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Artificial intelligence (AI) is one of the most promising technologies of the post-pandemic era. Cloud computing technology can simplify the process of developing AI applications by offering a variety of services, including ready-to-use tools to train machine learning (ML) algorithms. However, comparing the vast amount of services offered by different providers and selecting a suitable cloud service can be a major challenge for many firms. Also in academia, suitable criteria to evaluate this type of service remain largely unclear. Therefore, the overall aim of this work has been to develop a framework to evaluate cloud-based ML services. We use Design Science Research as our methodology and conduct a hermeneutic literature review, a vendor analysis, as well as, expert interviews. Based on our research, we present a novel framework for the evaluation of cloud-based ML services consisting of six categories and 22 criteria that are operationalized with the help of various metrics. We believe that our results will help organizations by providing specific guidance on how to compare and select service providers from the vast amount of potential suppliers.
Purpose
Digital transformation of organizations has major implications for required skills and competencies of the workforce, both as a prerequisite for implementation, and, as a consequence of the transformation. The purpose of this study is to analyze required skills and competencies for digital transformation using the context of robotic process automation (RPA) as an example.
Design/methodology/approach
This study is based on an explorative, thematic coding analysis of 119 job advertisements related to RPA. The data was collected from major online job platforms, qualitatively coded and subsequently analyzed quantitatively.
Findings
The research highlights the general importance of specific skills and competencies for digital transformation and shows a gap between available skills and required skills. Moreover, it is concluded that reskilling the existing workforce might be difficult. Many emerging positions can be found in the consulting sector, which raises questions about the permanent vs temporary nature of the requirements, as well as the difficulty of acquiring the required knowledge.
Originality/value
This paper contributes to knowledge by providing new empirical findings and a novel perspective to the ongoing discussion of digital skills, employment effects and reskilling demands of the existing workforce owing to recent technological developments and automation in the overall context of digital transformation.
Knowledge-intensive organizations primarily rely on knowledge and expertise as key strategic resources. In light of economic, social, and health-related crises in recent years, such organizations increasingly need to operate in dynamic environments. However, examinations on dynamic capabilities specifically in knowledge-intensive organizations remain scarce. This is remarkable given the role that knowledge holds as an economic resource in developed countries. To provide an explanation of how knowledge-intensive organizations can prevail among competitors under dynamic conditions, the authors integrate two literature streams in a knowledge-intensive context: the knowledge-based view and the dynamic capabilities approach. The knowledge-based view focuses on the nature of organizational knowledge as a critical resource and illustrates specific properties of knowledge in contrast to traditional means of labor such as capital. The dynamic capabilities approach on the other hand is about a firm's ability to integrate, build, and reconfigure internal and external resources and can be drawn on to explain organizational success through adaptation to dynamic contexts. In this conceptual study, the authors propose a research model linking knowledge processes to organizational performance through two different paths: (1) Operational capabilities permit organizations to make their living in the present and refer to efficiency. (2) Dynamic capabilities allow organizations to change their resource base and, therefore, enable their long-term survival in dynamic environments by focusing on effectiveness. Additionally, the authors hypothesize a moderating effect of environmental dynamics on the relationship between dynamic capabilities and performance. The study offers a comprehensive overview on the interplay between dynamic capabilities and the knowledge-based view, offering valuable insights for both researchers and practitioners in the field.
Organizational agility may be an antidote against threats from volatile, uncertain, complex, or ambiguous corporate environments. While agility has been extensively examined in manufacturing enterprises, comparably less is known about agility in knowledge-intensive organizations. As results may not be transferable, there is still some confusion about how agility in knowledge-intensive organizations can be characterized, what factors facilitate its development, what its organizational effects are, and what environmental conditions favor these effects. This study closes these gaps by presenting a systematic literature review on agility in knowledge-intensive organizations. A systematic literature search led to a sample of 37 relevant papers for our review. Integrating the knowledge-based view and a dynamic capabilities perspective, we (1) present different relevant conceptualizations of organizational agility, (2) discuss relevant knowledge management-related as well as information technology-related capabilities that support the development of organizational agility, and (3) shed light on the moderating role of environmental conditions in enhancing organizational agility and its effect on organizational performance. This academic paper adds value to theory by synthesizing existing research on agility in knowledge-intensive organizations. It furthermore may serve as a map for closing research gaps by proposing an extensive agenda for future research. Our study expands existing literature reviews on agility with its specific focus on a knowledge-intensive context and its integration of the research streams of knowledge management capabilities as well as information technology capabilities. It integrates relevant organizational knowledge management practices and the use of knowledge management systems to ensure superior performance effects. Our study can serve as a base for future examinations of organizational agility by illustrating fruitful topics for further examination as well as open questions. It may also provide value to practitioners by showing what factors favor the development of agility in knowledge-intensive organizations and what organizational effects can be achieved under which conditions.
In the last few years, business firms have substantially invested into the artificial intelligence (AI) technology. However, according to several studies, a significant percentage of AI projects fail or do not deliver business value. Due to the specific characteristics of AI projects, the existing body of knowledge about success and failure of information systems (IS) projects in general may not be transferrable to the context of AI. Therefore, the objective of our research has been to identify factors that can lead to AI project failure. Based on interviews with AI experts, this article identifies and discusses 12 factors that can lead to project failure. The factors can be further classified into five categories: unrealistic expectations, use case related issues, organizational constraints, lack of key resources, and, technological issues. This research contributes to knowledge by providing new empirical data and synthesizing the results with related findings from prior studies. Our results have important managerial implications for firms that aim to adopt AI by helping the organizations to anticipate and actively manage risks in order to increase the chances of project success.