Purpose: This study aims to examine the role of big data analytics (BDA) powered by artificial intelligence (AI) in improving sustainable performance (SP) through green supply chain collaboration (GSCC), sustainable manufacturing (SM) and environmental process integration (EPI). Design/methodology/approach: Data was collected from 249 supply chain professionals working at various manufacturing firms, and hypotheses were tested through a quantitative method using PLS-SEM with the help of SmartPLS version 4 to validate the measurement model. Findings: This study identified that BDA-AI significantly and positively affects GSCC, SM and EPI. Similarly, the results showed that GSCC significantly and positively affects SP. At the same time, SM and EPI have an insignificant effect on SP. The GSCC found a significant relationship between BDA-AI and SP for mediation. However, SM and environmental performance integration did not mediate the relationship between BDA and AI and SP. Originality/value: This research evaluated a second-order model and tested SP in conjunction with the dynamic capability theory in the manufacturing industry of Pakistan. Therefore, this research could be beneficial for researchers, manufacturers and policymakers to attain sustainable goals by implementing the BDA-AI in the supply chain.

The trustworthiness of qualitative data has been debatable, yet it has strong support from its supporters. However, the importance and worth of qualitative data cannot be undermined. This paper presents a critical review of the trustworthiness of the qualitative data. The degree of trustworthiness of qualitative research can be measured by ensuring the credibility, transferability, dependability, and conformability of research design, process, and action. The guarantee of trustworthiness in qualitative research is more complex than in quantitative research due to its subjective nature. Many researchers and experts denied the generalizability of qualitative research. However, few researchers, like Guba (1985), developed a widely accepted model and strategies to ensure the trustworthiness and generalizability of qualitative research. Credibility is like internal validity in quantitative analysis and provides the actual data about the phenomenon. Transferability shows the degree of application of the research finding in other exact natures of context, people, groups, and settings. If the findings of one study are replicated in a similar population, condition, or context, then the findings are dependable. Neutrality is the degree of fairness of results consisting of purity of initial responses and bias-free.

Purpose: The purpose of this study is to present a complete framework that defines the link between choices and decision criteria based on existing research on digital influencers (DIs) connected to consumer purchase intentions. The primary goal of this article is to assess the effect of DIs on customer purchase intentions via the creation of an integrated knowledge-based system (KBS).Design/methodology/approach: The suggested KBS is based on the fuzzy analytic hierarchy process (AHP), which creates a link between DI elements and their overall effect on consumer purchase intentions.Findings: With the help of a KBS, the performance of DIs may be evaluated. It demonstrates the link between choices connected to factors and decision criteria of various variables, demonstrating the beneficial effect of DIs in molding customer purchase intentions in the organic skincare industry.Practical implications: The proposed KBS would aid marketing managers and decision makers in assessing the effect of DIs on customer purchase intentions. This research would also give decision makers with extensive information on influencer marketing and crucial elements that have a significant effect on customer purchase intentions.Originality/value: This is the first research to employ the fuzzy AHP methodology and KBS in relation to influencers' effect. No prior research has targeted the organic skincare industry to assess the effect of Internet influencers on consumer purchase intentions. Furthermore, the KBS offers a holistic and complete way to studying influencers' effect on cost per impression (CPI) by establishing a linkage between choices and decision criteria.

Purpose: Supply chain (SC) management is more challenging than ever. Significantly, the pandemic has provoked global and economic destruction that appeared in the manufacturing industry as a “black swan.” Therefore, the purpose of this study was to examine the role of information processing and digital supply chain in supply chain resilience through supply chain risk management. Design/methodology/approach: This study examines SC risk management and resilience from an information processing theory perspective. The authors used data collected from 251 SC professionals in the manufacturing industry, and the authors used a quantitative method to analyze the data. The data was analyzed using partial least squares-structural equation modeling. To confirm the higher-order measurement model, the authors used SmartPLS version 4 software. Findings: This study found that information processing capability (disruptive orientation and visibility in high-order) and digital SC significantly and positively affect SC risk management and resilience. Similarly, SC risk management positively mediates the relationship between information processing capability and digital SC. However, information processing capability was found to have a more substantial effect on SC risk management than the digital SC. Research limitations/implications: This study has both academic and practical contributions. It contributed to existing information processing theory, and manufacturing firms can improve their performance by proactively responding to SC disruptions by recognizing the pivotal role of study variables in risk management for a resilient SC. Originality/value: The conceptual model of this study is based on information processing theory, which asserts that synchronizing information processing capabilities and digital SCs allows a firm to deal with unplanned events. SC disruption orientation and visibility are considered risk controllers as they allow the firms to be more proactive. An integrated model of conceptualizing the disruption orientation, visibility (higher-order) and digital SC with information processing theory makes this research novel.

Purpose: Service quality in academics is imperatively crucial. Therefore, the purpose of the current research is to examine the effect of academic service quality factors in higher education institutions which mainly focuses on students' satisfaction and the extent of recommending the same institution to other students. Design/methodology/approach: This research evaluated the effect of service quality on student satisfaction and word of mouth (WOM). Data were collected from 200 respondents from various business schools, and hypotheses were tested through a quantitative method using partial least square-structural equation modelling (PLS-SEM) with the help of SmartPLS to validate the measurement model. Findings: The findings of the current research revealed that all three components of service quality including information quality, teaching quality and service efficiency have a significant impact on student satisfaction. Results also indicated that student satisfaction is significantly linked with WOM which means satisfied students spread positive WOM and recommend the same institution to other students. Originality/value: This research provides an extension towards the body of knowledge on the issue, which will be used in future detailed and critical examinations. The focus of the research was on SERVQUAL and how, when and why business schools may spot possible applications that can have an influence on their admissions by WOM.

The organizational performance at public hospitals differs from the corporate sector and faces maladministration. Further, performance at public hospitals is crucial as it directly affects human lives and endeavors for accessibility to medicines and equipment. This research is designed to determine the mediating role of inventory control practices in proficiency and organizational performance at public hospitals. This paper uses a quantitative research with a survey method using multistage cluster sampling which was performed on a sample size of 200. Exploratory Factor Analysis and Confirmatory Factor Analysis validated the model for Structural Equation Modeling. The findings of this research indicate a significant positive effect and full mediation of inventory control practices between the study variables. Further, the significance indicated that competent and well-trained staff manages inventory efficiently for improved organizational performance through better service quality and reduced cost. An integrated second-order model of this research will benefit the public hospitals, healthcare industry at large, organizations managing mega structured inventory, and to the body of knowledge.