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In this study, the authors examine artificial knowledge as a fundamental stream of knowledge management for sustainable and resilient business models in supply chain management (SCM). The study aims to provide a comprehensive overview of artificial knowledge and digitalization as key enablers of the improvement of SCM accountability and sustainable performance towards the UN 2030 Agenda.

Using the SCOPUS database and Google Scholar, the authors analyzed 135 English-language publications from 1990 to 2022 to chart the pattern of knowledge production and dissemination in the literature. The data were collected, reviewed and peer-reviewed before conducting bibliometric analysis and a systematic literature review to support future research agenda.

The results highlight that artificial knowledge and digitalization are linked to the UN 2030 Agenda. The analysis further identifies the main issues in achieving sustainable and resilient SCM business models. Based on the results, the authors develop a conceptual framework for artificial knowledge and digitalization in SCM to increase accountability and sustainable performance, especially in times of sudden crises when business resilience is imperative.

The study results add to the extant literature by examining artificial knowledge and digitalization from the resilience theory perspective. The authors suggest that different strategic perspectives significantly promote resilience for SCM digitization and sustainable development. Notably, fostering diverse peer exchange relationships can help stimulate peer knowledge and act as a palliative mechanism that builds digital knowledge to strengthen and drive future possibilities.

This research offers valuable guidance to supply chain practitioners, managers and policymakers in re-thinking, re-formulating and re-shaping organizational processes to meet the UN 2030 Agenda, mainly by introducing artificial knowledge in digital transformation training and education programs. In doing so, firms should focus not simply on digital transformation but also on cultural transformation to enhance SCM accountability and sustainable performance in resilient business models.

This study is, to the authors' best knowledge, among the first to conceptualize artificial knowledge and digitalization issues in SCM. It further integrates resilience theory with institutional theory, legitimacy theory and stakeholder theory as the theoretical foundations of artificial knowledge in SCM, based on firms' responsibility to fulfill the sustainable development goals under the UN's 2030 Agenda.

The study examines the impact of behavioral biases, such as herd behavior, overconfidence and reactions to ESG News, on Socially Responsible Investing (SRI) decisions in the Indian context. Additionally, it explores gender differences in SRI decisions, thereby deepening the understanding of the factors shaping SRI choices and their implications for sustainable finance and gender-inclusive investment strategies.

The study employs Bayesian linear regression to analyze the impact of behavioral biases on SRI decisions among Indian investors since it accommodates uncertainties and integrates prior knowledge into the analysis. Posterior distributions are determined using the Markov chain Monte Carlo technique, ensuring robust and reliable results.

The presence of behavioral biases presents challenges and opportunities in the financial sector, hindering investors’ SRI engagement but offering valuable opportunities for targeted interventions. Peer advice and hot stocks strongly predict SRI engagement, indicating external influences. Investors reacting to extreme ESG events increasingly integrate sustainability into investment decisions. Gender differences reveal a greater inclination of women towards SRI in India.

The sample size was relatively small and restricted to a specific geographic region, which may limit the generalizability of the findings to other areas. While efforts were made to select a diverse sample, the results may represent something different than the broader population. The research focused solely on individual investors and did not consider the perspectives of institutional investors or other stakeholders in the SRI industry.

The study's practical implications are twofold. First, knowing how behavioral biases, such as herd behavior, overconfidence, and reactions to ESG news, affect SRI decisions can help investors and managers make better and more sustainable investment decisions. To reduce biases and encourage responsible investing, strategies might be created. In addition, the discovery of gender differences in SRI decisions, with women showing a stronger propensity, emphasizes the need for targeted marketing and communication strategies to promote more engagement in sustainable finance. These implications provide valuable insights for investors, managers, and policymakers seeking to advance sustainable investment practices.

The study has important social implications. It offers insights into the factors influencing individuals' SRI decisions, contributing to greater awareness and responsible investment practices. The gender disparities found in the study serve as a reminder of the importance of inclusivity in sustainable finance to promote balanced and equitable participation. Addressing these disparities can empower individuals of both genders to contribute to positive social and environmental change. Overall, the study encourages responsible investing and has a beneficial social impact by working towards a more sustainable and socially conscious financial system.

This study addresses a significant research gap by employing Bayesian linear regression method to examine the impact of behavioral biases on SRI decisions thereby offering more meaningful results compared to conventional frequentist estimation. Furthermore, the integration of behavioral finance with sustainable finance offers novel perspectives, contributing to the understanding of investors, investment managers, and policymakers, therefore, catalyzing responsible capital allocation. The study's exploration of gender dynamics adds a new dimension to the existing research on SRI and behavioral finance.

This paper aims to provide empirical evidence on adopting artificial intelligence (AI), including generative AI, in knowledge management (KM) processes and its impact on organisational decision-making. Specifically, the study addresses three key research questions: RQ1: How is (generative) AI adopted within KM processes in organisations? RQ2: What factors influence the adoption of AI in these processes, either facilitating or inhibiting it? RQ3: How does AI adoption in KM processes affect organisational decision-making?

An explorative investigation has been conducted through semi-structured interviews with KM and AI experts from a worldwide sample of 52 mostly private, large and for-profit organisations. Interviews have been analysed through a mixed thematic analysis.

The study provides an original framework in which the three investigated concepts are interconnected according to a dual relationship: linear and retroactive and 20 factors affecting AI adoption within KM processes.

The provided model guides managers in improving their organisational decision-making through AI adoption in KM processes. Moreover, according to the rational decision-making model, the authors propose a six-step systematic procedure for managers.

To the best of the authors’ knowledge, this is the first study that simultaneously addresses AI, KM and decision-making and provides an integrated framework showing the relationships between them, allowing organisations to better and practically understand how to ameliorate their decision-making through AI adoption in KM processes.

This study aims to meticulously evaluate the public service value-generation process facilitated by collaborative e-governance services within the framework of the National e-governance Plan (NeGP).

The study formulates a comprehensive research model through a combination of literature review, insights from domain experts and hands-on experience gained from the e-governance project. A conceptual research model was meticulously structured, validated, and interpreted by using a reflective measurement theory. The analytical tool SmartPLS3 was used to assess the proposed model rigorously.

The analysis of collected data reveals a statistically significant positive correlation between the implementation of collaborative e-governance strategies and the creation of public service value. This relationship is further reinforced by a strong alignment between the perceived aspects of collaborative e-governance, such as responsiveness, transparency and service delivery and their substantial contribution to the enhancement of public service value.

This study contributes to the scholarly discourse by introducing an innovative methodology for assessing public service value through analyzing empirical data from citizen-centric collaborative e-governance projects. It is noteworthy that no prior studies have examined the nuanced concept of public service value in the context of collaborative e-governance.

This study investigates the factors influencing citizen attitudes toward e-government services and their effects on the adoption of e-government services in Saudi Arabia. It sheds light on the moderating role of citizen motivation in the relationship between factors influencing citizen attitudes in favor of e-government services and their adoption and usage behavior in the Saudi Arabian context. The study analyzes both the drivers propelling the uptake and the barriers impeding it.

A descriptive research design was employed in this study, which surveyed 487 respondents from Jeddah and Madina cities and the surrounding region. The research identifies key drivers, including cultural factors, digital literacy, government policy and interventions, privacy and security, technical infrastructure, support services and citizen trust, alongside barriers such as concerns about data security and digital literacy.

The findings reveal the complex interplay of these factors in shaping citizen attitudes toward e-government services and their effects on adoption in Saudi Arabia. The study indicates that citizen motivation toward e-government services moderates the relationship between, adoption and usage behavior.

This study contributes valuable insights for policymakers and practitioners by offering a nuanced perspective on e-government service adoption in the Saudi Arabian context. It enhances our understanding of the factors influencing citizen attitudes and their impact on e-government adoption, highlighting the importance of citizen motivation as a moderating factor in this relationship.

Recent advancements in Artificial Intelligence (AI) and, at its core, Machine Learning (ML) offer opportunities for organizations to develop new or enhance existing capabilities. Despite the endless possibilities, organizations face operational challenges in harvesting the value of ML-based capabilities (MLbC), and current research has yet to explicate these challenges and theorize their remedies. To bridge the gap, this study explored the current practices to propose a systematic way of orchestrating MLbC development, which is an extension of ongoing digitalization of organizations.

Data were collected from Finland's Artificial Intelligence Accelerator (FAIA) and complemented by follow-up interviews with experts outside FAIA in Europe, China and the United States over four years. Data were analyzed through open coding, thematic analysis and cross-comparison to develop a comprehensive understanding of the MLbC development process.

The analysis identified the main components of MLbC development, its three phases (development, release and operation) and two major MLbC development challenges: Temporal Complexity and Context Sensitivity. The study then introduced Fostering Temporal Congruence and Cultivating Organizational Meta-learning as strategic practices addressing these challenges.

This study offers a better theoretical explanation for the MLbC development process beyond MLOps (Machine Learning Operations) and its hindrances. It also proposes a practical way to align ML-based applications with business needs while accounting for their structural limitations. Beyond the MLbC context, this study offers a strategic framework that can be adapted for different cases of digital transformation that include automation and augmentation of work.

While scholars recognize that digital technology is a major tool employed by contemporary learners to access knowledge, its full capacity is yet to be utilized. This paper investigates opportunities to increase individual learning engagement and knowledge retention in higher education and corporate environments through integrating educational content with innovative digital technology. Currently, within commonly adopted e-learning platforms, the most utilized online content delivery and student progress assessment tools in leadership education appear to be discussion-based instruction, along with written assignments, and research projects (Jenkins, 2016). To inform leadership educators’ choice in innovative tools for increased effectiveness of instruction, this paper shares an experience of delivering integrated global leadership education pedagogical content (i.e., cross-cultural competency instruction for graduate management class at a U.S.-based public university) within a gamified real-time multiuser learning platform—MyAltis—and discusses implications for leadership education.

The purpose of this paper is to understand the nomological network of associations between collective mindfulness and big data analytics in fostering resilient humanitarian relief supply chains.

The authors conceptualize a research model grounded in literature and test the hypotheses using survey data collected from informants at humanitarian aid organizations in Africa and Europe.

The findings demonstrate that organizational mindfulness is key to enabling resilient humanitarian relief supply chains, as opposed to just big data analytics.

This is the first study to examine organizational mindfulness and big data analytics in the context of humanitarian relief supply chains.

Although businesses continue to take up artificial intelligence (AI), concerns remain that companies are not realising the full value of their investments. The study aims to provide insights into how AI creates business value by investigating the mediating role of Business Process Management (BPM) capabilities.

The integrative model of IT Business Value was contextualised, and structural equation modelling was applied to validate the proposed serial multiple mediation model using a sample of 448 organisations based in the EU.

The results validate the proposed serial multiple mediation model according to which AI adoption increases organisational performance through decision-making and business process performance. Process automation, organisational learning and process innovation are significant complementary partial mediators, thereby shedding light on how AI creates business value.

In pursuing a complex nomological framework, multiple perspectives on realising business value from AI investments were incorporated. Several moderators presenting complementary organisational resources (e.g. culture, digital maturity, BPM maturity) could be included to identify behaviour in more complex relationships. The ethical and moral issues surrounding AI and its use could also be examined.

The provided insights can help guide organisations towards the most promising AI activities of process automation with AI-enabled decision-making, organisational learning and process innovation to yield business value.

While previous research assumed a moderated relationship, this study extends the growing literature on AI business value by empirically investigating a comprehensive nomological network that links AI adoption to organisational performance in a BPM setting.

The aim of this study is to assess the mediating effect of source appearance (SA) and self-influencer congruence (SIC) on the relationship between visual content (VC) and customer engagement (CE) towards mobile advertisement.

This study uses a quantitative approach to test the proposed model based on the stimulus–organism–response (SOR) theory. The non-probability purposive sampling technique was used to collect data from Pakistani mobile users through a self-administered questionnaire.

The study results prove that VC alone cannot generate mobile users’ engagement. SA is the key in this regard, which has a relatively higher importance compared to SIC. Furthermore, the serial mediation effect of SA and SIC on CE shows that attractive sources are likely to induce higher SIC and subsequent CE.

The results reveal that without a pleasing SA and positive SIC, mobile users skip the ads by perceiving them to be irritating or interruptive. Mobile ads might cost relatively less, but the advertisers should understand the significance of the SA toward minimizing the mobile advertising skepticism.

Advertisers can enhance the user’s engagement on mobile devices by addressing both the SA and SIC in their VC. The combined effect of both the SA and SIC on CE has not been assessed before. Furthermore, this study has used the SOR mechanism to examine CE.

El objetivo general de esta investigación es evaluar el efecto mediador de la apariencia de la fuente (SA) y la congruencia del auto-influencer (SIC) en la relación entre el contenido visual (CV) y el compromiso del cliente (CE) hacia la publicidad móvil.

Este estudio utiliza un enfoque cuantitativo para probar el modelo propuesto basado en la teoría estímulo-organismo-respuesta (SOR). Se utilizó la técnica de muestreo intencional no probabilístico para recoger datos de usuarios de móviles paquistaníes mediante un cuestionario autoadministrado.

Nuestros resultados demuestran que el contenido visual por sí solo no puede generar el compromiso de los usuarios de móviles. La apariencia de la fuente es la clave a este respecto, que tiene una importancia relativamente mayor en comparación con la congruencia del auto-influencer. Además, el efecto de mediación en serie de SA y SIC en CE muestra que es probable que las fuentes atractivas induzcan un mayor SIC y el consiguiente compromiso del cliente.

Los resultados revelan que sin una SA agradable y una SIC positiva, los usuarios de móviles omiten los anuncios al percibirlos como irritantes o interruptores. Los anuncios para móviles pueden costar relativamente menos, pero los anunciantes deben comprender la importancia de la apariencia de la fuente para minimizar el escepticismo de la publicidad móvil.

Los anunciantes pueden mejorar la participación del usuario en los dispositivos móviles abordando tanto la AS como el SIC en sus contenidos visuales. Hasta ahora no se había evaluado el efecto combinado de la AS y el SIC en el CE. Además, este estudio ha utilizado el mecanismo del SOR para examinar el engagement del cliente.

本研究的总体目标是评估源外观（SA）和自我影响者一致性（SIC）对移动广告视觉内容（VC）和客户参与（CE）之间关系的中介效应。

本研究采用定量方法来检验基于刺激-有机体-反应（SOR）理论提出的模型。研究采用了非概率目的性抽样技术, 通过自填问卷的方式向巴基斯坦移动用户收集数据。

我们的研究结果证明, 仅靠视觉内容并不能引起移动用户的参与。在这方面, 源外观是关键, 与自我影响者一致性相比, 源外观的重要性相对更高。此外, SA 和 SIC 对 CE 的串联中介效应表明, 有吸引力的信息源可能会诱发更高的 SIC 和随后的客户参与。

研究结果表明, 如果没有令人愉悦的SA和积极的SIC, 移动用户就会认为广告具有刺激性或干扰性, 从而跳过广告。移动广告的成本可能相对较低, 但广告商应了解广告源外观对减少移动广告怀疑的重要性。

广告商可以通过在视觉内容中同时考虑广告联盟和广告投放中心这两个因素, 提高用户在移动设备上的参与度。之前从未有人评估过 “SA “和 “SIC “对消费者参与度的综合影响。此外, 本研究还使用了 SOR 机制来考察客户参与度。