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This article reviews the quantitative marketing literature on artificial intelligence (AI) through an economics lens. We apply the framework in Prediction Machines: The Simple Economics of Artificial Intelligence to systematically categorize 96 research papers on AI in marketing academia into five levels of impact, which are prediction, decision, tool, strategy, and society. For each paper, we further identify each individual component of a task, the research question, the AI model used, and the broad decision type. Overall, we find there are fewer marketing papers focusing on strategy and society, and accordingly, we discuss future research opportunities in those areas.

The purpose of this article is to analyze the existing studies on the application of artificial intelligence (AI) in lean construction management (LCM). Further, this study offers a classification scheme that specifies different categories of AI tools, as applied to the field of LCM to support various principles of LCM.

This research adopts the systematic literature review (SLR) process, which consists of five consecutive steps: planning, searching, screening, extraction and synthesis and reporting. As a supplement to SLR, a bibliometric analysis is performed to examine the quantity and citation impact of the reviewed papers.

In this paper, seven key areas related to the principles of LCM for which AI tools have been used are identified. The findings of this research clarify how AI can assist in bolstering the practice of LCM. Further, this article presents directions for the future evolution of AI tools in LCM based on the current emerging trends.

This paper advances the LCM systems by offering a lens through which construction managers can better understand key concepts in the linkage of AI to LCM.

This research offers a new classification scheme that allows researchers to properly recall, identify and group various applications of AI categories in the construction industry based on various principles of LCM. In addition, this study provides a source of references for researchers in the LCM discipline, which advances knowledge and facilitates theory development in the field.

In the contemporary period, numerous businesses undergo significant adjustments, such as evaluating critical components of the corporate operations and relying on technology to keep operations running while conforming to an ever-changing set of norms and new tactics. The present study aims to (1) explore the relationship between Industry 4.0 (I4.0) tools and their impact on organizational performance and (2) find evidence supporting the moderating role of remote working and organizational agility (OA) in enhancing organizational performance.

The study employed the quantitative research method, and the data were collected from individuals working in different Asian IT firms using the previously established questionnaire. The data were examined using SPSS v22. Different statistical tests have been performed to find the relationship among constructs.

This study uncovers that I4.0 tools impact organizational performance, especially in the IT sector, with a particular emphasis on the moderating influence of remote work and OA. I4.0 tools encompass pivotal components such as artificial intelligence (AI), big data (BD), cloud computing (CC) and Internet of Things (IoT) indeed augment organizational performance. It can be referenced that I4.0 tools play the role of a driving force that equips organizations with the knowledge to augment their performance.

Companies should encourage remote work and use I4.0 technology to support and manage it. Enabling people to work from any location, lowering the requirement for physical infrastructure and enabling a more flexible and responsive organizational structure can increase OA. In conclusion, firms in Asia may increase the performance and agility using I4.0 technology. Organizations may innovate by putting money into these technologies, encouraging remote work and creating an innovative culture.

In this dynamic and technologically advanced environment, every industry is forced to look for latest tools, i.e. I4.0, tools to augment the performance. It has been concluded that I4.0 tools are “better practices” for boosting organizational performance; hence, the findings benefit firms working in the IT sector. The verdicts of this research can assist organizations in making decisions regarding the implementation of I4.0 tools.

To the best of the authors' knowledge, no specific study could be found in which the relationship among these constructs had been investigated earlier in the IT sector. This research work acts as value addition to the literature as it illustrates technological advancements may increase organizational performance, especially in Asia. This research work adds to the body of knowledge by amplifying the effect of latest technologies on organizational performance, via remote work and OA.

The purpose of this paper is to share findings from empirically driven conceptual research into the implications for English teachers of understanding generative AI as a “tool” for writing.

The paper reports early findings from an Australian National Survey of English teachers and interrogates the notion of the AI writer as “tool” through intersectional feminist discursive-material analysis of the metaphorical entailments of the term.

Through this work, the authors have developed the concept of “coloniser tool-thinking” and juxtaposed it with First Nations and feminist understandings of “tools” and “objects” to demonstrate risks to the pursuit of social and planetary justice through understanding generative AI as a tool for English teachers and students.

Bringing together white and First Nations English researchers in dialogue, the paper contributes a unique perspective to challenge widespread and common-sense use of “tool” for generative AI services.

The aim of this article is to map the use of AI in the user experience (UX) design process. Disrupting the UX process by introducing novel digital tools such as artificial intelligence (AI) has the potential to improve efficiency and accuracy, while creating more innovative and creative solutions. Thus, understanding how AI can be leveraged for UX has important research and practical implications.

This article builds on a systematic literature review approach and aims to understand how AI is used in UX design today, as well as uncover some prominent themes for future research. Through a process of selection and filtering, 46 research articles are analysed, with findings synthesized based on a user-centred design and development process.

The authors’ analysis shows how AI is leveraged in the UX design process at different key areas. Namely, these include understanding the context of use, uncovering user requirements, aiding solution design, and evaluating design, and for assisting development of solutions. The authors also highlight the ways in which AI is changing the UX design process through illustrative examples.

While there is increased interest in the use of AI in organizations, there is still limited work on how AI can be introduced into processes that depend heavily on human creativity and input. Thus, the authors show the ways in which AI can enhance such activities and assume tasks that have been typically performed by humans.

Recent United Nations Climate Change Conferences recognise extreme climate change of heatwaves, floods and droughts as threatening risks to the resilience and success of public–private partnership (PPP) infrastructure projects. Such conferences together with available project reports and empirical studies recommend project managers and practitioners to adopt smart technologies and develop robust measures to tackle climate risk exposure. Comparatively, artificial intelligence (AI) risk management tools are better to mitigate climate risk, but it has been inadequately explored in the PPP sector. Thus, this study aims to explore the tools and roles of AI in climate risk management of PPP infrastructure projects.

Systematically, this study compiles and analyses 36 peer-reviewed journal articles sourced from Scopus, Web of Science, Google Scholar and PubMed.

The results demonstrate deep learning, building information modelling, robotic automations, remote sensors and fuzzy logic as major key AI-based risk models (tools) for PPP infrastructures. The roles of AI in climate risk management of PPPs include risk detection, analysis, controls and prediction.

For researchers, the findings provide relevant guide for further investigations into AI and climate risks within the PPP research domain.

This article highlights the AI tools in mitigating climate crisis in PPP infrastructure management.

This article provides strong arguments for the utilisation of AI in understanding and managing numerous challenges related to climate change in PPP infrastructure projects.

The purpose of this paper is to understand motivation of young consumers to use artificial intelligence (AI) tools such as chatbots, voice assistants and augmented reality in shopping by generating Vroom’s expectancy theory of motivation using grounded theory approach.

Grounded theory approach has been used to develop the Vroom’s expectancy theory. Initially data were collected through participant interviews using theoretical sampling. These data were analyzed and coded using the three step process, i.e. open coding, axial coding and selective coding. The categories created during coding were integrated to generate Vroom’s expectancy theory of motivation.

The findings indicate that Vroom’s expectancy theory of motivation can be used to explain motivation of young consumers to use AI tools as an aid in taking shopping decisions. The motivation may be intrinsic motivation, extrinsic motivation or force choice motivation. Expectancy represents the ease of using the tools, instrumentality represents competence of tools in performing desired tasks while valence represents satisfaction, rewarding experience and trust in using of tools.

The findings of the study are based on grounded theory approach which is an inductive approach. Alternate research methodologies, both inductive and deductive, need to be employed to strengthen the external validity and generalize the results. The study is limited to shopping motives of young consumers in India. A comparison with other consumer motivational studies has not been done. Hence no claim is made regarding the advantage of Vroom’s theory over other motivational theories.

The study has strong implications for retailers in developing countries which are seen as an emerging market for retail and have introduced AI tools in recent years. The Vroom’s expectancy theory will help retailers to understand consumer motivation in using AI tools or shopping.

Vroom’s expectancy theory to understand consumer motivation to use AI tools in shopping was generated using the grounded theory approach.

This study aimed to evaluate the potential of artificial intelligence (AI) as a tool for knowledge synthesis, the production of written content and the delivery of coaching conversations.

The research employed the use of experts to evaluate the outputs from ChatGPT's AI tool in blind tests to review the accuracy and value of outcomes for written content and for coaching conversations.

The results from these tasks indicate that there is a significant gap between comparative search tools such as Google Scholar, specialist online discovery tools (EBSCO and PsycNet) and GPT-4's performance. GPT-4 lacks the accuracy and detail which can be found through other tools, although the material produced has strong face validity. It argues organisations, academic institutions and training providers should put in place policies regarding the use of such tools, and professional bodies should amend ethical codes of practice to reduce the risks of false claims being used in published work.

This is the first research paper to evaluate the current potential of generative AI tools for research, knowledge curation and coaching conversations.