Pub Date : 2022-11-22DOI: 10.1108/imds-04-2022-0220
Feiyang Guan, T. Wang, Liqing Tang
PurposeThis study aims at the sudden outbreak of COVID-19, which had an unprecedented negative impact on the Chinese economy, with firms being affected most. Firms differ in terms of their specific internal environment, shaping their ability to respond to the outbreak, so the impact may also vary.Design/methodology/approachIn this paper Chinese listed firms are selected as samples to investigate the mediating effect of prior digital technology on the relationship between R&D (research and development) investment (funds and staff) and firm performance during the epidemic. Firm size and diversification are then introduced as moderating variables to explore the conditional mediating effect of digital technology.FindingsThe results indicate that the higher the firm's prior R&D investment, the higher its digital technology level, and thus the stronger its resistance to the epidemic. Moreover, compared with large-scale firms, small-scale firms have the advantage of strategic flexibility to technological changes, which can help them accumulate experience from R&D activities for digital transformation, thus attenuating the negative impact of the COVID-19 on firm performance. Finally, the results also show that digital technology mediates more strongly between R&D investment and firm performance in diversified firms than in centralized firms.Originality/valueThe study builds a mediation model to reveal the process mechanism through which R&D investment affects firm performance via digital technology. Firm size and diversification are then innovatively introduced as situational factors to build the moderated mediation model, which opens up a new perspective for understanding the effect of firm internal factors on the relationship between R&D investment, digital transformation and firm performance.
{"title":"Organizational resilience under COVID-19: the role of digital technology in R&D investment and performance","authors":"Feiyang Guan, T. Wang, Liqing Tang","doi":"10.1108/imds-04-2022-0220","DOIUrl":"https://doi.org/10.1108/imds-04-2022-0220","url":null,"abstract":"PurposeThis study aims at the sudden outbreak of COVID-19, which had an unprecedented negative impact on the Chinese economy, with firms being affected most. Firms differ in terms of their specific internal environment, shaping their ability to respond to the outbreak, so the impact may also vary.Design/methodology/approachIn this paper Chinese listed firms are selected as samples to investigate the mediating effect of prior digital technology on the relationship between R&D (research and development) investment (funds and staff) and firm performance during the epidemic. Firm size and diversification are then introduced as moderating variables to explore the conditional mediating effect of digital technology.FindingsThe results indicate that the higher the firm's prior R&D investment, the higher its digital technology level, and thus the stronger its resistance to the epidemic. Moreover, compared with large-scale firms, small-scale firms have the advantage of strategic flexibility to technological changes, which can help them accumulate experience from R&D activities for digital transformation, thus attenuating the negative impact of the COVID-19 on firm performance. Finally, the results also show that digital technology mediates more strongly between R&D investment and firm performance in diversified firms than in centralized firms.Originality/valueThe study builds a mediation model to reveal the process mechanism through which R&D investment affects firm performance via digital technology. Firm size and diversification are then innovatively introduced as situational factors to build the moderated mediation model, which opens up a new perspective for understanding the effect of firm internal factors on the relationship between R&D investment, digital transformation and firm performance.","PeriodicalId":13427,"journal":{"name":"Ind. Manag. Data Syst.","volume":"53 1","pages":"41-63"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80403567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-21DOI: 10.1108/imds-01-2022-0028
Xiaoming Zhang, Yangyan Shi, P. Zhang, Fang Xu, Chaozhe Jiang
PurposeThe purpose of this study is to explore mitigation measures for cash flow interruption during the epidemic and provide decision support to ensure the regular operation and robustness of the supply chain (SC).Design/methodology/approachConsidering the scenarios of production capacity and demand disruption during the epidemic, the authors adopt system dynamics (SD) to construct a three-echelon SC financial system consisting of a core manufacturer, a capital-constrained retailer and the customer. In different interruption scenarios, through the decision adjustments of stakeholders, the differences in performance are compared to explore solutions for SC robust optimization.FindingsThe results show that partial credit guarantee (PCG) could solve cash flow interruption and maintain the regular operation of the SC. During epidemic, with the product price increases, the revenue of stakeholders and the robustness are generally negatively correlated. But when the manufacturer's production capacity is fully interrupted, increasing product price is the right decision for the retailer and could simultaneously promote performance and robustness.Originality/valueThis paper primarily focuses on the PCG under the cash flow interruption caused by epidemics. The authors adopt the supply chain finance (SCF) theory and SD method to supplement and expand existing research on interruption management of SC. It is a pioneering study to explore the robustness of the SC financial system under disruptions.
{"title":"System dynamics modeling and robustness analysis for capital-constrained supply chain under disruption","authors":"Xiaoming Zhang, Yangyan Shi, P. Zhang, Fang Xu, Chaozhe Jiang","doi":"10.1108/imds-01-2022-0028","DOIUrl":"https://doi.org/10.1108/imds-01-2022-0028","url":null,"abstract":"PurposeThe purpose of this study is to explore mitigation measures for cash flow interruption during the epidemic and provide decision support to ensure the regular operation and robustness of the supply chain (SC).Design/methodology/approachConsidering the scenarios of production capacity and demand disruption during the epidemic, the authors adopt system dynamics (SD) to construct a three-echelon SC financial system consisting of a core manufacturer, a capital-constrained retailer and the customer. In different interruption scenarios, through the decision adjustments of stakeholders, the differences in performance are compared to explore solutions for SC robust optimization.FindingsThe results show that partial credit guarantee (PCG) could solve cash flow interruption and maintain the regular operation of the SC. During epidemic, with the product price increases, the revenue of stakeholders and the robustness are generally negatively correlated. But when the manufacturer's production capacity is fully interrupted, increasing product price is the right decision for the retailer and could simultaneously promote performance and robustness.Originality/valueThis paper primarily focuses on the PCG under the cash flow interruption caused by epidemics. The authors adopt the supply chain finance (SCF) theory and SD method to supplement and expand existing research on interruption management of SC. It is a pioneering study to explore the robustness of the SC financial system under disruptions.","PeriodicalId":13427,"journal":{"name":"Ind. Manag. Data Syst.","volume":"85 1","pages":"492-514"},"PeriodicalIF":0.0,"publicationDate":"2022-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83922795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-17DOI: 10.1108/imds-06-2022-0372
Mohit Goswami, F. Chan, M. Ramkumar, Yash Daultani, S. Pratap, Ankita Chhabra
PurposeIn this research, collaboration attributes related to the firm's intrinsic and extrinsic facets at pertinent levels (i.e. enterprise, strategic, operational, and tactical levels) for construction equipment OEMs (original equipment manufacturers) operating in India have been quantified and modeled.Design/methodology/approachFor modeling the intra-firm collaboration at respective organizational levels, relevant attributes have been populated employing literature review followed by subsequent validation from pertinent focus groups. The focus groups comprising professionals working in the construction and mining equipment industry in India aided us in estimating the extent of interdependencies and influences within/amongst collaboration attributes. The collaboration attributes and respective interdependencies/influences are modeled employing the concept of graph theory wherein the individual attributes are represented using vertices and influences/interdependencies are represented using edges. The collaboration indices resulting from the variable permanent matrix have been derived as well.FindingsScenario and subsequent sensitivity analysis are performed. This research discusses the significance and aspects related to various collaborative attributes and the interrelations amongst them. Further, the research also evolves quantitative measures of collaboration indices at enterprise, strategic, tactical and operational levels by employing a graph-theoretic approach (GTA). The authors have also extricated and discussed a number of meaningful implications from both the perspectives of interorganizational relationships (IORs) and the normative theory of organizations using a cross-case analysis of five firms having operations in India.Originality/valueThe research would aid organizations (particularly those belonging to the construction equipment sector) measure the efficacy of collaboration in respective value-chains at strategic, tactical and operational levels. From the theoretical perspective, the integration of the IORs and normative theory of organizations enables looking at the intra-firm collaboration problem from a multi-dimensional standpoint involving activities, performance measures, action initiation, communication, shades of top management, level of activity, etc.
{"title":"A joint modeling and exploratory framework for intra-firm collaboration within construction and mining equipment industry","authors":"Mohit Goswami, F. Chan, M. Ramkumar, Yash Daultani, S. Pratap, Ankita Chhabra","doi":"10.1108/imds-06-2022-0372","DOIUrl":"https://doi.org/10.1108/imds-06-2022-0372","url":null,"abstract":"PurposeIn this research, collaboration attributes related to the firm's intrinsic and extrinsic facets at pertinent levels (i.e. enterprise, strategic, operational, and tactical levels) for construction equipment OEMs (original equipment manufacturers) operating in India have been quantified and modeled.Design/methodology/approachFor modeling the intra-firm collaboration at respective organizational levels, relevant attributes have been populated employing literature review followed by subsequent validation from pertinent focus groups. The focus groups comprising professionals working in the construction and mining equipment industry in India aided us in estimating the extent of interdependencies and influences within/amongst collaboration attributes. The collaboration attributes and respective interdependencies/influences are modeled employing the concept of graph theory wherein the individual attributes are represented using vertices and influences/interdependencies are represented using edges. The collaboration indices resulting from the variable permanent matrix have been derived as well.FindingsScenario and subsequent sensitivity analysis are performed. This research discusses the significance and aspects related to various collaborative attributes and the interrelations amongst them. Further, the research also evolves quantitative measures of collaboration indices at enterprise, strategic, tactical and operational levels by employing a graph-theoretic approach (GTA). The authors have also extricated and discussed a number of meaningful implications from both the perspectives of interorganizational relationships (IORs) and the normative theory of organizations using a cross-case analysis of five firms having operations in India.Originality/valueThe research would aid organizations (particularly those belonging to the construction equipment sector) measure the efficacy of collaboration in respective value-chains at strategic, tactical and operational levels. From the theoretical perspective, the integration of the IORs and normative theory of organizations enables looking at the intra-firm collaboration problem from a multi-dimensional standpoint involving activities, performance measures, action initiation, communication, shades of top management, level of activity, etc.","PeriodicalId":13427,"journal":{"name":"Ind. Manag. Data Syst.","volume":"2 1","pages":"451-491"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90452791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-16DOI: 10.1108/imds-02-2022-0091
Du Ni, Ming K. Lim, Xingzhi Li, Ying Qu, Mei Yang
PurposeMonitoring corporate credit risk (CCR) has traditionally relied on such indicators as income, debt and inventory at a company level. These data are usually released on a quarterly or annual basis by the target company and include, exclusively, the financial data of the target company. As a result of this exclusiveness, the models for monitoring credit risk usually fail to account for some significant information from different sources or channels, like the data of its supply chain partner companies and other closely relevant data yet available from public networks, and it is these seldom used data that can help unveil the immediate CCR changes and how the risk is being propagated along the supply chain. This study aims to discuss the a forementioned issues.Design/methodology/approachGoing beyond the existing CCR prediction data, this study intends to address the impact of supply chain data and network activity data on CCR prediction, by integrating machine learning technology into the prediction to verify whether adding new data can improve the predictability.FindingsThe results show that the predictive errors of the datasets after adding supply chain data and network activity data to them are made the ever least. Moreover, intelligent algorithms like support vector machine (SVM), compared to traditionally used methods, are better at processing nonlinear datasets and mining complex relationships between multi-variable indicators for CCR evaluation.Originality/valueThis study indicates that bringing in more information of multiple data sources combined with intelligent algorithms can help companies prevent risk spillovers in the supply chain from causing harm to the company, and, as well, help customers evaluate the creditworthiness of the entity to lessen the risk of their investment.
{"title":"Monitoring corporate credit risk with multiple data sources","authors":"Du Ni, Ming K. Lim, Xingzhi Li, Ying Qu, Mei Yang","doi":"10.1108/imds-02-2022-0091","DOIUrl":"https://doi.org/10.1108/imds-02-2022-0091","url":null,"abstract":"PurposeMonitoring corporate credit risk (CCR) has traditionally relied on such indicators as income, debt and inventory at a company level. These data are usually released on a quarterly or annual basis by the target company and include, exclusively, the financial data of the target company. As a result of this exclusiveness, the models for monitoring credit risk usually fail to account for some significant information from different sources or channels, like the data of its supply chain partner companies and other closely relevant data yet available from public networks, and it is these seldom used data that can help unveil the immediate CCR changes and how the risk is being propagated along the supply chain. This study aims to discuss the a forementioned issues.Design/methodology/approachGoing beyond the existing CCR prediction data, this study intends to address the impact of supply chain data and network activity data on CCR prediction, by integrating machine learning technology into the prediction to verify whether adding new data can improve the predictability.FindingsThe results show that the predictive errors of the datasets after adding supply chain data and network activity data to them are made the ever least. Moreover, intelligent algorithms like support vector machine (SVM), compared to traditionally used methods, are better at processing nonlinear datasets and mining complex relationships between multi-variable indicators for CCR evaluation.Originality/valueThis study indicates that bringing in more information of multiple data sources combined with intelligent algorithms can help companies prevent risk spillovers in the supply chain from causing harm to the company, and, as well, help customers evaluate the creditworthiness of the entity to lessen the risk of their investment.","PeriodicalId":13427,"journal":{"name":"Ind. Manag. Data Syst.","volume":"13 1","pages":"434-450"},"PeriodicalIF":0.0,"publicationDate":"2022-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89510078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-10DOI: 10.1108/imds-02-2022-0114
Yu-Qian Zhu, Kritsapas Kanjanamekanant
PurposeRobotic process automation (RPA) has been widely implemented to automate digital tasks. The resulting new type of human–bot co-working environment, however, has been understudied. This paper investigated how the depth and breadth of RPA deployment impact employees' job autonomy and work intensification, as well as perceived RPA performance. It further examined how job autonomy, work intensification, and perceived RPA performance predict burnout and continuance intention to use RPA.Design/methodology/approachUsing data collected from online survey of 128 RPA users, whose organizations have already gone live on RPA, partial least squares is used in the validation of the conceptual model and analysis.FindingsThe analytical results indicate that RPA deployment breadth and depth affect work intensification differently, and RPA deployment breadth and depth significantly predict perceived RPA performance. While work intensification increases burnout, job autonomy alleviates the burnout of employees. Finally, job autonomy and perceived RPA performance are both positive predictors of continuance intention to use RPA.Originality/valueThis study contributes to the literature by investigating how co-working affects employees' autonomy and quality of work. It also advances the research on technology deployment by showing how deployment breadth and depth differently affect employees' evaluations of work-related aspects. Third, it extends the applicability of job demand-resource model into technology deployment and continuance technology use literature, by illustrating the importance of a job resource such as job autonomy. Finally, it provides firms with RPA implementation strategies.
{"title":"Human-bot co-working: job outcomes and employee responses","authors":"Yu-Qian Zhu, Kritsapas Kanjanamekanant","doi":"10.1108/imds-02-2022-0114","DOIUrl":"https://doi.org/10.1108/imds-02-2022-0114","url":null,"abstract":"PurposeRobotic process automation (RPA) has been widely implemented to automate digital tasks. The resulting new type of human–bot co-working environment, however, has been understudied. This paper investigated how the depth and breadth of RPA deployment impact employees' job autonomy and work intensification, as well as perceived RPA performance. It further examined how job autonomy, work intensification, and perceived RPA performance predict burnout and continuance intention to use RPA.Design/methodology/approachUsing data collected from online survey of 128 RPA users, whose organizations have already gone live on RPA, partial least squares is used in the validation of the conceptual model and analysis.FindingsThe analytical results indicate that RPA deployment breadth and depth affect work intensification differently, and RPA deployment breadth and depth significantly predict perceived RPA performance. While work intensification increases burnout, job autonomy alleviates the burnout of employees. Finally, job autonomy and perceived RPA performance are both positive predictors of continuance intention to use RPA.Originality/valueThis study contributes to the literature by investigating how co-working affects employees' autonomy and quality of work. It also advances the research on technology deployment by showing how deployment breadth and depth differently affect employees' evaluations of work-related aspects. Third, it extends the applicability of job demand-resource model into technology deployment and continuance technology use literature, by illustrating the importance of a job resource such as job autonomy. Finally, it provides firms with RPA implementation strategies.","PeriodicalId":13427,"journal":{"name":"Ind. Manag. Data Syst.","volume":"5 1","pages":"515-533"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88802848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-04DOI: 10.1108/imds-05-2022-0270
Zhimei Lei, Shanshan Cai, Lili Cui, Lin Wu, Yiwei Liu
PurposeUncovering the relationship between Industry 4.0 (I4.0) technologies and circular economy (CE) practices is critical not only for implementing CE but also for leveraging I4.0 to achieve sustainable development goals. However, the potential connection between them – especially how different I4.0 technologies may influence various CE practices – remains inadequately researched. The purpose of this study was to quantitatively explore the impacts of various I4.0 technologies on CE practices.Design/methodology/approachA mixed method consisting of a systematic literature review, content analysis, and social network analysis was adopted. First, 266 articles were selected and mined for contents of I4.0 technologies and CE practices; 27 I4.0 technologies and 21 CE practices were identified. Second, 62 articles were found that prove the positive influence of I4.0 technologies on CE practices, and 124 relationships were identified. Third, based on evidence supporting the link between I4.0 technologies and CE practices, a two-mode network and two one-mode networks were constructed, and their network density and degree centrality indicators were analyzed.FindingsI4.0 technologies have a low application scope and degree for promoting CE. The adoption of a single I4.0 technology has limited effect on CE practices, and wider benefits can be realized through integrating I4.0 technologies. The Internet of Things (IoT), additive manufacturing, big data and analytics, and artificial intelligence (AI) are among the top technologies promoting CE implementation and reduction and recycling were identified as the main mechanism. The integration of these technologies is the most popular and effective. Twelve CE practices were identified to be the most widely implemented and supported by I4.0 technologies.Research limitations/implicationsFirst, only journal articles, reviews, and online publications written in English were selected, excluding articles published in other languages. Therefore, the results obtained only represent a specific group of scholars, which may be fragmented to a certain extent. Second, because the extraction of the impact of I4.0 on CE mainly relies on a manual literature review, this paper only provides the statistics of the number of publications involving relationships, while lacking the weight measurement of relationships.Originality/valueA comprehensive, quantitative, and visual analysis method was employed to unveil the current implementation levels of I4.0 technologies and CE practices. Further, it was explored how different I4.0 technologies can affect various CE aspects, how different I4.0 technologies are integrated to promote CE realization, and how various CE practices are implemented simultaneously by I4.0 technologies.
{"title":"How do different Industry 4.0 technologies support certain Circular Economy practices?","authors":"Zhimei Lei, Shanshan Cai, Lili Cui, Lin Wu, Yiwei Liu","doi":"10.1108/imds-05-2022-0270","DOIUrl":"https://doi.org/10.1108/imds-05-2022-0270","url":null,"abstract":"PurposeUncovering the relationship between Industry 4.0 (I4.0) technologies and circular economy (CE) practices is critical not only for implementing CE but also for leveraging I4.0 to achieve sustainable development goals. However, the potential connection between them – especially how different I4.0 technologies may influence various CE practices – remains inadequately researched. The purpose of this study was to quantitatively explore the impacts of various I4.0 technologies on CE practices.Design/methodology/approachA mixed method consisting of a systematic literature review, content analysis, and social network analysis was adopted. First, 266 articles were selected and mined for contents of I4.0 technologies and CE practices; 27 I4.0 technologies and 21 CE practices were identified. Second, 62 articles were found that prove the positive influence of I4.0 technologies on CE practices, and 124 relationships were identified. Third, based on evidence supporting the link between I4.0 technologies and CE practices, a two-mode network and two one-mode networks were constructed, and their network density and degree centrality indicators were analyzed.FindingsI4.0 technologies have a low application scope and degree for promoting CE. The adoption of a single I4.0 technology has limited effect on CE practices, and wider benefits can be realized through integrating I4.0 technologies. The Internet of Things (IoT), additive manufacturing, big data and analytics, and artificial intelligence (AI) are among the top technologies promoting CE implementation and reduction and recycling were identified as the main mechanism. The integration of these technologies is the most popular and effective. Twelve CE practices were identified to be the most widely implemented and supported by I4.0 technologies.Research limitations/implicationsFirst, only journal articles, reviews, and online publications written in English were selected, excluding articles published in other languages. Therefore, the results obtained only represent a specific group of scholars, which may be fragmented to a certain extent. Second, because the extraction of the impact of I4.0 on CE mainly relies on a manual literature review, this paper only provides the statistics of the number of publications involving relationships, while lacking the weight measurement of relationships.Originality/valueA comprehensive, quantitative, and visual analysis method was employed to unveil the current implementation levels of I4.0 technologies and CE practices. Further, it was explored how different I4.0 technologies can affect various CE aspects, how different I4.0 technologies are integrated to promote CE realization, and how various CE practices are implemented simultaneously by I4.0 technologies.","PeriodicalId":13427,"journal":{"name":"Ind. Manag. Data Syst.","volume":"7 21","pages":"1220-1251"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91441977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-02DOI: 10.1108/imds-03-2022-0167
Mohammad Daneshvar Kakhki, Alan I. Rea, Mehdi Deiranlou
PurposeThis study aims to analyze the mediating role of data analytics management capability (DAMC) in the relationship between supply chain integration (SCI) and supply chain agility, adaptability and alignment (Triple-A). It also studies the role of Triple-A supply chains in performance improvement. We develop and present a model based on our proposition and informed by the dynamic capabilities perspective.Design/methodology/approachThe authors employ meta-analytic structural equation modeling to test the proposed model by analyzing reported statistics of 117 published studies.FindingsThis study’s results describe why some prior research findings are contradictory. For example, researchers have posited mixed findings on the impact of SCI on agility. The results show that SCI and agility do not have a significant direct association, and DAMC mediates their indirect relationship.Originality/valueThe impact of SCI on performance is debatable. SCI permits access to shared resources for competitive advantage; conversely, SCI-induced rigidity may reduce supply chain agility and adaptability. Informed by dynamic capabilities theory, the authors demonstrate that DAMC positively mediates the impact of SCI on performance.
{"title":"Data analytics dynamic capabilities for Triple-A supply chains","authors":"Mohammad Daneshvar Kakhki, Alan I. Rea, Mehdi Deiranlou","doi":"10.1108/imds-03-2022-0167","DOIUrl":"https://doi.org/10.1108/imds-03-2022-0167","url":null,"abstract":"PurposeThis study aims to analyze the mediating role of data analytics management capability (DAMC) in the relationship between supply chain integration (SCI) and supply chain agility, adaptability and alignment (Triple-A). It also studies the role of Triple-A supply chains in performance improvement. We develop and present a model based on our proposition and informed by the dynamic capabilities perspective.Design/methodology/approachThe authors employ meta-analytic structural equation modeling to test the proposed model by analyzing reported statistics of 117 published studies.FindingsThis study’s results describe why some prior research findings are contradictory. For example, researchers have posited mixed findings on the impact of SCI on agility. The results show that SCI and agility do not have a significant direct association, and DAMC mediates their indirect relationship.Originality/valueThe impact of SCI on performance is debatable. SCI permits access to shared resources for competitive advantage; conversely, SCI-induced rigidity may reduce supply chain agility and adaptability. Informed by dynamic capabilities theory, the authors demonstrate that DAMC positively mediates the impact of SCI on performance.","PeriodicalId":13427,"journal":{"name":"Ind. Manag. Data Syst.","volume":"25 1","pages":"534-555"},"PeriodicalIF":0.0,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89059996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-18DOI: 10.1108/imds-05-2021-0310
Shiqi Liu, Tao Shen, Yuliang Wu, Yang Chen, Yifan Li, Yumeng Tang, Lu Lu
PurposeExtant research has paid considerable attention to the effects of enterprise social media (ESM) on employees' work attitudes and outcomes, yet the authors know little about the influence of job demands arising from the implementation of ESM. Drawing on resource allocation theory, the purpose of this study is to unravel how ESM-related job demands influence employee outcomes.Design/methodology/approachThis study conducts a two-wave time-lagged survey of 223 employees from 53 teams in 14 financial service firms in China to test the conceptual model.FindingsThe findings of this paper indicate that ESM-related job demands have indirect effects on employee outcomes (i.e. job satisfaction and work–family conflict), and emotional exhaustion plays an intermediary role in these relationships. Specifically, ESM-related job demands have a U-shaped effect on emotional exhaustion.Originality/valueThis study combines job demands with ESM research and clarifies the mechanism behind how ESM-related job demands at different intensity affect employee outcomes from a new perspective. Moreover, this study’s findings suggest several beneficial courses of action for managers to take advantage of ESM.
{"title":"The effects of job demands of enterprise social media on employees' outcomes: a curvilinear mediated model","authors":"Shiqi Liu, Tao Shen, Yuliang Wu, Yang Chen, Yifan Li, Yumeng Tang, Lu Lu","doi":"10.1108/imds-05-2021-0310","DOIUrl":"https://doi.org/10.1108/imds-05-2021-0310","url":null,"abstract":"PurposeExtant research has paid considerable attention to the effects of enterprise social media (ESM) on employees' work attitudes and outcomes, yet the authors know little about the influence of job demands arising from the implementation of ESM. Drawing on resource allocation theory, the purpose of this study is to unravel how ESM-related job demands influence employee outcomes.Design/methodology/approachThis study conducts a two-wave time-lagged survey of 223 employees from 53 teams in 14 financial service firms in China to test the conceptual model.FindingsThe findings of this paper indicate that ESM-related job demands have indirect effects on employee outcomes (i.e. job satisfaction and work–family conflict), and emotional exhaustion plays an intermediary role in these relationships. Specifically, ESM-related job demands have a U-shaped effect on emotional exhaustion.Originality/valueThis study combines job demands with ESM research and clarifies the mechanism behind how ESM-related job demands at different intensity affect employee outcomes from a new perspective. Moreover, this study’s findings suggest several beneficial courses of action for managers to take advantage of ESM.","PeriodicalId":13427,"journal":{"name":"Ind. Manag. Data Syst.","volume":"23 1","pages":"409-433"},"PeriodicalIF":0.0,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81369558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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