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This study aims to investigate how the management of supply chain information impacts the performance of the supply chain. This relationship is explored by considering the mediating influence of internal and external integration, which is underpinned by the principles of resource-based theory.

Data was collected from 144 supply chain professionals across different manufacturing companies. The proposed hypotheses were subjected to quantitative analysis using partial least squares structural equation modeling, and the validity of the measurement model was assessed with the assistance of SmartPLS software.

This study identified that information management (IM) significantly and positively affects internal integration (II), customer integration (CI) and supply chain performance (SCP). Similarly, the results showed that II significantly and positively affects SCP. Likewise, CI positively and significantly affected SCP. At the same time, II found a positive yet significant mediating role in the relationship between IM and SCP. CI was found to play a positive but insignificant mediating role in the relationship between IM and SCP.

This research evaluated a second-order model and tested the SCP in conjunction with the resource-based view in the manufacturing industry. This research could be beneficial for researchers, manufacturers and policymakers to attain organizational objectives by implementing an efficient IM system for better integration and SCP.

This study aims to explore the critical application areas of radio frequency identification (RFID) technology for sustainable buildings.

The quantitative research approach was adopted through a structured questionnaire administered to relevant stakeholders of construction projects. The data collected were analysed with the exploratory factor analysis, relative importance index (RII) and fuzzy synthetic evaluation (FSE).

The study’s results have categorised the crucial areas of application where construction industry stakeholders should focus their attention. These areas are divided into four categories: management technologies, production technologies, sensing technologies and monitoring technologies. The findings from the FSE indicate that monitoring technologies represent the most significant category, whereas management technologies rank as the least significant. Moreover, the RII analysis highlights that tools management stands out as the most important application of RFID, while dispute resolution emerges as the least significant RFID application.

The study establishes the core areas of RFID application and their benefits to sustainable buildings. Consequently, it helps stakeholders (consultants, clients and contractors) to examine the RFID application areas and make informed decision on sustainable construction. Furthermore, it provides systematic proof that can aid the implementation of RFID in developing countries.

The study provides an insight into the possible application areas and benefits of RFID technology in the construction industry of developing countries. It also developed a conceptual frame for the critical application areas of RFID technology in the construction industry of developing countries.

This study aims to address a systematic literature review (SLR) using bibliometrics on the relationship between academic integrity and artificial intelligence (AI), to bridge the scattering of literature on this topic, given the challenge and opportunity for the educational and academic community.

This review highlights the enormous social influence of COVID-19 by mapping the extensive yet distinct and fragmented literature in AI and academic integrity fields. Based on 163 publications from the Web of Science, this paper offers a framework summarising the balance between AI and academic integrity.

With the rapid advancement of technology, AI tools have exponentially developed that threaten to destroy students' academic integrity in higher education. Despite this significant interest, there is a dearth of academic literature on how AI can help in academic integrity. Therefore, this paper distinguishes two significant thematical patterns: academic integrity and negative predictors of academic integrity.

This study also presents several contributions by showing that tools associated with AI can act as detectors of students who plagiarise. That is, they can be useful in identifying students with fraudulent behaviour. Therefore, it will require a combined effort of public, private academic and educational institutions and the society with affordable policies.

This study proposes a new, innovative framework summarising the balance between AI and academic integrity.

The purpose of this study is to present a methodical procedure on how to prepare event logs and analyse them through process mining, statistics and visualisations. The aim is to derive roots and patterns of quality deviations and non-conforming finished products as well as best practice facilitating employee training in the food processing industry. Thereby, a key focus is on recognising tacit knowledge hidden in event logs to improve quality processes.

This study applied process mining to detect root causes of quality deviations in operational process of food production. In addition, a data-ecosystem was developed which illustrates a continuous improvement feedback loop and serves as a role model for other applications in the food processing industry. The approach was applied to a real-case study in the processed cheese industry.

The findings revealed practical and conceptional contributions which can be used to continuously improve quality management (QM) in food processing. Thereby, the developed data-ecosystem supports production and QM in the decision-making processes. The findings of the analysis are a valuable basis to enhance operational processes, aiming to prevent quality deviations and non-conforming finished products.

Process mining is still rarely used in the food industry. Thereby, the proposed method helps to identify tacit knowledge in the food processing industry, which was shown by the framework for the preparation of event logs and the data ecosystem.

The digital direct-to-consumer (D2C) approach has seen widespread adoption across many industries, but its application to regional food products has been slower. This study aims to identify and rank the most significant challenges to the widespread use of digital D2C for regional food products.

A multi-criteria decision-making method called a fuzzy analytic hierarchy process (FAHP) was used to determine the order in which these obstacles were evaluated. Thirty-five barriers were identified and categorized into six categories named technological, operational, sociocultural, financial, market and institutional and regulatory barriers.

Market barriers ranked as the top barrier, and the technological barrier ranked the least significant amongst the main barrier categories for the adoption of digital D2C model for the regional food products. Lack of consumer awareness ranked number one globally, and lack of government subsidies ranked the least amongst the thirty-five identified barriers. Operational barriers came out to be second most significant barrier followed by institutional and regulatory barrier, sociocultural barrier, financial barrier and technological barrier.

The findings of this research were derived through a numerical examination of data gathered from the Indian setting. It follows that the technological, sociocultural, financial, market, operational and institutional constraints, among others, outlined here are all unique to India. Because of the unique nature of the Indian setting, the results of this study can only be used there and not elsewhere. It is possible that future research will broaden the aims of this one and refine its methodology. Digital D2C adoption for regional food products may be prioritized and ranked using quantitative and qualitative data sources like ANP and TOPSIS. It is possible that similar studies may be conducted in nations which have a different set of operational, technological, sociocultural market and financial and regulatory barriers. Conceptual framework can be formed by integrating TAM and TPB to understand the buying behavior of regional food products via digital D2C.

This research is the first to identify challenges to the widespread use of the digital D2C model for regional food products. Policymakers and other interested parties can use this information to better understand the difficulties of expanding the distribution of regional food products beyond their immediate regions.

This paper aims to recommend a method entitled “lean process mining (LPM)” for mapping, analyzing and improving the material/information flows in the value stream of manufacturing processes.

The method is developed based on literature review and in-depth explorative research in value stream mapping and process mining approaches. The proposed LPM framework consists of three phases including as-realized process state, improvement strategies and reengineered process state. Hence, firstly, extracts the as-realized model, measures the identified wastes and identifies the sources of wastes. Secondly, implements prediction-recommendation-prevention strategies. Thirdly, reengineers the process model and measures the improved wastes.

It presents the applicability of the proposed approach in (1) online observation of manufacturing process behavior and tracing the process deviations dynamically in real time to identify the sources of waste; (2) avoiding defective products occurring during the production and eliminating the relevant derived wastes including wasted material, wasted energy, waste of labor, excess inventory, increased production lead time and wasted operational costs.

The practical application of LPM is illustrated through implementing it in a real-life manufacturing case. The outcomes prove the remarkable applicability of this method in lean manufacturing to avoid waste occurrence in the value stream.

This paper aims to integrate zero defect manufacturing (ZDM) with process mining (PM) to avoid defect occurrence during production and improve sustainability.

The method is developed based on literature review in ZDM and PM. It uses PM for process discovery as an initial strategy in priority to predict-prevent strategies of ZDM.

It presents the applicability of the proposed approach in observing manufacturing process behavior, identifying dynamic causes of defects during production, predicting the time of defect occurrence and preventing defective products. It also identifies, explains and measures criteria for environmental, social and economic pillars of sustainability affected by defects and presents the impacts of the proposed approach on sustainability improvement.

The extended view of this research, as well as its analytical approach, helps practitioners to develop their ZDM and PM approaches more holistically. The practical application of this research is illustrated through implementing it in a real-life manufacturing case, where the outcomes prove its applicability in avoiding defect occurrence and improving all three pillars of sustainability.

The objective of this paper is to investigate the digital transformation (DT) trends in higher education institutes (HEI) (from 2010 to 2023) and examine the role of upcoming technologies in the HEI and their impact on user experience and social inclusiveness.

The middle-range research method is adopted, wherein the Scopus database is used for publication searches. The bibliometric analysis is done on a dataset of 408 articles to determine research trends in DT, top-cited articles, top journals, key authors, country-wise contribution, keyword analysis, bibliographic coupling and co-occurrence analysis. Additionally, content analysis was done on 51 articles that were finally selected to identify the upcoming DT themes in HEI.

Three key themes emerged from the analysis: digital technologies (for teaching, learning, assessment and administration), user experience and social inclusiveness, based on which we propose a conceptual model for DT in HEI.

This study contributes to the ongoing studies in the following way: (1) it proposes a conceptual model for DT in HEI. (2) It provides an understanding of the impact of DT on user experience and social inclusiveness in the context of HEI. (3) It paves the way for future research by providing future research themes, future research directions and future research questions, providing a groundwork for upcoming investigations which will help researchers develop this area further.

This study aims to explore the characteristics of the Love wave propagation through a heterogeneous hydrogel layer bounded to a functionally graded PFRC substrate while determining the displacement and electric potential in a di-electrically faintly conducting, mechanically compliant interface.

In order to calculate the value of response variables, the Wave mode method has been used. The governing modeled equations are non-homogeneous. The analytical solution has been obtained by deploying the boundary conditions.

The effect of thickness on the field variables for different values of variation parameter, mechanical constant of proportionality, electrical constant of proportionality and volume fraction of the PFRC substrate is examined for both, electrically open and short cases.

To the best of authors' knowledge, no attempt has been made to analyze the propagation characteristics of Love wave through the heterogeneous hydrogel Layer bedded over the functionally graded piezoelectric fiber-reinforced composites substrate.

We examine the twin deficits and the direction of its movement for the case of Bolivia, a natural resource-dependent country, using the database of (Kehoe et al., 2019) from 1960 to 2019.

We combine a structural vector autoregression (SVAR) model with a dynamic stochastic general equilibrium (DSGE) model to understand the transmission mechanisms.

Our results suggest the existence of twin deficits in Bolivia; however, causality in the Mundell-Fleming sense does not hold. While fiscal policy shocks explain current account deficits, current account shocks have a stronger effect over fiscal deficit. In fact, only 23% of the variance of current account forecast errors is explained by fiscal policy shocks; in contrast, 45% of the variance of the fiscal deficit is explained by current account shocks.

The study is for a specific case, which is a limitation; however, other country samples can be included.

Based on the results of the work, policies can be recommended and designed to cushion the effects of external shocks.

According to the literature available for the Bolivian case, our work constitutes a significant contribution and, therefore, is original for this specific case.