the growing stream of Fintech literature by exploring the role of linguistic style on consumer engagement in social media communication. The study’s findings indicate the relevance of the dual processing mechanism of elaboration likelihood model (ELM) as an explanatory theory for evaluating consumer engagement with messages posted by Fintech brands.

This study aims to apply the service robot acceptance model (sRAM) to examine how attitude toward human–robot interaction (HRI) and engagement influence consumer acceptance of service robots in a frontline setting.

Data was collected from 255 visitors who interacted with a robotic tour guide at a city museum. The data was analyzed using smart PLS 4.0.

The findings show the positive effects of subjective norms, appearance, perceived trust and positive emotion on both attitude toward HRI and engagement. In addition, social capability impacted attitude toward HRI, whereas perceived usefulness affected engagement.

To deliver engaging museum experiences that bring about positive word-of-mouth and intention to visit, managers need to incorporate the sRAM dimensions in the design and deployment of service robots.

This research uses field data to empirically validate the sRAM in the context of service robot acceptance. It introduces engagement as a novel mediating variable, enriching current understanding of human-like qualities in HRIs.