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Supply chains (SCs) have been growingly virtualized in response to the market challenges and opportunities that are presented by new and cost-effective internet-based technologies today. This paper designed a virtual closed-loop supply chain (VCLSC) network based on multiperiod, multiproduct and by using the Internet of Things (IoT). The purpose of the paper is the optimization of the VCLSC network.

The proposed model considers the maximization of profit. For this purpose, costs related to virtualization such as security, energy consumption, recall and IoT facilities along with the usual costs of the SC are considered in the model. Due to real-world demand fluctuations, in this model, demand is considered fuzzy. Finally, the problem is solved using the Grey Wolf algorithm and Firefly algorithm. A numerical example and sensitivity analysis on the main parameters of the model are used to describe the importance and applicability of the developed model.

The findings showed that the Firefly algorithm performed better and identified more profit for the SC in each period. Also, the results of the sensitivity analysis using the IoT in a VCLSC showed that the profit of the virtual supply chain (VSC) is higher compared to not using IoT due to tracking defective parts and identifying reversible products. In proposed model, chain members can help improve chain operations by tracking raw materials and products, delivering products faster and with higher quality to customers, bringing a new level of SC efficiency to industries. As a result, VSCs can be controlled, programmed and optimized remotely over the Internet based on virtual objects rather than direct observation.

There are limited researches on designing and optimizing the VCLSC network. This study is one of the first studies that optimize the VSC networks considering minimization of virtual costs and maximization of profits. In most researches, the theory of VSC and its advantages have been described, while in this research, mathematical optimization and modeling of the VSC have been done, and it has been tried to apply SC virtualization using the IoT. Considering virtual costs in VSC optimization is another originality of this research. Also, considering the uncertainty in the SC brings the issue closer to the real world. In this study, virtualization costs including security, recall and energy consumption in SC optimization are considered.

1. Investigates the role of IoT for virtual supply chain profit optimization and mathematical optimization of virtual closed-loop supply chain (VCLSC) based on multiperiod, multiproduct with emphasis on using the IoT under uncertainty.

2. Considering the most important costs of virtualization of supply chain include: cost of IoT information security, cost of IoT energy consumption, cost of recall the production department, cost of IoT facilities.

3. Selection of the optimal suppliers in each period and determination of the price of each returned product in virtual supply chain.

4. Solving and validating the proposed model with two meta-heuristic algorithms (the Grey Wolf algorithm and Firefly algorithm).

Investigates the role of IoT for virtual supply chain profit optimization and mathematical optimization of virtual closed-loop supply chain (VCLSC) based on multiperiod, multiproduct with emphasis on using the IoT under uncertainty.

Considering the most important costs of virtualization of supply chain include: cost of IoT information security, cost of IoT energy consumption, cost of recall the production department, cost of IoT facilities.

Selection of the optimal suppliers in each period and determination of the price of each returned product in virtual supply chain.

Solving and validating the proposed model with two meta-heuristic algorithms (the Grey Wolf algorithm and Firefly algorithm).

The commonly used e-procurement systems still show unsatisfactory implementation outcomes because many organisations are still unable to effectively interpret the initial adoption decision. The e-procurement systems are generally developed at organisational level, but their usage is at individual level, by the employees particularly. This paper examined technology acceptance model’s (TAM) key antecedents, involving e-procurement systems usage by employees in their daily activities. This study aims to examine the impact of factors affecting e-procurement acceptance among users through the mediating role of users’ attitude. The commonly used e-procurement systems still show.

TAM was applied and expanded in this study, in exploring the factors impacting the employees’ e-procurement acceptance. This study used quantitative method, and questionnaires were distributed to 200 users in Jordanian public shareholding firms. The collected data were quantitatively analysed using PLS modelling.

Significant TAM relationships involving e-procurement were affirmed. The expanded TAM in the scrutiny of antecedents showed that content, processing and usability affected perceived usefulness, while perceived convenience did not affect the usefulness factor. Furthermore, it was noticed that perceived ease of use was affected by usability and training, while perceived connectedness was not affected by usability and training.

The results demonstrated the necessity of e-procurement training. Furthermore, at the start of the implementation stage, effective design on system navigation and system usability, and consistent support, could increase use effectiveness and acceptance. Also, expedient information and buyer–supplier product flows should be made available.

Most organizations invest a lot of time and money in installing e-procurement systems to deliver their goods at the right time and at the right price. However, many of these e-procurement systems have failed due to low acceptance among users. Thus, to the best of the authors’ knowledge, this is the first study that used TAM and theory of planned behaviour in examining the effects of perceived convenience, perceived connectedness, content, training, processing and usability factors, in Jordanian firms. Lastly, the focus of this study was on the individual employee’s acceptance, rather than on the organisational-level adoption, as the unit of analysis, to provide insight on how organisations can achieve maximally from e-procurement investments and from other comparable technologies of e-supply chain management.

Purpose: The study’s objective is to look at the link between money laundering and economic and financial performance, emphasising the effectiveness of the literature and possible later research directions using science mapping, which allows for scientific knowledge analysis.

Need for the Study: This study is related to a better understanding of the field’s historical evolution in terms of publications.

Methodology: This study used bibliometric approaches to analyse a sample of 660 studies from the Web of Science between 1994 and 2022, concentrating on keywords, author, paper, journal, and subject analysis. This study focused on performance analysis and scientific mapping of articles using the R package.

Findings: The empirical results indicated that the research field’s primary issues include corporate governance, fraud, machine learning, fraud detection, financial fraud, financial statement, corruption, earnings management, ethics, governance, financial reporting, bankruptcy, internal control, or performance. M. S. Beasly, D. B. Farber, E. M. Fich, R. Romano, and A. Shivdasani are the most well-known authors on the issue of money laundering and financial and economic performance. At the same time, the most typical journals are the Journal of Business Ethics, Journal of Money Laundering Control, Accounting Review, Journal of Financial Economics, and Journal of Corporate Finance.

Practical Implications: This study will act as a guide for researchers of various fields to evaluate the development of scientific publications in a particular theme over time, especially for those who are in the field of money laundering and financial performance.

Supply chain management (SCM)-embedded valuable resources, such as capital, raw-materials, products, partners, customers and finished inventories, where the evaluation of environmental texture and flexibilities are needed to perceive sustainability. The present study aims to identify and evaluate the directory of green and agile (G-A) attributes based on decision support framework (DSF) for identifying dominating measures in SCM.

DSF is developed by exploiting generalized interval valued trapezoidal fuzzy numbers (GIVTFNs). Two technical approaches, i.e. degree of similarity approach (DSA) and distance approach (DA) under the extent boundaries of GIVTFNs, are implicated for data analytics and for recognizing constructive G-A measures based on comparative study for robust decision. A fuzzy-based performance indicator, i.e. fuzzy performance important index (FPII), is presented to enumerate the weak and strong G-A characteristics to manage knowledge risks in allied business environment.

The modeling is illustrated from the insights of decision-makers for augmenting business value based on cognitive identification of measures, where the best performance score is identified by the “sustainable packaging” under the traits of green supply chain management (GSCM). “The use of Web-based applications” under the traits of agile supply chain management (ASCM) and “Outsourcing flexibility” under traits of ASCM is found as the second and third most significant performance characteristics for business sustainability. Additionally, the “Reutilization (recycling) and reprocessing” under GSCM in manufacturing and “Responsiveness and speed toward customers needs” under ASCM are found difficult in attainment.

The G-A evaluation will assist in attaining performance excellence in day-to-day operations and overall functioning. The outcomes will help executives to plan strategic objectives and attaining success.

To reinforce the capabilities of SCM, wide extent of G-A dimensions are presented, concept of FPII is reported to manage knowledge risks based on identification of strong attributes and two technical approaches, i.e. DSA and DA under GIVTFNs are presented for attaining robust decision and directing managerial decision-making process.

This chapter aims to list the literature that document the role of hospitality industry achieving Sustainable Development Goals (SDGs), and to summarize those contributions, related to the literature. Extensive literature review was also conducted to explore a critical analysis of sustainable digitalization of the hospitality industry.

The article has undertaken a systematic literature review of all the significant research area of almost last two decades. Keyword searches were performed in Google Scholar search engine, where timeframe of “2001–2023” was used to filter the desired article. Total 141 research articles were primarily identified after the initial search. After screening the articles for relevance or duplicates, finally 107 articles were considered for this study.

This study figures out those environment-related SDGs which is considered essential for the hospitality industry. This study found the importance of adopting digitalization in hospitality sector to build inclusive environment and providing seamless experience to customers while focusing on both positive and negative aspects associated with digital transformation.

Hospitality industry of numerous countries around the world are now exploring by implementing SDGs and Digitalization in their business practices. This study will provide insight to policymakers as development and usage of digital technologies and implementing SDGs in their practices are crucial for the sustainable transformation of hospitality industry. Sustainable transformation of hospitality sector not only improves services and helps us to make wiser choices when planning for a trip but also positively impact both physical and psychological well-being.

This study aims to compare machine learning models, datasets and splitting training-testing using data mining methods to detect financial statement fraud.

This study uses a quantitative approach from secondary data on the financial reports of companies listed on the Indonesia Stock Exchange in the last ten years, from 2010 to 2019. Research variables use financial and non-financial variables. Indicators of financial statement fraud are determined based on notes or sanctions from regulators and financial statement restatements with special supervision.

The findings show that the Extremely Randomized Trees (ERT) model performs better than other machine learning models. The best original-sampling dataset compared to other dataset treatments. Training testing splitting 80:10 is the best compared to other training-testing splitting treatments. So the ERT model with an original-sampling dataset and 80:10 training-testing splitting are the most appropriate for detecting future financial statement fraud.

This study can be used by regulators, investors, stakeholders and financial crime experts to add insight into better methods of detecting financial statement fraud.

This study proposes a machine learning model that has not been discussed in previous studies and performs comparisons to obtain the best financial statement fraud detection results. Practitioners and academics can use findings for further research development.

The coronavirus (COVID-19) is the worst pandemic in recent memory in terms of its economic and social impacts. Deadly second wave of COVID-19 in India shook the country and reshaped the ways organizations functions and societies behave. Medical infrastructure was unaffordable and unsupportive which created high distress in the Indian society, especially for poor. At this juncture, some pharmaceutical firms made a unique social investment when they reduced price of drugs used to treat COVID-19 patients. This study aims to examine how the market and the society respond to the price reduction announcement during the psychological distress of COVID-19.

Market reactions have been analyzed by conducting an event study on stock market data and visual analytics-based sentiment analysis on Twitter data.

Overall, this study finds positive abnormal returns on the day and around the day of event. Interestingly, this study finds that returns during the time of high distress are significantly higher. Sentiment analysis conveys that net sentiment is favorable to the pharmaceutical firms around the day of event and it sustains more during the time of high distress.

This study is unique in contributing to the business and industrial management literature by highlighting market reactions to social responsibility of business during the time of psychological distress in emerging economies.

Artificial intelligence (AI) is transforming consumers' experiences and how firms identify, create, nurture and manage interactive marketing relationships. However, most marketers do not have a clear understanding of what AI is and how it may mutually benefit consumers and firms. In this paper, the authors conduct an extensive review of the marketing literature, develop an AI framework for understanding value co-creation in interactive buyer–seller marketing relationships, identify research gaps and offer a future research agenda.

The authors first conduct an extensive literature review in 16 top marketing journals on AI. Based on this review, an AI framework for understanding value co-creation in interactive buyer–seller marketing relationships was conceptualized.

The literature review led to a number of key research findings and summary areas: (1) an historical perspective, (2) definitions and boundaries of AI, (3) AI and interactive marketing, (4) relevant theories in the domain of interactive marketing and (5) synthesizing AI research based on antecedents to AI usage, interactive AI usage contexts and AI-enabled value co-creation outcomes.

This is one of the most extensive reviews of AI literature in marketing, including an evaluation of in excess or 300 conceptual and empirical research. Based on the findings, the authors offer a future research agenda, including a visual titled “What is AI in Interactive Marketing? AI design factors, AI core elements & interactive marketing AI usage contexts.”

While there has been extensive research on understanding the effects of online reviews on product sales, there is not enough investigation of the inter-relationships between online reviews, online search and product sales. The study attempts to address this gap in the context of the Indian car market.

The research uses text mining and considers six important review features volume, valence, length, deviation of valence, sentiment and readability within the heuristic and systematic model of information processing. Panel data regression is used along with mediation analysis to study the inter-relationships between features of reviews, online search and sales.

The study finds that numerical heuristic features significantly affect sales and online search, numerical systematic feature affects sales and the textual heuristic and systematic features do not affect sales or online search in the Indian car market. Further, online search mediates the association between features of reviews and sales of cars.

Although only car sales data from India is considered in this research, similar relationships between review features, online search and sales could exist for the car market of other countries as well.

This research uncovers the unique role of online search as a mediator between review features and sales, whereas prior literature has considered review features and online search as independent variables that affect sales.

To empirically study the direct and mediating relationships between blockchain technology adoption strategy (BCTAS), electronic supply chain management diffusion (eSCMD), entrepreneurial orientation (EO) and human resources information system (HRIS) in Egyptian banks. This paper aims to connect the dots and show the relationships linking these related constructs after the emergence of this breakthrough blockchain technology.

The authors have undertaken a thematic review of relevant multidisciplinary business management literature and then developed a conceptual model. This model was examined through adopting a mixed-methods approach, through which 300 quantitative questionnaires were filled by information technology (IT) staff at 12 banks in Egypt utilising a snowball sample. Besides, 20 qualitative interviews were carried out with international and Egyptian blockchain experts for exploratory and explanatory purposes. The suggested hypotheses were tested using structural equation modelling.

The results revealed that EO affects positively and significantly BCTAS and HRIS. BCTAS affects positively and significantly both HRIS and eSCMD. Concerning the linkage between external/supply chain and internal/organisational information diffusion, HRIS has a positive and significant effect on eSCMD. The direct EO–eSCMD relationship is not supported. Yet, indirectly, BCTAS mediates significantly EO–eSCMD and EO–HRIS relationships. Further, HRIS mediates significantly the indirect BCTAS–eSCMD relationship.

The findings of this research shed light on the benefits and challenges of adopting BCTAS within emerging markets in general and Egyptian banking in specific, which can support an effective and efficient decision-making process undertaken by strategic and functional banking managers with EO in similar emerging economies.

Conceptually and empirically, it is the first article that investigated direct and mediating EO–BCTAS–HRIS–eSCMD relationships in a promising banking industry of an emerging market. It solved an interdisciplinary research puzzle by piecing together the relevant contemporary literature on production, operations and SC management, entrepreneurship, HR management and strategic technology adoption.