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The pressing issues of climate change and environmental degradation call for a reevaluation of how we approach economic activities. Both leaders and corporations are now shifting their focus, toward adopting practices and embracing the concept of circular economy (CE). Within this context, the Food and Beverage (F&B) sector, which significantly contributes to greenhouse gas (GHG) emissions, holds the potential for undergoing transformations. This study aims to explore the role that Artificial Intelligence (AI) can play in facilitating the adoption of CE principles, within the F&B sector.

This research employs the Best Worst Method, a technique in multi-criteria decision-making. It focuses on identifying and ranking the challenges in implementing AI-driven CE in the F&B sector, with expert insights enhancing the ranking’s credibility and precision.

The study reveals and prioritizes barriers to AI-supported CE in the F&B sector and offers actionable insights. It also outlines strategies to overcome these barriers, providing a targeted roadmap for businesses seeking sustainable practices.

This research is socially significant as it supports the F&B industry’s shift to sustainable practices. It identifies key barriers and solutions, contributing to global climate change mitigation and sustainable development.

The research addresses a gap in literature at the intersection of AI and CE in the F&B sector. It introduces a system to rank challenges and strategies, offering distinct insights for academia and industry stakeholders.

The rise of artificial intelligence (AI) and differing attitudes towards its adoption in the building and environment (B&E) industry has an impact upon whether companies can meet changing demand and remain relevant and competitive. The emergence of Industry 4.0 technologies, coupled with the repercussions of COVID-19, increases the urgency and opportunities offered that companies must react to, as disruptive technologies impact how project management (PM) professionals work and necessitate acquisition of new skills. This paper attempts to identify the drivers of and barriers to, as well as the general perception and receptiveness of local PM professionals towards, AI adoption in PM and thereby propose potential strategies and recommendations to drive AI adoption in PM.

This study employs both quantitative and qualitative approaches to examine the findings gathered. A survey questionnaire was used as the primary method of gathering quantitative data from 60 local PM professionals. Statistical tests were performed to analyse the data. To substantiate and validate the findings, in-depth interviews with several experienced industry professionals were performed.

It is found that top drivers include support from top management and leadership, organisational readiness and the need for greater work productivity and efficiency. Top barriers were found to be the high cost of AI implementation and maintenance and the lack of top-down support and skilled employees trained in AI. These findings could be attributed to the present state of AI technologies being new and considerably underutilised in the industry. Hence, substantial top-down support with the right availability of resources and readiness, both in terms of cost and skilled employees, is paramount to kick-start AI implementation in PM.

Little research has been done on the use of AI in PM locally. AI's potential to improve the productivity and efficiency of PM processes in the B&E industry cannot be overlooked. An understanding of the drivers of, barriers to and attitudes towards AI adoption can facilitate more intentional and directed oversight of AI's strategic roll-out at both the governmental and corporate levels and thus mitigate potential challenges that may hinder the implementation process in the future.

While artificial intelligence (AI) has shown its promise in assisting human decision, there exist barriers to adopting AI for decision-making. This study aims to identify barriers in the adoption of AI for automated organisational decision-making. AI plays a key role, not only by automating routine tasks but also by moving into the realm of automating decisions traditionally made by knowledge or skilled workers. The study, therefore, selected respondents who experienced the adoption of AI for decision-making.

The study applied an interpretive paradigm and conducted exploratory research through qualitative interviews with 13 senior managers in South Africa from organisations involved in AI adoption to identify potential barriers to using AI in automated decision-making processes. A thematic analysis was conducted, and AI coding of transcripts was conducted and compared to the manual thematic coding of transcripts with insights into computer vs human-generated coding. A conceptual framework was created based on the findings.

Barriers to AI adoption in decision-making include human social dynamics, restrictive regulations, creative work environments, lack of trust and transparency, dynamic business environments, loss of power and control, as well as ethical considerations.

The study uniquely applied the adaptive structuration theory (AST) model to AI decision-making adoption, illustrated the dimensions relevant to AI implementations and made recommendations to overcome barriers to AI adoption. The AST offered a deeper understanding of the dynamic interaction between technological and social dimensions.

This study aims to identify, review and synthesize existing literature on key theories, drivers and barriers affecting consumer adoption or resistance to artificial intelligence (AI) in the hospitality sector.

This study aims to conduct a complete literature review of the accrued knowledge generated so far on AI in the hospitality sector. To attain the overall objectives of this study, we used the systematic literature review (SLR) method. This method systematically handles the diversity of knowledge in a specific topic to answer precise research questions. It also generates new visions through a synthesis of the literature, to identify the knowledge gaps, set the new directions for the future researcher and provide sufficient guidance to inform the policy and practice.

The findings of this study are presented in three sections, as follows: descriptive analysis, content analysis and synthesized framework. The findings highlighted the state-of-the-art mapping of the existing research in terms of publication frequency over time and across publication outlets, key theories, methods and geographies. In addition, literature on consumer adoption (or resistance) of AI in hospitality is content analyzed to highlight key drivers and barriers. Moreover, this review critically evaluates extant literature and sets future agendas by postulating specific research questions for further knowledge development in this field of study.

The SLR focused on consumer adoption or resistance to use AI in hospitality literature. The future researcher may include additional streams to get better results.

The study findings will help multiple stakeholders to understand the underlying causes of customer resistance or barriers to the intention to use/adopt AI services in the hotel sector. Furthermore, study results will allow them to better analyze the relationship between customer barriers, intents or consumer decision behaviors.

First, this study provides a comprehensive synthesis of the literature on the consumer adoption or resistance of AI in hospitality. This study categorizes the existing diversified literature in two main themes – drivers and barriers – to present a simplistic picture of the existing literature. Second, the review highlights the gaps and limitations in existing research and provides guidance for future scholars. Third, the key contribution of this review is the development of a unified framework on the consumer adoption or resistance of AI in the hospitality sector. That is, this study puts forward the behavioral reasoning theory framework and suggests that future research using this lens will immensely contribute to existing literature. Finally, this study facilitates the practitioners to understand the key motivating and hindering factors affecting the adoption and resistance behavior.

During the last three decades, a number of autonomous and, in many cases, heterogeneous systems have been evolved in organisations which cause integration problems and increase the complexity and cost of maintaining these applications. Enterprise Resource Planning (ERP) systems were then introduced to overcome integration problems. However, organisations did not abandon their existing systems when adopting an ERP solution, as ERP systems focus on general processes and do not allow much customisation. As a result, ERP systems co‐exist alongside other systems, and therefore amplify the need for integration. Recently, a new generation of software solutions called Application Integration (AI) has been introduced to address integration issues. AI is a new area with limited literature and documentation and explains the integration of basic types of applications and summarises the benefits of and the barriers to the adoption of an AI solution. Uses benchmarking to search and study best practices in the integration area. Explains how AI can be used by organisations to help them increase their productivity and improve their business processes. In addition, proposes a taxonomy of AI benefits and barriers when mapped against custom, packaged and e‐business solutions. The proposed taxonomy will help researchers to better understand, analyse and compare the benefits and barriers of AI and will therefore improve decision making.

With the development of information technology (IT), governments around the globe are using state-of-the-art IT interfaces to implement the so-called 3E’s in public service delivery, that is, economy, efficiency and effectiveness. Two of these IT interfaces relate to Artificial Intelligence (AI) and Internet of Things (IoT). While AI focuses on providing a “human” garb for computing devices, thereby making them “intelligent” devices, IoT relies on interfaces between sensors and the environment to make “intelligent” decisions. Recently, the convergence of AI and IoT – also referred to as Artificial Intelligence of Things (AIoT) – is seen as a real opportunity to refurbish the public service delivery formats. However, there is limited understanding as to how AIoT could contribute to the improvisation of public service delivery. This study aims to create a modular framework for AIoT in addition to highlighting the drivers and barriers for its integration in the public sector.

This descriptive-explanatory study takes a qualitative approach. It entails a thorough examination of the drivers and barriers of integrating AI and IoT in the public sector. A review of literature has led to the development of a conceptual framework outlining the various factors that contribute to creating public value.

Value creation occurs when AI and IoT coalesce in the public service delivery mechanisms.

AIoT is a cutting-edge technology revolutionizing health care, agriculture, infrastructure and all other industrial domains. This study adds to the growing body of knowledge on the public sector's use of AI and IoT. Understanding these disruptive technologies is critical to formulating policies and regulations that can maximize the potential benefits for the public-sector organizations.

This chapter aims to highlight the existing applications and future prospects of Artificial Intelligence (AI) in the tourist business. In addition, this chapter investigates the obstacles in using AI in the Indian tourist industry.

To achieve the study's aims, both primary and secondary data are used. Using secondary sources, desk research was conducted to investigate the existing uses and future prospects of AI application in the global tourism industry. In addition, qualitative interviews with 25 executives in the Indian tourist business were undertaken to study the obstacles to using AI in the Indian tourism industry.

The research found that the applications of AI in the worldwide tourist business are extensive. Nonetheless, corporations are actively using AI-based technology to improve the customer experience via chatbots, intelligent forecasting and smart, tailored travel experiences. The qualitative interviews found that the implementation of AI technology in the Indian tourist industry is hindered by budgetary restrictions, knowledge constraints and barriers relating to human resources.

The use of AI in the tourism business may significantly improve the client experience. As a consequence, the use of AI-based chatbots and intelligent travel aides is growing exponentially. The research examined the many uses of AI in the worldwide tourist industry as well as the obstacles associated with the deployment of AI in the Indian tourism industry.

Industry 4.0 technologies have the potential to improve the quality management performance of industrial companies. The paper analyses the influence of Industry 4.0 technologies on quality management aspects, but also the barriers that slow down the deployment of each Industry 4.0 technology and limit each impact.

The impact of Industry 4.0 technologies on quality management aspects (QMAs) is a heterogeneous and multidimensional phenomenon dependent on the current context, a holistic multiple case study has been applied. Twenty-six case studies were carried out on eight Industry 4.0 technologies, with a minimum of two cases per technology. These cases were selected from the 168 projects presented in the four editions of the BIND 4.0 program, winner of the 14th edition of the European Enterprise Promotion Awards. The cases were selected based on a preliminary survey of 124 project managers. Subsequently, individual case and cross-case analyses for each technology were carried out. Finally, these results were confirmed by interviews with a minimum of two customers per Industry 4.0.

Results show that the adoption of Industry 4.0 technologies positively affects QMAs. Specifically, the influences received by “process control” and “customer satisfaction” from all the Industry 4.0 technologies studied are medium to high. In addition, barriers from the “economic and legal” and “workers” categories exert greater influence than the barriers pertaining to “organization”, “lack of training and information” and “technology”.

The main limitation is the generalizability of the findings of qualitative studies (ergo the case study). In this sense, statistical generalizability, characteristic of a random sample, is not intended in this paper. Therefore, the use of multiple case studies has been chosen to reinforce analytical generalizations with corroborated evidence (literal replication).

Managers interested in adopting Industry 4.0 technologies Ts should plan the implementation process to minimize the impact of these barriers and optimize the results for each stakeholder. In this sense, the barriers that concern the workers should be managed. It is the responsibility of managers to inform and explain how data will be handled, and how privacy concerns will be addressed.

It is essential to explain and convince workers about the need for a renewal of tasks. New types of jobs (i.e. the use of robots) will involve training for workers to enable their integration alongside the new technologies.

This paper addresses two under-researched areas that are essential when defining strategies in the industrial business context. Firstly, the paper analyses the influence of each I40 T on each QMA. Secondly, it analyses the barriers to adopt that slow down the rollout of each I40 T and limits each impact.

In the context of libraries adopting artificial intelligence (AI) technology, this paper aims to demonstrate the synergy between two different yet complimentary frameworks, need-based experimentation (NBE) and curiosity-based experimentation (CBE). It looks at how these frameworks interact and operate together to promote technological innovation and innovation in libraries.

The authors’ extensive professional experience in the AI adoption and innovation of libraries is drew upon in this paper. The methodology encompasses empirical observations of various libraries engaging in digital innovations through experimentations with AI technology adoption practices. Using the frameworks of NBE and CB), these observations are examined to find patterns, relationships and mutual reinforcement between the two methods. The analysis of this study is built on the authors’ observations and real-world case studies.

The research reveals that NBE and CBE work together to provide libraries with all-encompassing adoption methods for AI technology. This study indicates a dynamic interaction between NBE and CBE that boosts libraries’ methods for adopting AI technology. NBE acts as a catalyst for CBE by raising awareness of specific library needs, prompting librarians to explore AI technologies aligned with those needs. This synergy empowers librarians to creatively experiment with technology solutions that directly address pressing library challenges. Conversely, CBE fuels NBE by promoting group learning among diverse team members and fostering individual motivation to tackle library needs collaboratively. As they explore AI technology out of personal curiosity, librarians make important contributions that enhance NBE.

The novel aspect of this study is the recognition of the complementarity between NBE and CBE frameworks, which suggests that libraries should view them as intertwined rather than two separate approaches. Focusing on both methodologies increases the culture of experimentation and improves the problem-solving abilities of librarians. Innovation is fueled by controlled experimentation and innate curiosity in an atmosphere that is fostered by the mutual influence of NBE and CBE. This synthesis offers libraries a comprehensive strategy for adopting AI technology, empowering them to manage the shifting environment and realize the revolutionary promise of AI technologies.

This paper aims to discuss the effects of COVID-19 on hotel marketing and management practices and outlines a three-pronged research agenda to stimulate knowledge development in the hotel sector.

This paper is based on an overview of the relevant literature on hotel marketing and management and the hotel guest behavior. The authors also investigated hospitality service trends to propose a research agenda.

This paper presents a research agenda from three dimensions – artificial intelligence (AI) and robotics, hygiene and cleanliness and health and health care. First, different types of AI (mechanical, thinking and feeling) might open up distinct research streams at the intersection of health crises and hotel management, in light of the COVID-19 pandemic. Additionally, this paper recommends that researchers move beyond typical perspectives on the antecedents and outcomes of hotel hygiene and cleanliness to delve into guests’ perceptions of the cleanliness of specific hotel surfaces. Furthermore, a more in-depth analysis is warranted about the evolving relationship between hotels and the health-care sector.

The recommended research areas are intended to advance the knowledge base to help hotels recover from the COVID-19 pandemic. The suggested research streams are expected to provide actionable insights to promote the development and sustainability of the hotel sector.

This paper appears to be a frontier study, critically examining possible effects of the COVID-19 pandemic on hotel marketing and management practices and how hoteliers may respond to such challenges to recover after this pandemic.