Learning from Workaround Practices: the Challenge of Enterprise System Implementations in Multinational Corporations

Implementing Artificial Intelligence in Traditional B2B Marketing Practices: An Activity Theory Perspective

Anecdotal evidence suggests that artificial intelligence (AI) technologies are highly effective in digital marketing and rapidly growing in popularity in the context of business-to-business (B2B) marketing. Yet empirical research on AI-powered B2B marketing, and particularly on the socio-technical aspects of its use, is sparse. This study uses Activity Theory (AT) as a theoretical lens to examine AI-powered B2B marketing as a collective activity system, and to illuminate the contradictions that emerge when adopting and implementing AI into traditional B2B marketing practices. AT is appropriate in the context of this study, as it shows how contradictions act as a motor for change and lead to transformational changes, rather than viewing tensions as a threat to prematurely abandon the adoption and implementation of AI in B2B marketing. Based on eighteen interviews with industry and academic experts, the study identifies contradictions with which marketing researchers and practitioners must contend. We show that these contradictions can be culturally or politically challenging to confront, and even when resolved, can have both intended and unintended consequences.

'Bringing the Covert into the Open': A Case Study on Technology Appropriation and Continuous Improvement

As end-users, employees appropriate technologies. Technology appropriation is generally conceived as a covert phenomenon. In particular, alternative ways and new purposes for which employees deploy technologies tend to remain hidden. Therefore, the potential of technologies as a source of organizational improvements may remain undisclosed. Continuous improvement (CI) programs, in contrast, are explicitly oriented at disclosing organizational improvements. In essence, CI programs encourage employees to openly discuss how to improve their work practices. Such continuous movements towards novel, often better, ways of working may be perfectly suited to bring the covert nature of technology appropriation into the open. Based on a case study on a personal digital assistant (PDA) in a Belgian nursing home with such a CI program in place, we document and analyze to what extent and why functionalities of the PDA were discussed and further developed. We distinguish between the functionalities that, upon implementation, intended to improve particular work practices, and those that surfaced after the technology had been introduced. To conclude, we point at employees’ perceived usefulness of their work practices and their willingness to improve these, rather than only the technology itself, to further the debate on technology appropriation.

Stagewise Overview of Issues Influencing Organizational Technology Adoption and Use

This paper provides a stagewise overview of the important issues that play a role in technology adoption and use in organizations. In the current literature, there is a lack of consistency and clarity about the different stages of the technology adoption process, the important issues at each stage, and the differentiation between antecedents, after-effects, enablers, and barriers to technology adoption. This paper collected the relevant issues in technology adoption and use, mentioned dispersedly and under various terminologies, in the recent literature. The qualitative literature review was followed by thematic analysis of the data. The resulting themes were organized into a thematic map depicting three stages of the technology adoption process: pre-change, change, and post-change. The relevant themes and subthemes at each stage were identified and their significance discussed. The themes at each stage are antecedents to the next stage. All the themes of the pre-change and change stages are neutral, but the way they are managed and executed makes them enablers or barriers in effect. The thematic map is a continuous cycle where every round of technology adoption provides input for the subsequent rounds. Based on how themes have been addressed and executed in practice, they can either enhance or impair the subsequent technology adoption. This thematic map can be used as a qualitative framework by academics and practitioners in the field to evaluate technological changes.

Deep Learning for Enterprise Systems Implementation Lifecycle Challenges: Research Directions

Transforming the state-of-the-art definition and anatomy of enterprise systems (ESs) seems to some academics and practitioners as an unavoidable destiny. Value depletion lead by early retirement and/or replacement of ESs solutions has been a constant throughout the past decade. That did drive an enormous amount of research that works on addressing the problems leading to the resource drain. The resource waste had persisted throughout the ESs implementation lifecycle phases and dimensions especially post-live phases; leading to depleting the value of the social and technical dimensions of the lifecycle. Parallel to this research stream, the momentum gained by deep learning (DL) algorithms and platforms has been exponentially growing to fuel the advancements toward artificial intelligence and automated augmentation. Correspondingly, this paper is set out to present five key research directions through which DL would take part as a contributor towards the transformation of the ESs state-of-the-art. The paper reviews the ESs implementation lifecycle challenges and the intersection with DL research conducted on ESs by analyzing and synthesizing key basket journals (list of the Association of Information Systems). The paper also presents results from several experiments showcasing the effectiveness of DL in adding a level of augmentation to ESs by analyzing a large set of data extracted from the Atlassian Jira Software Issue Tracking System across different ecosystems. The paper then concludes by presenting the research directions and discussing socio-technical research courses that work on key frontiers identified within this scholarly work.

Understanding adaptive information seeking in the context of microblogging from the cognitive switching perspective

Drawing on adaptive structuration theory and cognitive switching theory, this study develops a research model exploring the effects of cognitive switching stimuli on adaptive information seeking and the moderating effects of information need and personal innovativeness in information technologies. Data collected from microblogging users were used to test the model. The findings suggest that other people’s use, discrepancies, and deliberate initiatives each have significant positive effects on trying new features to seek information. Other people’s use which essentially reflects the nature of learning from observing other people is the most important determinant. Meanwhile, information need and personal innovativeness in IT each positively moderate the effect of other people’s use on trying new features to seek information. This study contributes to theory by examining adaptive information seeking in the context of microblogging which has been largely overlooked by prior literature. The findings and more implications for theory and practice are discussed.