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The objective of this research work is to study the artificial intelligence (AI)-based product benefits and problems of the agritech industry. The research variables were developed from the existing review of literature connecting to AI-based benefits and problems, and 90 samples of primary data from agritech industry managers were gathered using a survey of a well-structured research questionnaire. The statistical package of IBM-SPSS 21 was utilized to analyze the data using the statistical techniques of descriptive and inferential statistical analysis. Results show that better information for faster decision-making has been ranked as the topmost AI benefit. This implies that the executives of agritech units have a concern about the quality of decisions they make and resistance to change from employees and internal culture has been ranked as the topmost AI problem.

This study aims to empirically examine accounting information system (AIS) success at the organizational level during COVID-19 era.

Based on the information system success model, this paper developed its model and proposed a total of nine hypotheses. This paper gathered the required data via a questionnaire from Yemeni small and medium enterprises (SMEs) owners and managers. To test the proposed research model paths, SmartPLS software, which is known as partial least squares structural equation modeling, was used.

The results showed that the quality dimensions (information quality and system quality) positively affected the use of AIS and satisfaction; user satisfaction positively affected the use of AIS. Management support positively affected the AIS users' usage and satisfaction. Finally, the use dimensions (user satisfaction and usage) positively impacted the net benefits in terms of gaining a competitive advantage, productivity enhancement and saving time and cost. In all, this research has succeeded in providing support for DeLone and McLean's IS success model at the organizational level during the COVID-19 era.

AIS is becoming increasingly important for SMEs in low-income countries like Yemen, particularly in the present pandemic conditions (COVID-19 era). By using AIS, users can access the enterprise's data and conduct transactions without being limited by distance. Indeed, AIS proved its ability in enhancing the net benefits at the organizational level in the COVID-19 era in terms of gaining a competitive advantage, productivity enhancement and saving time and cost. However, AIS can only be considered useful to the enterprise if it is effective/successful.

This study is one of the first to have assessed the impact of AIS success at the organizational level in the era of COVID-19 pandemic, the context of Yemeni SMEs.

The purpose of this study is to test the antecedent factors that directly influence accounting information systems (AIS) usage, which, in turn, affects net benefits of AIS usage, drawing upon the technology acceptance model (TAM) in the context of Jordan.

To test the suggested research model, an online questionnaire was employed to collect data from 213 owners in Jordanian small and medium-sized enterprises (SMEs). The data were analyzed utilizing bootstrapped procedure by the partial least Squares-structural equation modeling (PLS-SEM).

Out of ten postulated hypotheses, eight were accepted. Mainly, the empirical outcomes confirm the suggested hypotheses that the perceived usefulness (PUS) of AIS is positively and significantly impacted by perceived convenience (PCN) and perceived ease of use (PEU). Besides, the outcomes confirm that AIS usage is significantly influenced by PUS, PEU and perceived compatibility (PCM). Finally, the net benefits of AIS are positively influenced by AIS usage and information technology (IT) knowledge, whereby it was revealed that IT knowledge has a direct and indirect effect.

This study addresses a vital research gap in the literature by suggesting a comprehensive research model that can help garner enhanced usage of an AIS to obtain a better achievement among Jordanian SMEs performance.

The purpose of this research study was to understand the simultaneous competitive and social gains of machine learning (ML) and artificial intelligence (AI) usage in organizations. There was a knowledge hiatus regarding the contribution of the deployment of ML and AI technologies and their effects on organizations and society.

This study was grounded on the dynamic capabilities (DC) and ML and AI automation-augmentation paradox literature. This research study examined these theoretical perspectives using the response of 239 Indian organizational chief technology officers (CTOs). Partial least square-structural equation modeling (PLS-SEM) path modeling was applied for data analysis.

The results indicated that ML and AI technologies organizational usage positively influenced DC initiatives. The findings depicted that DC fully mediated ML and AI-based technologies' effects on firm performance and social performance.

This study contributed to theoretical discourse regarding the tension between organizational and social outcomes of ML and AI technologies. The study extended the role of DC as a vital strategy in achieving social benefits from ML and AI use. Furthermore, the theoretical tension of the automation-augmentation paradox was explored.

Organizations deploying ML and AI technologies could apply this study's insights to comprehend the organizational routines to pursue simultaneous competitive benefits and social gains. Furthermore, chief technology executives of organizations could devise how ML and AI technologies usage from a DC perspective could help settle the tension of the automation-augmentation paradox.

Increased ML and AI technologies usage in organizations enhanced DC. They could lead to positive social benefits such as new job creation, increased compensation to skilled employees and greater gender participation in employment. These insights could be derived based on this research study.

This study was among the first few empirical investigations to provide theoretical and practical insights regarding the organizational and societal benefits of ML and AI usage in organizations because of their DC. This study was also one of the first empirical investigations that addressed the automation-augmentation paradox at the enterprise level.

The purpose of our study was two-fold: (1) to examine the effect of perceived value derived from perceived benefits and sacrifices in the adoption of artificial intelligence (AI) in talent acquisition and (2) to investigate the moderating role of human resource (HR) readiness in the association between perceived value and AI adoption intention.

A structured questionnaire was administered to 198 talent acquisition executives and HR professionals of Indian IT companies based on a purposive sampling technique. Partial least squares structural equation modeling (PLS-SEM) was used on the Smart PLS 2.0 platform to analyse the data and test the model.

Results revealed that perceived benefits and sacrifices significantly predict perceived value which significantly affects the HR professional’s AI adoption intention. The study further found that HR readiness moderates the link between perceived value and the intention of HR professionals to adopt AI in the talent acquisition process in the Indian IT industry.

IT companies are advised to continuously monitor and evaluate the performance of AI tools to ensure that they are meeting the recruitment process needs to leverage AI’s benefits in talent acquisition. This study seeks to provide the impetus for a planned AI adoption in talent acquisition.

This research provides ample evidence for the existing technology adoption theories. It explored the predictors of adoption by validating the value-based adoption model in the Indian context. It provides valuable insights into the practice of acquiring talents in the IT sector using artificial intelligence.

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.

This study aims to examine hotel guests’ acceptance of technology agency – the extent to which they would let artificial intelligence (AI)-based systems make decisions for them when staying in hotels. The examination was conducted through the prism of several antecedents of acceptance of technology agency, including perceived ethics, benefits, risks and convenience orientation.

A thorough literature review provided the foundation of the structural model, which was tested using confirmatory factor analysis, followed by structural equation modeling. Data were collected from 400 US hotel guests.

The most important determinant of acceptance of technology agency was perceived ethics, followed by benefits. Risks of using AI-based systems to make decisions for consumers had a negative impact on acceptance of technology agency. In addition, perceived loss of competence and unpredictability had relatively strong impacts on risks.

The results provide a conceptual foundation for research on systems that make decisions for consumers. As AI is increasingly incorporated in the business models of hotel companies to make decisions, ensuring that the decisions are perceived as ethical and beneficial for consumers is critical to increase the utilization of such systems.

Most research on AI in hospitality is either conceptual or focuses on consumers’ intentions to stay in hotels that may be equipped with AI technologies. Occupying a unique position within the literature, this study discusses the first time AI-based systems that make decisions for consumers. The value of this study stems from the examination of the main concept of technology agency, which was never examined in hospitality.

Organizations across industries are increasingly using Artificial Intelligence (AI) systems to support their innovation processes, supply chains, marketing and sales and other business functions. Implementing AI, firms report efficiency gains from automation and enhanced decision-making thanks to more relevant, accurate and timely predictions. By exposing the benefits of digitizing everything, COVID-19 has only accelerated these processes. Recognizing the growing importance of AI and its pervasive impact, this chapter defines the “social value of AI” as the combined value derived from AI adoption by multiple stakeholders of an organization. To this end, we discuss the benefits and costs of AI for a business-to-business (B2B) firm and its internal, external and societal stakeholders. Being mindful of legal and ethical concerns, we expect the social value of AI to increase over time as the barriers for adoption go down, technology costs decrease, and more stakeholders capture the value from AI. We identify the contributions to the social value of AI, by highlighting the benefits of AI for different actors in the organization, business consumers, supply chain partners and society at large. This chapter also offers future research opportunities, as well as practical implications of the AI adoption by a variety of stakeholders.

The purpose of this paper is to examine the likely impact of AI robotics technology on the labor market through the lens of comparative advantage.

The first section reviews the recent success of AI in outperforming humans in cognitive intense activities such as Go, poker and other strategic games, which portends a shift in comparative advantage in human brain power work to machines. It notes the potential for a portfolio of specialized computer algorithms to compete with human general intelligence in work. The analysis contributes to the debate between economists dubious about claims that AI robotics will disrupt work and futurists who expect many jobs to be fully automated in coming years. It advances three “laws of robo-economics” to guide thinking about the new technologies and presents evidence that growing robot intensity has begun to impact the job market.

The paper finds that the case for AI robotics substantially changing the world of work and the distribution of income is more compelling than the case that it will have similar impacts on wages and employment as past technological changes. It advances an ownership solution to spread the benefits of AI robot-driven automation widely.

To the extent that who owns the robots rules the world, it argues for a concerted social effort to widen the “who” in ownership from the few to the many. It reviews policies to expand employee ownership of their own firm and of the stream of revenue via profit-sharing and gain-sharing bonuses. But the paper notes that ensuring that growth of AI robotics benefits all through ownership will require expansion of workers’ and citizens’ stake in business broadly, through collective investment via pension funds, individual investment in mutual funds and development of sovereign wealth funds.

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.