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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.

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.

Road freight transportation companies need to take advantage of information and communication technologies to develop capabilities. This study proposes a framework to guide road freight transportation companies to achieve data visibility in their operations by developing such capabilities. By proposing this framework, this research contributes to literature and practice, highlighting the capabilities and the respective supporting technologies for improved data visibility in road freight transportation.

A mixed-method approach is used to develop the framework, considering three methodological steps. In phase 1, the capabilities are identified in the literature and validated by experts. In phase 2, an empirical assessment of cause–effect relationships between capabilities is performed using a multiple case study and DEMATEL. Lastly, in phase 3, an analysis of the cause model and significant associations is conducted to enable the development of the framework. In addition, the proposed framework was validated by the experts interviewed.

The results provide a framework that explains the link between the technology-enabled data visibility capabilities in road freight transportation operations. In addition, a pathway was established that road freight transportation companies could follow to achieve data visibility in their operations by developing such capabilities.

This work develops the first framework that provides a path for data visibility in road freight transportation operations from adopting certain technologies. The insights are compelling for researchers and practitioners to optimize the decision-making process for adopting technologies and developing capabilities related to data visibility.

To examine the role of supply chain integration (SCI – i.e. supplier integration, customer integration and internal integration) between integrated information technology (IIT) and financial performance (FP).

An explanatory sequential mixed-methods study was conducted, collecting quantitative data first and then examining the quantitative results with in-depth qualitative data from a sample targeting manufacturing and services firms in Egypt.

This study indicates that IIT relates positively to SCI, which in turn relates positively to FP. Main enablers/mechanisms and disablers/barriers for those linkages are identified.

After establishing the linkages between ITT, SCI and FP in the quantitative phase of research, a qualitative phase based on follow-up interviews provide deeper understanding about mechanisms and contexts behind those linkages.

Offer firms guidance to assess IIT, SCI and FP.

With a disaggregated framework of IIT, SCI and FP, this study contributes by answering a need for development of a methodological toolbox of the field in supply chain management, with a rigorous use of mixed method research, as a way of departing from the normal approach to help researchers in providing a deeper and richer understanding of supply chain problems.

This paper aims to conduct a tertiary review to analyse the state of the art of literature reviews on knowledge management (KM) published in academic journals and provide an overview of their evolution. From 2000 to 2022, about 500 reviews have been published in the KM field, with most systematic studies compared to bibliometric or meta-analytic studies, and an absence of previous tertiary studies. Therefore, given the lack of previous tertiary research, this paper provides a complete picture of the evolution of review topics in the past and presents implications for both researchers and practitioners.

A classification scheme was defined to cluster and evaluate the literature reviews, both in terms of methodological approach and content. Regarding the content, the various secondary papers were classified according to the purpose of the research (state of the art, taxonomy, research agenda and research framework), the unit of analysis (small and medium enterprise, large company, start-up and university), the KM models adopted and the thematic areas addressed. Furthermore, a tertiary review methodology was identified integrating two main approaches: a bibliometric approach for cluster identification and a systematic approach for the discussion.

Two categories of contributions emerge from the results: those concerning research topics that have found a continuous interest over time and those that have not yet found a constant research interest. This latter aspect is relevant to help researchers conduct future literature analysis in KM research to bridge existing research gaps.

This paper provides a unique compendium of search directions to offer a comprehensive overview of the scientific debate about KM. This overview can also be used as a managerial panacea to identify best KM practice guidelines from existing reviews.

This is a unique attempt to conduct a tertiary study on KM for more than two decades by providing insights into the structural body of knowledge through academic progress in the subject of KM. Thus, this study expands the field of KM and provides original approaches for research in the field.

Brand reputation (BR) is one of the most important factors that affect the consumer–brand relationship and give businesses a competitive advantage. Businesses with a strong BR can increase their market shares and product market prices, in addition to gaining a competitive advantage. In order for businesses to have these advantages, they need to know and analyze their consumers. This study aimed to develop an alternative analysis method by using classification algorithms and regression analysis to measure and evaluate the effect of consumers' BR perceptions on their willingness to pay premium prices (WPP).

The research data were collected from 483 participants by the online survey method due to the COVID-19 pandemic. The data were first analyzed with regression analysis, and the effect of BR on WPP was found to be significant. Then, using artificial intelligence (AI) methods that were not used in previous studies, consumers' perceptions of BR and WPP were clustered and classified.

The results revealed the highest and lowest customer groups with BR and WPP and empirically demonstrated that highly accurate practical classification models can be applied to determine strategies in line with these findings.

The model proposed in this study offers an integrated approach by using AI and regression analysis together and tries to fill the gap in the literature in this field. Therefore, the novelty of this study is to quantitatively reveal and evaluate the relationship between BR and WPP by using AI classification algorithms and regression analysis together.

This chapter delves into the impact of digital initiatives on firms and sheds light on how they can be explained through market reactions and the resource/capabilities mechanism. By providing a novel conceptual framework that reflects the potential impact of digital initiatives on the sensing, seizing and transforming capabilities of dynamic capabilities, this chapter reveals the tremendous potential of digital initiatives to help firms become more adaptive to their environment and create sustainable competitive advantages that elicit positive market responses. This conceptual framework represents an original contribution to the literature. It enhances the understanding of the resource-based view and efficient market hypothesis, providing a fresh perspective on the influence of digital initiatives on firm performance and the dynamic capabilities mechanism that has hitherto been overlooked. As a result, this chapter enables researchers to develop testable hypotheses that examine the causal relationships between digital initiatives, dynamic capabilities and market performance using robust quantitative research methods. Furthermore, this chapter offers valuable insights for managers seeking to develop a more focused approach to digital transformation and enhance their competitive advantage. By exploring the impact of digital initiatives on sensing, seizing and transforming capabilities, managers can gain a deeper understanding of how they can leverage digital initiatives to improve their organisational performance and respond more effectively to the demands of an ever-changing landscape.