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Purpose: Nowadays, many terms like computer vision, deep learning, and machine learning have all been made possible by recent artificial intelligence (AI) advances. As new types of employment have risen significantly, there has been significant growth in adopting AI technology in enterprises. Despite the anticipated benefits of AI adoption, many businesses are still struggling to make progress. This research article focuses on the influence of elements affecting the acceptance procedure of AI in organisations.

Design/Methodology/Approach: To achieve this objective, propose a hierarchical paradigm for the same by developing an Interpretive Structural Modelling (ISM). This paper reveals the barriers obstructing AI adoption in organisations and reflects the contextual association and interaction amongst those barriers by emerging a categorised model using the ISM approach. In the next step, cross-impact matrix multiplication is applied for classification analysis to find dependent, independent and linkages.

Findings: As India is now focusing on the implementation of AI adoption, therefore, it is essential to identify these barriers to AI to conceptualise it systematically. These findings can play a significant role in identifying essential points that affect AI adoption in organisations. Results show that low regulations are the most critical factor and functional as the root cause and further lack of IT infrastructure is the barrier. These two factors require the most attention by the government of India to improve AI adoption.

Implications: This study may be utilised by organisations, academic institutions, Universities, and research scholars to fill the academic gap and faster implementation of AI.

The article discusses the current relevance of artificial intelligence (AI) in research and how AI improves various research methods. This article focuses on the practical case study of systematic literature reviews (SLRs) to provide a guideline for employing AI in the process.

Researchers no longer require technical skills to use AI in their research. The recent discussion about using Chat Generative Pre-trained Transformer (GPT), a chatbot by OpenAI, has reached the academic world and fueled heated debates about the future of academic research. Nevertheless, as the saying goes, AI will not replace our job; a human being using AI will. This editorial aims to provide an overview of the current state of using AI in research, highlighting recent trends and developments in the field.

The main result is guidelines for the use of AI in the scientific research process. The guidelines were developed for the literature review case but the authors believe the instructions provided can be adjusted to many fields of research, including but not limited to quantitative research, data qualification, research on unstructured data, qualitative data and even on many support functions and repetitive tasks.

AI already has the potential to make researchers’ work faster, more reliable and more convenient. The authors highlight the advantages and limitations of AI in the current time, which should be present in any research utilizing AI. Advantages include objectivity and repeatability in research processes that currently are subject to human error. The most substantial disadvantages lie in the architecture of current general-purpose models, which understanding is essential for using them in research. The authors will describe the most critical shortcomings without going into technical detail and suggest how to work with the shortcomings daily.

Digitalization and marketing technologies have made it possible to overcome some barriers to pricing – a multidisciplinary field between marketing, finance and IT – and have set the stage for a paradigm shift in the pricing profession. Value creation, the pricing process, and price communication have been transformed by innovative business models and advanced algorithmic and human–machine solutions. This chapter synthesizes the literature to date and provides a comprehensive framework for an all-encompassing 360° pricing approach that broadens the understanding of pricing in the context of digital business across all steps of the price management process. Starting from product attributes and motivational beliefs in consumers' value assessment and adoption of (technological or digital) products or services, new business models and pricing models emerge in the digital economy, human–machine solutions for price implementation and repricing are increasingly applied, and price search and communication take place through a variety of digital communication channels. Each stage of this framework discusses concrete examples, highlighting the freemium strategy, the subscription model, price tracking and repricing tools, and digital price information channels such as e-commerce, marketplace, or price comparison platforms. The implications for price management in a digital, technology-driven landscape are discussed from the executive level to the analyst level.

While compassionate and morally motivated people are theorised to be more likely to engage in activities that contribute to the social good, the literature provides contradictory evidence regarding the role of empathy and moral obligation in building social entrepreneurial intention (SEI). This study aims to clarify how empathy and moral obligation influence SEI.

The authors used survey data (n = 307) from Bangladesh, a frontier economy, to test the hypothesised relationships between empathy, moral obligation and SEI by applying partial least square–structural equation modelling in Smart PLS 3. They then conducted a second study with a larger sample (n = 339) from Saudi Arabia, an emerging economy, to further investigate how the findings withstand in a different socio-economic context.

The findings contradicted extant conceptualisations and revealed that empathy and moral obligation influence SEI indirectly through other individual and contextual factors, such as social entrepreneurial self-efficacy and perceived social support. The findings indicate that a person with a feeling of compassion and moral responsibility to help others will not start a social venture unless they feel capable and supported to start and run the venture.

The study contributes to a contentious area of research in SEI by demonstrating the links between various individual-level (empathy, moral obligation and social entrepreneurial self-efficacy) and contextual-level (perceived social support) variables and their relationship with SEI.

The rapid growth in volume and value of on-demand restaurant food delivery, accelerated by the COVID-19 pandemic, is causing a paradigm shift in the food service sector. However, there is a lack of hospitality management research into this emerging phenomenon. To address this gap, this paper defines and develops a novel conceptual model and typology and proposes a research agenda for ghost production in the context of food service.

This paper uses the Servuction model to explore, define and model the radical separation between food service production sites, points of sale and consumer interaction from the perspective of on-demand restaurant food delivery. A novel typology is developed and illustrated with eight industry examples from the UK and an accompanying cost benefit analysis. Future research priorities are identified.

In the hospitality literature, little attention has been paid to changes on-demand restaurant food delivery brings to production and business models of food service organisations, resulting in significant gaps between food service practice and theory. The knock-on effects to stakeholders include increased convenience for customers, uncertain employment status of riders and, for restaurants, striking a balance between capturing new markets and losing control of the customer. Additionally, for aggregators, there is a lack of profitability in existing models, despite holding the balance of power (and data).

The concept of “ghost production” and its associated typology is novel and offers a contribution to hospitality management literature by defining the term, scope and scale of this new phenomenon. Practical implications are proposed.

This study is carried out to evaluate how well legal knowledge can be demonstrated by a built environment professional via the scenario-based approach to the learning of law modules.

A Delphi analysis of the advice provided by an MSC quantity surveying student, on a scenario-based legal problem arising due to a property contract, is carried out. The tendered legal advice was submitted as part of the assessment requirements for an MSc law module, following the university criteria, after the teaching of a law module. The student report, which attained an A-grade, and the assessment criteria used for marking/grading by the university were subsequently sent to 18 practicing lawyers, who were selected to constitute an expert panel, to independently judge the extent of legal knowledge demonstrated in the student report.

The Delphi analysis outcome showed that the expert panel holds a similar consensus view to the university on the level of legal knowledge demonstrated and by extension the effectiveness of the law module in imparting legal knowledge to a non-lawyer. The study outcome shows how well legal knowledge can be acquired and applied by a non-lawyer, within the context of the built environment, via scenario-based teaching of a law module.

This study serves as a preliminary step necessary to arouse further research toward empirically profiling the current outlook of a wider range of graduating students receiving scenario-based legal education in the built environment.

The purpose of this paper is to investigate, through a quantitative research, the impact of the COVID-19 pandemic on online food delivery in Italy and Poland, as well as to ascertain whether or not, under a consumer perspective, respondents consider such method as impacting on the environment and on food waste.

An empirical survey of consumers at Italian and Polish Universities was conducted using the online survey computer-assisted web interview (CAWI) method.

Results reveal that the COVID-19 pandemic has modified consumer habits in both Italy and Poland, though the researched sample does not have full knowledge of these changes. Moreover, the study has proved once again that online food delivery may impact negatively on the environment and the food waste issue.

This paper fills a gap in literature since, by the authors' knowledge, it is the first quantitative study analysing Italian and Polish young consumers as to online food delivery habits and their approach to sustainability aspects of this purchasing method.

The purpose of this paper is to investigate the applicability of cloud computing (CC) and the challenges that contribute to more successful projects with a major sustainable construction development.

The previous studies provided information on CC implementation barriers, which were then evaluated by 104 construction stakeholders through a questionnaire survey. As a result, the exploratory factor analysis (EFA) approach was used to investigate these barriers. Furthermore, a partial least square structural equation model was used to build a model of these barriers (PLS-SEM).

The EFA results revealed that the above-noted factors are in a close relation with three key components, i.e. social, economic and communication. In addition, the proposed model results found the social barrier a key challenge to the implementation of CC.

The results from this study can help decision-makers to improve the approaches regarding data fragmentation that has great effects on the execution of all construction projects. The focus of the paper is to enhance the data fragmentation processes. In addition, the results would be useful to strengthen the sustainability of existing construction projects by enhancing the implementation of CC.

The novelty of this research work will provide a solid foundation for critically assessing and appreciating the different barriers affecting the adoption of CC.

This paper aims to contribute to the field of social entrepreneurship by exploring the interrelationships among the antecedents of social entrepreneurial intention (SEI) through the lens of Mair Noboa model (MNM). In recent years, many researchers have applied the antecedents of MNM to determine SEI. However, interrelationship among these antecedents has not been a focus of enquiry despite the repeated scholarly calls.

Applying quantitative methodology, the data was collected from a Web-based survey distributed across Bangladesh (N = 412). Data analysis was carried out based on the covariance-based structural equation modelling technique to confirm the hypotheses. The final measurement and structural models met all the requirements for reliability, model fit, convergent validity and discriminant validity. The proposed hypotheses were tested based on direct relationships and mediating effects.

The findings suggested that interrelationships among these antecedents do increase individuals’ intentions to become social entrepreneurs.

This paper fills an important knowledge gap by exploring the interrelationships among moral obligation, empathy, perceived social support and social entrepreneurial self-efficacy. This paper stressed identifying whether the SEI enhances through the interrelationships among these antecedents or not. The study findings bring new theoretical and practical implications on the role of empathy, moral obligation, perceived social support and social entrepreneurial self-efficacy.

COVID-19 has affected the economies adversely from all sides. The sudden halt in production has impacted both the supply and demand sides. It calls for analysis to quantify the impact of the reduction in economic activity on the economy-wide variables so that appropriate steps can be taken. This study aims to evaluate the sensitivity of various sectors of the Indian economy to this dual shock.

The eight-sector open economy general equilibrium Global Trade Analysis Project (GTAP) model has been simulated to evaluate the sector-specific effects of a fall in economic activity due to COVID-19. This model uses an economy-wide accounting framework to quantify the impact of a shock on the given equilibrium economy and report the post-simulation new equilibrium values.

The empirical results state that welfare for the Indian economy falls to the tune of 7.70% due to output shock. Because of demand–supply linkages, it also impacts the inter- and intra-industry flows, demand for factors of production and imports. There is a momentous fall in the demand for factor endowments from all sectors. Among those, the trade-hotel-transport and manufacturing sectors are in the first two positions from the top. The study recommends an immediate revival of the manufacturing and trade-hotel-transport sectors to get the Indian economy back on track.

The present study has modified the existing GTAP model accounting framework through unemployment and output closures to account for the impact of change in sectoral output due to COVID-19 on the level of employment and other macroeconomic variables.