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The very nature of organizational life is transforming as collaborative technologies erase the prerequisite of co-location for collaboration. Using three example cases of which we have been a part, World Vision, the American Society for Association Executives, and Healthy Kids Healthy Schools, we illustrate how such technology is also augmenting the generative capacity of the Appreciative Inquiry (AI) Summit methodology. We then use the five principles of wikinomics that Tapscott and Williams (2010) identify as keys for organizational thrival into today’s digitally connected world: collaboration, openness, sharing, integrity, and interdependence, as a lens for examining how the virtually connected AI Summit is a whole-system change methodology that helps to promote these principles. The chapter concludes with lessons on integrating collaborative technology into summit designs and opportunities for future experiments in this domain.

With the increase in the adoption of artificial intelligence (AI)-based decision-making, organizations are facilitating human–AI collaboration. This collaboration can occur in a variety of configurations with the division of labor, with differences in the nature of interdependence being parallel or sequential, along with or without the presence of specialization. This study intends to explore the extent to which humans express comfort with different models human–AI collaboration.

Situational response surveys were adopted to identify configurations where humans experience the greatest trust, role clarity and preferred feedback style. Regression analysis was used to analyze the results.

Some configurations contribute to greater trust and role clarity with AI as a colleague. There is no configuration in which AI as a colleague produces lower trust than humans. At the same time, the human distrust in AI may be less about human vs AI and more about the division of labor in which human–AI work.

The study explores the extent to which humans express comfort with different models of an algorithm as partners. It focuses on work design and the division of labor between humans and AI. The finding of the study emphasizes the role of work design in human–AI collaboration. There is human–AI work design that should be avoided as they reduce trust. Organizations need to be cautious in considering the impact of design on building trust and gaining acceptance with technology.

The paper's originality lies in focusing on the design of collaboration rather than on performance of the team.

This study aims to empirically investigate the relationship between artificial intelligence (AI) in marketing (AIM) and business performance from the resource-based view (RBV) perspective.

A survey strategy was used in this study to collect data from 225 small and medium enterprises (SMEs) respondents who were on the registered list of the Ghana Enterprise Agency in the Eastern Region of Ghana. Structural equation modeling – path analysis was used to estimate the impact of AIM on the performance of SMEs.

The analyzed data shows that AIM has significant impact on the financial performance, customer performance, internal business process performance and learning and growth performance in the case of SMEs in Ghana. This study establishes the significance of AIM approach in achieving financial performance, customer performance, internal business process performance and learning and growth performance through the application of AIM determinants including, Internet of Things (IoT), collaborative decision-making systems (CDMS), virtual and augmented reality (VAR) and personalization.

Aside the aforementioned significance of this research study, this study has limitations. The sample size of this research study can be expanded to include SME respondents in other geographical areas that were not considered in this study. Future research studies should concentrate on how AIM can analyze customer communications and information such as posts on social media to develop future communications that may enhance customer engagement.

The practical implications comprise of two key items. First, this research study encourages SME owners and managers to develop an AIM method as a fundamental strategic goal in their pursuit to improve SME performance. Second, SME owners and managers should increasingly implement the four determinants of AIM indicated in this research study (i.e., IOT, CDMS, VAR and personalization) to develop essential resources for effective application of AIM to improve their performance.

The results of this study provide a strong support to RBV theory and the proposition that AIM and its determinants (i.e., IOT, CDMS, VAR and personalization) should be recognized as an essential strategic resource for improving the performance (i.e., financial performance, customer performance, internal business process performance and learning and growth performance) of SMEs. This study also contributes to the current body of knowledge on AIM and management, particularly in the context of an emerging economy.

Artificial intelligence (AI) has a significant impact on value co-creation (VCC). However, a study providing a comprehensive summary of the current state of the art and common ground of the two fields is missing. The current study aims to fill this gap by conceptualizing the role of AI in VCC and customer decision-making.

The study reviews literature on VCC and AI together, including a total of 108 articles. To bring the literature together, the authors adopted the antecedents-mediators-outcomes framework and narrative approach that helped them develop a framework by integrating the antecedents, mediators and outcomes of AI-facilitated VCC. Furthermore, the authors also operationalized existing literature to facilitate an understanding of the role of AI in customer decision-making.

The study, in addition to identifying the common theoretical grounds of VCC and AI (human behavior, cognition and social interactions), operationalizes AI functionality, its characteristics and customer characteristics as the antecedents of AI-facilitated VCC. Moreover, based on literature, on the continuum of low-to-high involvement, four types of decision-making were identified as mediator of the relationship between AI characteristics, customer characteristics and VCC. Additionally, the authors found different categorizations of AI in literature as archetypes to support various forms of VCC.

The study contributes to the literature of VCC and AI by construing a comprehensive framework for analyzing AI's impact on VCC, envisioning customer–AI interaction as continual exchange of advantages in which characteristics of AI and customers play a critical role in customer decision-making and shaping VCC.

The purpose of the paper is to note that customers are not necessarily human and to figure out how best to serve artificial intelligence (AI) customers. The authors also propose several major research streams, as examples, to help launch research on AI customers and how to serve them.

The current paper is a conceptual one that draws upon research from many areas to support the ideas proposed.

AI customer are proliferating. AI as customers can augment or replace human customers and can be the customer itself. Service providers may also be AI, which means that both humans serving AI customers and AI serving AI customers are relevant here. The authors show that even truly autonomous AI customers are likely to be more common in the future. The authors conclude that reverse engineering will probably not be successful in understanding AI customers and that an approach similar to how we research human consumer behavior is likely to be more useful.

Virtually, the entire literature on customers and how to serve them assumes that customers are human. With the rapid advancement of AI, purchase decisions are increasingly made by AI, suggesting that it is now important and necessary to consider the possibility of AI customers and how best to serve them. This paper opens the door for such research.

Most studies have focused on the impact of the application of AI on management attributes, management decisions and management ethics. However, how job demand and job control in the context of AI collaboration determine employees' learning process and learning behaviors, as well as how AI collaboration moderates employees' learning process and learning behaviors, remains unknown. To answer these questions, the authors adopted a Job Demand-Control (JDC) model to explore the influencing factors of employee's individual learning behavior.

This study used questionnaire survey in organizations using AI to collect data. Partial least squares (PLS) predict algorithm and SPSS were used to test the hypotheses.

Job demand and job control positively influence self-efficacy, self-efficacy positively influences learning goal orientation and learning goal orientation positively influences learning behavior. Learning goal orientation plays a mediating role between self-efficacy and learning behavior. Meanwhile, collaboration with AI positively moderates the impact of employees' job demand on self-efficacy and the impact of self-efficacy on learning behavior.

This study introduces self-efficacy as the outcome of JDC model, demonstrates the mediating role of learning goal orientation and introduces collaborative factors related to artificial intelligence. This study further enriches the theoretical system of human–AI interaction and expands the content of organizational learning theory.

This paper aims to offer an approach to cyborg composing with artificial intelligence (AI). The author posits that the hybridity of the cyborg, which amalgamates human and artificial elements, invites a cascade of creative and emancipatory possibilities. The author critically examines the biases embedded in AI systems while gesturing toward the generative potential of AI–human entanglements. Drawing on Bakhtinian theories of dialogism, the author contends that crafting found poetry with AI could inspire writers to problematize the ideologies embedded into the corpus while teasing apart its elisions or contradictions, sparking new forms of expression at the interface of the organic and the artificial.

To illustrate this approach to human–AI composing, the author shares a found poem that she wrote using ChatGPT alongside her reflection on the poem. The author reflects on her positionality as well as the positionality of her artificial interlocutor, interrogating the notion of subjectivity in relation to Bakhtinian dialogism and multivocality.

Weaving tales of resilience in harmony or tension with AI could unravel threads of possibility as human writers enrich, deepen or complicate AI-generated texts. By composing with AI, writers can resist closure, infiltrate illusions of objectivity and “speak back” to AI and the dominant voices replicated in its systems.

By encouraging students to critically engage with, question and complicate AI-generated texts, one can open avenues for alternative ways of thinking and writing, inspiring students to imagine and compose speculative futures. Ultimately, in animating assemblages of the organic and the artificial, one can invite transformative possibilities of being and becoming.

Recent socio-economic trends have made Artificial intelligence (AI) a vital institutional force driving development among countries with optimal opportunities and costs. Among researchers in this domain, the benefit of AI is highly espoused, having been underexplored in Africa. However, the outbreak of the COVID-19 pandemic has highlighted the need to strengthen the education sector, given that many schools have migrated their teaching and learning online. While face-to-face teaching was the norm, the transition to online teaching has brought about the need to rethink the use of Information Technology to strengthen teaching and learning. To proffer solutions for the implementation of AI in Africa, there is the need to understand the challenges. Therefore, in this chapter, we explore the possible challenges that hinder the implementation of AI in Africa. Further, we propose solutions for the implementation of AI in the education sector, especially in this era of the COVID-19 pandemic. The solutions stem from rethinking the role of AI in the education sector. Finally, a conceptual framework that synthesises the problems and the proposed solution is developed. We envision that the proffered solutions can mitigate the deepening misconceptions and challenges bedevilling AI implementation in Africa.

Appreciative Inquiry (AI) has gained prominence as an organizational development approach. For over 15 years, it has had varied use in higher education research as a methodology and as a collection of methods. Perhaps the most consistently used, yet most criticized, aspect of AI is the positive stance that its adherents adopt. In this chapter, we survey the prevalence and use of AI, both in the wider literature and in higher education research. We offer our own case study to illustrate the practicalities of employing it and discuss our findings. We suggest that educational researchers are overlooking relevant AI research published within other disciplines; that our own and other case stories can provide guidance for the use of AI in academic contexts; and that AI’s collaborative and positive standpoint has potential as a research methodology influencing policy.

This study aims to develop the alleviating bullwhip effects framework (ABEF) replenishment rules, and bullwhip, inventory fluctuations and customer service fulfilment rates were examined. In addition, automated smoothing and replenishment rules can alleviate supply chain bullwhip effects. This study aims to understand the current artificial intelligence (AI) implementation practice in alleviating bullwhip effects in supply chain management. This study aimed to develop a system for writing reviews using a systematic approach.

The methodology for the present study consists of three parts: Part 1 deals with the systematic review process. In Part 2, the study applies social network analysis (SNA) to the fourth phase of the systematic review process. In Part 3, the author discusses developing research clusters to analyse the research state more granularly. Systematic literature reviews synthesize scientific evidence through repeatable, transparent and rigorous procedures. By using this approach, you can better interpret and understand the data. The author used two databases (EBSCO and World of Science) for unbiased analysis. In addition, systematic reviews follow preferred reporting items for systematic reviews and meta-analyses.

The study uses UCINET6 software to analyse the data. The study found that specific topics received high centrality (more attention) from scholars when it came to the study topic. Contrary to this, others experienced low centrality scores when using NETDRAW visualization graphs and dynamic capability clusters. Comprehensive analyses are used for the study’s comparison of clusters.

This study used a journal publication as the only source of information. Peer-reviewed journal papers were eliminated for their lack of rigorousness in evaluating the state of practice. This paper discusses the bullwhip effect of digital technology on supply chain management. Considering the increasing use of “AI” in their publications, other publications dealing with sensor integration could also have been excluded. To discuss the top five and bottom five topics, the author used magazines and tables.

The study explores the practical implications of smoothing the bullwhip effect through AI systems, collaboration, leadership and digital skills. Artificial intelligence is rapidly becoming a preferred tool in the supply chain, so management must understand the opportunities and challenges associated with its implementation. Furthermore, managers should consider how AI can influence supply chain collaboration concerning trust and forecasting to smooth the bullwhip effect.

Digital leadership and addressing the digital skills gap are also essential for the success of AI systems. According to the framework, it is necessary to balance AI performance and accountability. As a result of the framework and structured management approach, the author can examine the implications of AI along the supply chain.

The study uses a systematic literature review based on SNA to analyse how AI can alleviate the bullwhip effects of supply chain disruption and identify the focused and the most important AI topics related to the bullwhip phenomena. SNA uses qualitative and quantitative methodologies to identify research trends, strengths, gaps and future directions for research. Salient topics for reviewing papers were identified. Centrality metrics were used to analyse the contemporary topic’s importance, including degree, betweenness and eigenvector centrality. ABEF is presented in the study.