Maria Ijaz Baig, Elaheh Yadegaridehkordi and Mohd Hairul Nizam Bin Md Nasir
This research aimed to analyze and prioritize the factors affecting sustainable marketing (SM) and sustainable operation (SO) of manufacturing small and medium-sized enterprises…
Abstract
Purpose
This research aimed to analyze and prioritize the factors affecting sustainable marketing (SM) and sustainable operation (SO) of manufacturing small and medium-sized enterprises SMEs through big data adoption (BDA).
Design/methodology/approach
The technology-organization-environment (TOE) framework was used as a theoretical base and data were gathered from manufacturing SMEs in Malaysia. The 159 questionnaire replies of chief executive officer (CEO)/managers were analyzed using a hybrid approach of structural equation modeling-artificial neural network (SEM-ANN).
Findings
The findings of this study showed that perceived benefits (PB), technological complexity (TC), organization's resources (OR), organization's management support (OMS) and government legislation (GL) are the factors that influence BDA and promote SM and SO. The findings of ANN showed that a perceived benefit is the most important factor, followed by OMS.
Practical implications
The findings of this study can assist SMEs managers in making strategic decisions and improving sustainable performance and thus contribute to overall economic development.
Originality/value
The manufacturing industry is under immense pressure to integrate sustainable practices for long-term success. BDA can assist industries in aligning industries' operational capabilities. The majority of the current research have mainly emphasized on BDA in corporations. However, the associations between BDA and sustainable performance of manufacturing SMEs have been less explored. To address this issue, this study developed a theoretical model and examined the influence of BDA on SM and SO of manufacturing SMEs. Meanwhile, the hybrid methodological approach can help to uncover both linear and non-linear relationships better.
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Zoha Rahman, Sedigheh Moghavvemmi, Kumaran Suberamanaian, Hasmah Zanuddin and Hairul Nizam Bin Md Nasir
The purpose of this paper is to identify the mediating effect of fan-page followers’ engagement activities and moderating role of followers’ demographic profile and trust level on…
Abstract
Purpose
The purpose of this paper is to identify the mediating effect of fan-page followers’ engagement activities and moderating role of followers’ demographic profile and trust level on their purchase intention.
Design/methodology/approach
This study utilised the customer engagement behaviour and consumer involvement theory as a foundation to explore the impact of variables. Structural equation modelling was utilised to test the model with the data collected from 307 Facebook fan pages’ followers of five Malaysian companies.
Findings
It was shown that following fan pages will influence fan page engagement, which in turn affects purchase intention and social media connectedness. Further analysis indicated that the impact of “follow” and “engagement” on purchase intention differs between genders, ages, level of trust and income.
Research limitations/implications
The study serves as a basic fundamental guideline for academics and researchers to interpret the concept of following fan pages and engagement actions and its effects on purchase intention and social media connectivity, as well as opening a vast area of unexplored researches on the subject of social media.
Practical implications
The research provides information for business-to-consumer companies in utilising fan page based on user categories.
Originality/value
This study proposes the application of an empirically tested framework to the fan-page follow actions. The authors argue that this framework can provide a useful foundation for future social commerce research. The results would help academics be aware of fan page and its user’s engagement actions, which will provide a new avenue of research.
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Xuerong Peng, Lian Zhang, Seoki Lee, Wenhao Song and Keyan Shou
This study aims to identify key contributors, research themes, research gaps, and future directions in hospitality innovation by conducting bibliometric and content analyses of…
Abstract
Purpose
This study aims to identify key contributors, research themes, research gaps, and future directions in hospitality innovation by conducting bibliometric and content analyses of peer-reviewed articles in this field.
Design/methodology/approach
A bibliometric analysis was conducted using VOSviewer software on 2,698 peer-reviewed English-language articles retrieved from the Web of Science database, published between 1995 and 2023. Key contributors were identified based on publication volume, citation, and co-citation analysis. Co-occurrence analysis of index keywords and content analysis of influential articles were used to identify research themes.
Findings
The study identified four distinct research themes in hospitality innovation: (1) digital technology adoption primarily among customers, (2) innovation management within hospitality firms, focusing on knowledge management and eco-innovation, (3) service innovation primarily among employees, and (4) business model innovation involving multiple stakeholders. Additionally, the study determined key contributors, highlighted research gaps, and provided suggestions for future research directions.
Originality/value
This study contributes to the existing literature by providing a systematic and in-depth review of hospitality innovation research. It identifies key contributors, research themes, and potential gaps for future research, offering valuable insights for both industry practitioners and scholars.
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Rajasshrie Pillai and Kailash B.L. Srivastava
The study explores the factors affecting the use of smart human resource management 4.0 (SHRM 4.0) practices and its effect on dynamic capabilities and, consequently, on…
Abstract
Purpose
The study explores the factors affecting the use of smart human resource management 4.0 (SHRM 4.0) practices and its effect on dynamic capabilities and, consequently, on organizational performance.
Design/methodology/approach
The authors used socio-technical and dynamic capabilities theory to propose the notable research model. The authors explored the factors driving the use of SHRM 4.0 practices and their contribution to organizational performance through the development of dynamic capabilities. The authors collected data from 383 senior HR managers using a structured questionnaire, and PLS-SEM was used to analyze the data.
Findings
The results show that socio-technical factors such as top management support, HR readiness, competitive pressure, technology readiness and perceived usefulness influence the use of SHRM 4.0 practices, whereas security and privacy concerns negatively influence them. Furthermore, the authors also found the use of SHRM 4.0 practices influencing the dynamic capacities (build (learning), integration and reconfiguration) and, subsequently, its impact on organizational performance.
Originality/value
Its novelty lies in developing a model using dynamic capabilities and socio-technical theory to explore how SHRM 4.0 practices influence organizational performance through dynamic capabilities. This study extends the literature on SHRM 4.0 practices, HR technology use, HR and dynamic capabilities by contributing to socio-technical theory and dynamic capabilities and expanding the scope of these theories in the area of HRM. It provides crucial insights into HR and top managers to benchmark SHRM 4.0 practices for improved organizational performance.
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Rajasshrie Pillai and Brijesh Sivathanu
Human resource managers are adopting AI technology for conducting various tasks of human resource management, starting from manpower planning till employee exit. AI technology is…
Abstract
Purpose
Human resource managers are adopting AI technology for conducting various tasks of human resource management, starting from manpower planning till employee exit. AI technology is prominently used for talent acquisition in organizations. This research investigates the adoption of AI technology for talent acquisition.
Design/methodology/approach
This study employs Technology-Organization-Environment (TOE) and Task-Technology-Fit (TTF) framework and proposes a model to explore the adoption of AI technology for talent acquisition. The survey was conducted among the 562 human resource managers and talent acquisition managers with a structured questionnaire. The analysis of data was completed using PLS-SEM.
Findings
This research reveals that cost-effectiveness, relative advantage, top management support, HR readiness, competitive pressure and support from AI vendors positively affect AI technology adoption for talent acquisition. Security and privacy issues negatively influence the adoption of AI technology. It is found that task and technology characteristics influence the task technology fit of AI technology for talent acquisition. Adoption and task technology fit of AI technology influence the actual usage of AI technology for talent acquisition. It is revealed that stickiness to traditional talent acquisition methods negatively moderates the association between adoption and actual usage of AI technology for talent acquisition. The proposed model was empirically validated and revealed the predictors of adoption and actual usage of AI technology for talent acquisition.
Practical implications
This paper provides the predictors of the adoption of AI technology for talent acquisition, which is emerging extensively in the human resource domain. It provides vital insights to the human resource managers to benchmark AI technology required for talent acquisition. Marketers can develop their marketing plan considering the factors of adoption. It would help designers to understand the factors of adoption and design the AI technology algorithms and applications for talent acquisition. It contributes to advance the literature of technology adoption by interweaving it with the human resource domain literature on talent acquisition.
Originality/value
This research uniquely validates the model for the adoption of AI technology for talent acquisition using the TOE and TTF framework. It reveals the factors influencing the adoption and actual usage of AI technology for talent acquisition.