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This study aims to evaluate the Accounting Information Systems (AIS) effectiveness on the organizational level by extending DeLone and McLean success model in developing countries like Jordan. Unlike other studies, this study investigates the effects of system quality, information quality, process quality, collaboration quality, service quality, individual performance and workgroup performance toward organizational performance during the period of the COVID-19 pandemic outbreak.

A self-administrated survey was used to collect data from 104 accounting managers operating in listed Jordanian firms in the Amman Stock Exchange (ASE). The partial least squares-structural equation modeling method was used for data analysis.

Eight relationships were tested, whereby six were supported as was expected. Particularly, the empirical results reveal that individual performance is positively and significantly influenced by information quality, process quality and service quality. Furthermore, workgroup performance is positively and significantly influenced by individual performance. The results also show that organizational performance is impacted by individual performance and workgroup performance.

The current study is among the first empirical studies that has extended the information systems success model to evaluate AIS success during COVID-19 pandemic. This study also offers insights into the importance of AIS success among listed Jordanian firms in ASE.

Various consumer-facing artificial intelligence (AI) applications are used to interact with consumers at all purchase stages, and related research has sharply increased. This study aims to synthesize the literature related to consumer–AI interaction using the customer journey framework, identify the factors affecting AI's effectiveness in interactive marketing and offer an agenda for future research.

This study undertakes a framework-based systematic review of 239 articles on AI in marketing from the consumer perspective published in peer-reviewed journals from 2007 to 2021.

This review identifies the roles of AI touch points and factors affecting the acceptance and effectiveness of consumer–AI interaction in each stage of the customer journey.

This study is the first to review the existing literature using a customer journey framework to identify the factors that influence customer interactions with AI touch points at each purchase stage and pave the way for future research.

The purpose of this study is to explore and examine the determinants of artificial intelligence (AI) adoption by human resource management (HRM). Further, the impact of AI adoption by HR department on their effectiveness has also been tested.

A model explaining the antecedents of AI adoption by HRM is proposed in this study. The proposed model is based on task–organization–environment and task–technology fit models. A two-step partial least square-based structural equational modelling (PLS-SEM) has been used for testing the model. Data was collected from 210 HRM employees (only senior level or specialized HR positions), working in IT firms located in Delhi-NCR region.

Literature review shows that among others, organizational preparedness, perceived benefits and technology readiness determine AI adoption which in turn can make HR system more effective. Results of PLS-SEM support all hypothesized relationships and validate the proposed model.

Considering paucity of research on antecedents of AI adoption by human resource department, this study adds significantly to the body of knowledge. Additionally, based on the findings of statistical analysis, certain AI-related recommendations are given to HRM.

Artificial intelligence (AI) is transforming consumers' experiences and how firms identify, create, nurture and manage interactive marketing relationships. However, most marketers do not have a clear understanding of what AI is and how it may mutually benefit consumers and firms. In this paper, the authors conduct an extensive review of the marketing literature, develop an AI framework for understanding value co-creation in interactive buyer–seller marketing relationships, identify research gaps and offer a future research agenda.

The authors first conduct an extensive literature review in 16 top marketing journals on AI. Based on this review, an AI framework for understanding value co-creation in interactive buyer–seller marketing relationships was conceptualized.

The literature review led to a number of key research findings and summary areas: (1) an historical perspective, (2) definitions and boundaries of AI, (3) AI and interactive marketing, (4) relevant theories in the domain of interactive marketing and (5) synthesizing AI research based on antecedents to AI usage, interactive AI usage contexts and AI-enabled value co-creation outcomes.

This is one of the most extensive reviews of AI literature in marketing, including an evaluation of in excess or 300 conceptual and empirical research. Based on the findings, the authors offer a future research agenda, including a visual titled “What is AI in Interactive Marketing? AI design factors, AI core elements & interactive marketing AI usage contexts.”

This study aims to explore whether, how and when leaders' artificial intelligence (AI) symbolization (i.e. the demonstration of leaders' acceptance of and support for AI by engaging in AI-related behaviors and/or displaying objects that reflect their affinity for AI) affects employee job crafting behaviors.

The authors conducted two studies (i.e. an experiment and a multi-wave field survey) with samples from different contexts (i.e. United States and China) to test our theoretical model. The authors used ordinary least squares (OLS) and hierarchical linear modeling (HLM) to test the hypotheses.

Leaders' AI symbolization is positively related to employee change readiness and, in turn, promotes employee job crafting. Moreover, employee-attributed impression management motives moderate the positive indirect effect of leaders' AI symbolization on employee job crafting via change readiness, such that this indirect effect is stronger when employee-attributed impression management motives are low (vs high).

Leaders should engage in AI symbolization to promote employee job crafting and avoid behaviors that may lead employees to attribute their AI symbolization to impression management.

By introducing the concept of leaders' AI symbolization, this study breaks new ground by illustrating how leaders' AI symbolization positively influences employees' change readiness, as well as job crafting in the workplace. Further, integrating AI as a novel and timely context for evaluating job crafting contributes to the literature where empirical research is relatively scant, particularly regarding the factors that prompt employees to engage in job crafting.

The purpose of this study is to gain empirical insights into whether accounting information systems (AIS) usage matters among Jordanian small and medium-sized enterprises (SMEs) during the period of COVID-19 pandemic.

The suggested research model in the current study is based on the extending technology acceptance model (TAM) to test the antecedents’ factors that impact on AIS usage among SMEs. To test the proposed research model, partial least squares structural equation modeling (PLS-SEM) was used.

The empirical findings revealed all postulated hypotheses were accepted except H3. Contrary to what is expected, the empirical outcomes confirmed that perceived compatibility does not affect the perceived usefulness of AIS, and hence, the related hypothesis was rejected.

The results of the current research could be beneficial to a number of managers (owners) to obtain a better understanding of the benefits of AIS success usage among Jordanian SMEs performance during crises time as the COVID-19 pandemic crisis.

While disruptions as a result of the COVID-19 pandemic resulted in the failure of some companies, others embraced innovative digital technologies to face the challenge posed by COVID-19. The COVID-19 crisis is also an opportunity for the extractive industry (EI) sectors to review their digitalization processes. The purpose of this paper is to conduct a systematic review of infectious disease mitigation in EI and to evaluate the resilience of these industries as they address pandemic prevention and control.

Multi-case studies including digital and organizational responses to COVID-19 were analyzed to evaluate the readiness of health risk management (HRM) and resilience of EIs against the pandemic. The evaluation uses Google Scholar and Trends searches to compare the level of relevant activity in EIs with other industries.

Although EI sectors have various plans for minimizing pandemic impacts, unexpected disruptions and delays of the COVID-19 responses revealed many limitations of the existing HRM system. Digital technologies (e.g. artificial intelligence-based public health monitoring, digital collaboration, wearable health tracking and 3D printing) demonstrated their remarkable benefits in the pandemic responses and nontechnical elements affecting technology adoption (TA).

Lessons learned from the deployment of digital technologies against the pandemic help to improve the organizational capacity to deal effectively with future outbreaks and suggest lessons for the future trajectory of TA in these industries.

This study, first, reviews the existing literature on electronic word-of-mouth (eWOM) and, using communication theory, examines its impact on its readers’ decision-making processes. Second, this paper aims to propose some elements of eWOM communications that might be further researched.

A literature review of relevant academic articles produced 97 works related to social communication theory, eWOM and new artificial intelligence trends in hospitality. Thereafter, potential avenues for future research were explored.

The study results showed: valence, relevance, understandability and visual cues are the most important antecedents of message usefulness and the reader’s behavioral intentions; source credibility is the sender characteristic that most affects the reader’s behavioral intentions and consumer susceptibility to interpersonal influence is the receiver characteristic that most influences their attitudes and behavioral intentions. In addition, the study highlights four relevant aspects for future research. First, more research into online fake reviews is needed to better understand sender motivations. Second, companies should actively manage negative reviews. Then, the careful choice of platforms on which companies promote their products/services. Finally, the role of artificial intelligence in increasing the effectiveness of eWOM in the hospitality industry.

This paper integrates – grounded on communication theory – results from previous studies about the central elements of communication (message, sender and receiver) and discusses the main trends in hospitality-related eWOM. In addition, the paper examines the potential of specific eWOM elements as future lines of research, in particular: fake reviews, strategies for dealing with negative reviews, the eWOM platform used and artificial intelligence applications.

Este estudio, en primer lugar, revisa la literatura existente sobre el boca-oído electrónico (eWOM) y, utilizando la teoría de la comunicación, examina su impacto en los procesos de toma de decisiones de sus lectores. En segundo lugar, proponemos algunos elementos de las comunicaciones eWOM que podrían investigarse más adelante.

Una revisión de la literatura de artículos académicos relevantes produjo 97 trabajos relacionados con la teoría de la comunicación social, eWOM y nuevas tendencias de inteligencia artificial en hotelería. A partir de entonces, se exploraron posibles vías de investigación futura.

Los resultados del estudio señalaron: 1) la valencia, la relevancia, la comprensibilidad y las señales visuales son los antecedentes más importantes de la utilidad del mensaje y las intenciones de comportamiento del lector; 2) La credibilidad de la fuente es la característica del emisor que más afecta las intenciones de comportamiento del lector; 3) La susceptibilidad del consumidor a la influencia interpersonal es la característica del receptor que más influye en sus actitudes e intenciones de comportamiento. Además, el estudio destaca cuatro aspectos relevantes para futuras investigaciones. Primero, se necesita más investigación sobre las reseñas falsas en línea para comprender mejor las motivaciones del emisor. En segundo lugar, las empresas deben gestionar activamente las críticas negativas. Luego, la cuidadosa elección de las plataformas en las que las empresas promocionan sus productos/servicios. Por último, el papel de la inteligencia artificial en el aumento de la eficacia de eWOM en la industria hotelera.

Este artículo integra –con base en la teoría de la comunicación– resultados de estudios previos sobre los elementos centrales de la comunicación (mensaje, emisor y receptor) y analiza las principales tendencias en eWOM relacionadas con la hostelería. Además, el artículo examina el potencial de elementos específicos de eWOM como líneas futuras de investigación, en particular: revisiones falsas, estrategias para lidiar con críticas negativas, la plataforma eWOM utilizada y aplicaciones de inteligencia artificial.

Palabras clave: eWOM, Teoría de comunicación, Revisiones falsas, Revisiones negativas, Inteligencia artificialTipo de artículo: Revisión de la literatura

本研究, 首先, 回顾了现有的关于电子口碑（eWOM）的文献, 并利用传播理论, 研究了它对读者决策过程的影响。其次, 我们提出了一些可能需要进一步研究的电子口碑传播的要素。

对相关学术文章的文献回顾产生了97篇与社会传播理论、eWOM和酒店业新的人工智能趋势有关的作品。此后, 对未来研究的潜在途径进行了探讨。

研究结果显示。1）价值、相关性、可理解性和视觉线索是信息有用性和读者行为意图的最重要的前因; 2）来源的可信度是最能影响读者行为意图的发送者特征; 3）消费者对人际影响的易感性是最能影响他们态度和行为意图的接受者特征。此外, 该研究还强调了未来研究的四个相关方面。首先, 需要对网上虚假评论进行更多的研究, 以更好地了解发送者的动机。其次, 公司应该积极管理负面评论。然后, 谨慎选择公司推广其产品/服务的平台。最后, 人工智能在提高酒店业eWOM的有效性方面的作用。

本文以传播理论为基础, 整合了以往关于传播中心要素（信息、发送者和接受者）的研究结果, 并讨论了与酒店业相关的电子WOM的主要趋势。此外, 本文还研究了特定的eWOM要素作为未来研究方向的潜力, 特别是：虚假评论、处理负面评论的策略、使用的eWOM平台和人工智能应用。

关键词：eWOM, 传播理论, 虚假评论, 负面评论, 人工智能。

Determining the number of circulating kanban cards is important in order effectively to operate a just‐in‐time with kanban production system. While a number of techniques exist for setting the number of kanbans, artificial neural networks (ANNs) and classification and regression trees (CARTs) represent two practical approaches with special capabilities for operationalizing the kanban setting problem. This paper provides a comparison of ANNs with CART for setting the number of kanbans in a dynamically varying production environment. Our results show that both methods are comparable in terms of accuracy and response speed, but that CARTs have advantages in terms of explainability and development speed. The paper concludes with a discussion of the implications of using these techniques in an operational setting.

This paper aims to develop an artificial intelligence (AI) audit tool for auditing text-based evidence and determine its efficiency and effectiveness.

A manual audit checklist and an AI audit tool are developed with fuzzy front-end (FFE) from Innovation Management System Standard (IMSS) as the audit scope, First, a manual audit of five organisations is conducted to determine their compliance scores. The transcripts of the audit are recorded which are used by the AI audit tool to assign compliance scores for the same organisations. The effectiveness and efficiency of the AI audit tool are determined by comparing their results with the manual audit.

This paper demonstrates the development of the FFE AI audit tool which led to 92% improved efficiency while being 95% effective compared to a human auditor.

The publication of new financial and non-financial standards (such as ISO56002: IMSS) have implications for internal auditing (IA). The scope of IA must expand to include new standards while remaining efficient. Emerging technologies, such as AI help achieve this. Even though the use of AI in financial auditing is widely studied, it has not received similar attention in non-financial auditing. This paper develops a non-financial AI audit tool to audit an essential component of the IMSS, the FFE of innovation and determine its efficiency and effectiveness.

The study develops an FFE AI audit tool for the first time. The methodology used has practical and academic implications for the use of AI in non-financial auditing.