Artificial intelligence in logistics and supply chain management: A primer and roadmap for research

Journal of Business Logistics, Год журнала: 2023, Номер 44(4), С. 532 - 549

Опубликована: Сен. 29, 2023

Abstract The dawn of generative artificial intelligence (AI) has the potential to transform logistics and supply chain management radically. However, this promising innovation is met with a scholarly discourse grappling an interplay between capabilities drawbacks. This conversation frequently includes dystopian forecasts mass unemployment detrimental repercussions concerning academic research integrity. Despite current hype, existing exploring intersection AI (L&SCM) sector remains limited. Therefore, editorial seeks fill void, synthesizing applications within L&SCM domain alongside analysis implementation challenges. In doing so, we propose robust framework as primer roadmap for future research. will give researchers organizations comprehensive insights strategies navigate complex yet landscape integration domain.

Язык: Английский

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Generative artificial intelligence in supply chain and operations management: a capability-based framework for analysis and implementation

International Journal of Production Research, Год журнала: 2024, Номер 62(17), С. 6120 - 6145

Опубликована: Янв. 31, 2024

This research examines the transformative potential of artificial intelligence (AI) in general and Generative AI (GAI) particular supply chain operations management (SCOM). Through lens resource-based view based on key capabilities such as learning, perception, prediction, interaction, adaptation, reasoning, we explore how GAI can impact 13 distinct SCOM decision-making areas. These areas include but are not limited to demand forecasting, inventory management, design, risk management. With its outcomes, this study provides a comprehensive understanding GAI's functionality applications context, offering practical framework for both practitioners researchers. The proposed systematically identifies where be applied SCOM, focussing enhancement, process optimisation, investment prioritisation, skills development. Managers use it guidance evaluate their operational processes identify deliver improved efficiency, accuracy, resilience, overall effectiveness. underscores that GAI, with multifaceted applications, open revolutionary substantial implications future practices, innovations, research.

Язык: Английский

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Multidisciplinary collaboration: key players in successful implementation of ChatGPT and similar generative artificial intelligence in manufacturing, finance, retail, transportation, and construction industry

Опубликована: Окт. 17, 2023

The emergence of generative artificial intelligence (AI), exemplified by ChatGPT, has fundamentally transformed numerous sectors amplifying operational efficiency, output, and customer satisfaction. However, effectively integrating such sophisticated AI systems, especially in manufacturing, finance, retail, transportation, construction, demands concerted efforts from cross-functional teams. This investigation delves into the indispensable role played these teams ensuring seamless integration ChatGPT akin technologies across diverse fields. In research underscores vital significance collaboration between specialists, industrial engineers, production managers to optimize manufacturing processes, preemptive maintenance, quality assurance. finance sector, study highlights essential synergy data scientists, regulatory experts, financial analysts harness ChatGPT's complete potential automating tasks, detecting fraud, providing personalized interactions. For retail industry, this accentuates necessity collaborative marketing strategists, user experience designers, developers utilizing for targeted campaigns, virtual shopping assistants, instantaneous support. It explores how can facilitate assimilation boost engagement, inventory management, predict consumer trends, thereby propelling business growth competitive advantage. transportation imperative planners, software developers, experts leveraging efficient route planning, predictive vehicle real-time logistics oversight. construction importance cohesive among architects, civil programmers project design enhancement, risk mitigation. By promoting collaboration, effective communication, cross-domain expertise, are instrumental harnessing transformative AI, industries toward a more efficient, sustainable, technologically advanced future.

Язык: Английский

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ChatGPT and generative artificial intelligence: an exploratory study of key benefits and challenges in operations and supply chain management

International Journal of Production Research, Год журнала: 2023, Номер 62(16), С. 5676 - 5696

Опубликована: Дек. 20, 2023

ChatGPT and generative artificial intelligence (Gen-AI) are transforming firms supply chains. However, the empirical literature reporting benefits, challenges, outlook of these nascent technologies in operations chain management (OSCM) is limited. This study surveys current projects perceptions US (n = 119) UK 181) We found that range from proof-of-concept to full implementation, with a main focus on operational gains, such as improved customer satisfaction, cost minimisation, process efficiencies. The challenges concern data, technological organisational issues. Expected benefits dominated by savings enhanced experience, but also include increased automation sustainability. Industries were cluster around six groups according perceived implementation challenges. Our findings contribute emerging Gen-AI use OSCM, practice mapping outlook, maturity level

Язык: Английский

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Enhancing trust in online grocery shopping through generative AI chatbots

Journal of Business Research, Год журнала: 2024, Номер 180, С. 114737 - 114737

Опубликована: Май 24, 2024

Generative Artificial Intelligence (GAI) is witnessing a lot of adoption across industries, but literature yet to fully document the nuances these applications. We develop comprehensive framework for understanding factors that affect trust in online grocery shopping (OGS) using GAI chatbots. Our exploratory study was conducted via interviews, which helped build our model. integrate Elaboration Likelihood Model (ELM) and Status Quo Bias (SQB) theory Unified Framework Trust on Technology Platforms. In confirmatory study, by analyzing 372 responses from users, structural equation modelling (SEM), we initially validate path Subsequently, used fuzzy set qualitative comparative analysis (fsQCA) check causal combinations explain different levels. Apart perceived regret avoidance, all other had significant effect attitude trust. Perceived anthropomorphism moderated associations between interaction quality, credibility, threat, attitude.

Язык: Английский

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AI hype as a cyber security risk: the moral responsibility of implementing generative AI in business

AI and Ethics, Год журнала: 2024, Номер 4(3), С. 791 - 804

Опубликована: Фев. 23, 2024

Abstract This paper examines the ethical obligations companies have when implementing generative Artificial Intelligence (AI). We point to potential cyber security risks are exposed rushing adopt AI solutions or buying into “AI hype”. While benefits of for business been widely touted, inherent associated less well publicised. There growing concerns that race integrate is not being accompanied by adequate safety measures. The rush buy hype and fall behind competition potentially exposing broad possibly catastrophic cyber-attacks breaches. In this paper, we outline significant threats models pose, including ‘backdoors’ in could compromise user data risk ‘poisoned’ producing false results. light these concerns, discuss moral considering principles beneficence, non-maleficence, autonomy, justice, explicability. identify two examples concern, overreliance over-trust AI, both which can negatively influence decisions, leaving vulnerable threats. concludes recommending a set checklists implementation environment minimise based on discussed responsibilities concern.

Язык: Английский

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Building entrepreneurial resilience during crisis using generative AI: An empirical study on SMEs

Technovation, Год журнала: 2024, Номер 135, С. 103063 - 103063

Опубликована: Июнь 25, 2024

Recently, Gen AI has garnered significant attention across various sectors of society, particularly capturing the interest small business due to its capacity allow them reassess their models with minimal investment. To understand how and medium-sized firms have utilised AI-based tools cope market's high level turbulence caused by COVID-19 pandemic, geopolitical crises, economic slowdown, researchers conducted an empirical study. Although is receiving more attention, there remains a dearth studies that investigate it influences entrepreneurial orientation ability cultivate resilience amidst market turbulence. Most literature offers anecdotal evidence. address this research gap, authors grounded theoretical model hypotheses in contingent view dynamic capability. They tested using cross-sectional data from pre-tested survey instrument, which yielded 87 useable responses medium enterprises France. The used variance-based structural equation modelling commercial WarpPLS 7.0 software test model. study's findings suggest EO influence on building as higher-order lower-order capabilities. However, negative moderating effect path joins resilience. results assumption will positive effects capabilities competitive advantage not always true, linear does hold, consistent some scholars' assumptions. offer contributions open new avenues require further investigation into non-linear relationship

Язык: Английский

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Modelling supply chain Visibility, digital Technologies, environmental dynamism and healthcare supply chain Resilience: An organisation information processing theory perspective

Transportation Research Part E Logistics and Transportation Review, Год журнала: 2024, Номер 188, С. 103613 - 103613

Опубликована: Июнь 15, 2024

Язык: Английский

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An Empirical Evaluation of a Generative Artificial Intelligence Technology Adoption Model from Entrepreneurs’ Perspectives

Systems, Год журнала: 2024, Номер 12(3), С. 103 - 103

Опубликована: Март 18, 2024

Technologies, such as Chat Generative Pre-Trained Transformer (ChatGPT), are prime examples of Artificial Intelligence (AI), which is a constantly evolving area. SMEs, particularly startups, can obtain competitive edge, innovate their business models, gain value, and undergo digital transformation by implementing these technologies. Continuous but gradual experimentation with technologies the foundation for adoption. The experience that comes from trying new help entrepreneurs adopt more strategically experiment them. urgent need an in-depth investigation highlighted paucity previous research on ChatGPT uptake in startup context, entrepreneurial perspective. objective this study to empirically validate AI technology adoption model establish direction strength correlations among factors perspectives entrepreneurs. data collected 482 who exhibit great diversity genders, countries startups located, industries serve, age, educational levels, work entrepreneurs, length time have been market. Collected analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) technique, results statistical examination relationships between model’s factors. indicate social influence, domain experience, familiarity, system quality, training support, interaction convenience, anthropomorphism impact pre-perception perception phase These motivate technology, thereby building perceptions its usefulness, perceived ease use, enjoyment, three turn affect emotions toward and, finally, switching intentions. Control variables like gender, attainment no appreciable effect intentions alternatives technology. Rather, factor running businesses shows itself be crucial one. practical implications other innovation ecosystem actors, including, instance, providers, libraries, policymakers. This enriches acceptance theory extends existing literature introducing stages specific entrepreneurship.

Язык: Английский

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Generative Artificial Intelligence (AI) Technology Adoption Model for Entrepreneurs: Case of ChatGPT

Internet Reference Services Quarterly, Год журнала: 2024, Номер 28(2), С. 223 - 242

Опубликована: Янв. 5, 2024

This article presents an extensive Generative AI Technology Adoption Model intended to elucidate the complex process that entrepreneurs and other innovation ecosystem actors, for instance, libraries, go through its adoption. The model suggests adoption happens in three stages: Pre-Perception & Perception, Assessment, Outcome. During Perception Phase, initiate their technology exploration by navigating social factors, domain experience, technological familiarity, system quality, training support, interaction convenience, anthropomorphism; with utilitarian value hedonic values playing important role. As they transition Assessment Stage, perceived usefulness, ease of use, a novel addition, enjoyment, shape evaluations, leading generations emotions toward it, overweighting values. finishes Outcome where developed Stage become tangible intentions switch (use or human services). highlights factors (also called latent variables) relationships grounded on researcher's professional experiences need be further empirically validated. Entrepreneurial implications highlight strategic insights model, providing decision-making roadmap highlighting between hedonistic Entrepreneurs can create well-informed integrations are line business objectives using incremental process. model's focus comparative evaluations gives ability strategically map usability best possible commercial results. offers nuanced understanding entrepreneurs' processes, which is also applicable actors ecosystem.

Язык: Английский

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