Pub Date : 2023-08-24DOI: 10.1108/jeim-06-2023-0298
Kai-Xiang Sun, K. Ooi, G. Tan, Voon‐Hsien Lee
PurposeThis research examines the relationships between the components of supply chain integration (SCI) (i.e. internal integration (INI), customer integration (CI) and supplier integration (SI)), supply chain risk management (SCRM) and supply chain resilience (SCRE), with disruption impact (DI) as the moderator, among small and medium-sized enterprises (SMEs).Design/methodology/approach271 useable data were collected from Chinese SMEs to test the research model with two statistical approaches of PLS-SEM and ANN analysis.FindingsResults show that SCI (i.e. INI, CI and SI) positively affects SCRM, and subsequently affects SCRE. Moreover, SCRM has also been found to fully mediate the relationship between INI, CI and SI with SCRE. Additionally, DI was also found to moderate the relationship between SCRM and SCRE.Research limitations/implicationsThis study expands the supply chain management-related knowledge by empirically validating the mediating role of SCRM between the elements of SCI and SCRE, as well as empirically identifying DI as the moderator between SCRM and SCRE.Practical implicationsThe findings offer valuable understanding that can guide SME managers, owners and stakeholders in developing strategies for integrating with customers, suppliers and internal departments, as well as implementing SCRM practices to enhance SCRE performance.Originality/valueThe research expands the existing literature on the elements of SCI and SCRM in maintaining SCRE from an Asian developing country's perspective.
{"title":"Enhancing supply chain resilience in SMEs: a deep Learning-based approach to managing Covid-19 disruption risks","authors":"Kai-Xiang Sun, K. Ooi, G. Tan, Voon‐Hsien Lee","doi":"10.1108/jeim-06-2023-0298","DOIUrl":"https://doi.org/10.1108/jeim-06-2023-0298","url":null,"abstract":"PurposeThis research examines the relationships between the components of supply chain integration (SCI) (i.e. internal integration (INI), customer integration (CI) and supplier integration (SI)), supply chain risk management (SCRM) and supply chain resilience (SCRE), with disruption impact (DI) as the moderator, among small and medium-sized enterprises (SMEs).Design/methodology/approach271 useable data were collected from Chinese SMEs to test the research model with two statistical approaches of PLS-SEM and ANN analysis.FindingsResults show that SCI (i.e. INI, CI and SI) positively affects SCRM, and subsequently affects SCRE. Moreover, SCRM has also been found to fully mediate the relationship between INI, CI and SI with SCRE. Additionally, DI was also found to moderate the relationship between SCRM and SCRE.Research limitations/implicationsThis study expands the supply chain management-related knowledge by empirically validating the mediating role of SCRM between the elements of SCI and SCRE, as well as empirically identifying DI as the moderator between SCRM and SCRE.Practical implicationsThe findings offer valuable understanding that can guide SME managers, owners and stakeholders in developing strategies for integrating with customers, suppliers and internal departments, as well as implementing SCRM practices to enhance SCRE performance.Originality/valueThe research expands the existing literature on the elements of SCI and SCRM in maintaining SCRE from an Asian developing country's perspective.","PeriodicalId":47889,"journal":{"name":"Journal of Enterprise Information Management","volume":" ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43078069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-15DOI: 10.1108/jeim-06-2021-0267
Xin Tian, Wu He, Yuming He, Steve Albert, Michael Howard
PurposeThis study aims to examine how different hospitals utilize social media to communicate risk information about COVID-19 with the communities they serve, and how hospitals' social media messaging (firm-generated content and their local community's responses (user-generated content) evolved with the COVID-19 outbreak progression.Design/methodology/approachThis research proposes a healthcare-specific social media analytics framework and studied 68,136 tweets posted from November 2019 to November 2020 from a geographically diverse set of ten leading hospitals' social media messaging on COVID-19 and the public responses by using social media analytics techniques and the health belief model (HBM).FindingsThe study found correlations between some of the HBM variables and COVID-19 outbreak progression. The findings provide actionable insight for hospitals regarding risk communication, decision making, pandemic awareness and education campaigns and social media messaging strategy during a pandemic and help the public to be more prepared for information seeking in the case of future pandemics.Practical implicationsFor hospitals, the results provide valuable insights for risk communication practitioners and inform the way hospitals or health agencies manage crisis communication during the pandemic For patients and local community members, they are recommended to check out local hospital's social media sites for updates and advice.Originality/valueThe study demonstrates the role of social media analytics and health behavior models, such as the HBM, in identifying important and useful data and knowledge for public health risk communication, emergency responses and planning during a pandemic.
{"title":"Using health belief model and social media analytics to develop insights from hospital-generated twitter messaging and community responses on the COVID-19 pandemic","authors":"Xin Tian, Wu He, Yuming He, Steve Albert, Michael Howard","doi":"10.1108/jeim-06-2021-0267","DOIUrl":"https://doi.org/10.1108/jeim-06-2021-0267","url":null,"abstract":"PurposeThis study aims to examine how different hospitals utilize social media to communicate risk information about COVID-19 with the communities they serve, and how hospitals' social media messaging (firm-generated content and their local community's responses (user-generated content) evolved with the COVID-19 outbreak progression.Design/methodology/approachThis research proposes a healthcare-specific social media analytics framework and studied 68,136 tweets posted from November 2019 to November 2020 from a geographically diverse set of ten leading hospitals' social media messaging on COVID-19 and the public responses by using social media analytics techniques and the health belief model (HBM).FindingsThe study found correlations between some of the HBM variables and COVID-19 outbreak progression. The findings provide actionable insight for hospitals regarding risk communication, decision making, pandemic awareness and education campaigns and social media messaging strategy during a pandemic and help the public to be more prepared for information seeking in the case of future pandemics.Practical implicationsFor hospitals, the results provide valuable insights for risk communication practitioners and inform the way hospitals or health agencies manage crisis communication during the pandemic For patients and local community members, they are recommended to check out local hospital's social media sites for updates and advice.Originality/valueThe study demonstrates the role of social media analytics and health behavior models, such as the HBM, in identifying important and useful data and knowledge for public health risk communication, emergency responses and planning during a pandemic.","PeriodicalId":47889,"journal":{"name":"Journal of Enterprise Information Management","volume":" ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47122413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}