While the topic of artificial intelligence (AI) in multinational enterprises has been receiving attention for some time, small and medium enterprises (SMEs) have recently begun to recognize the potential of this new technology. However, the focus of previous research and AI applications has therefore mostly been on large enterprises. This poses a particular issue, as the vastly different starting conditions of various company sizes, such as data availability, play a central role in the context of AI. For this reason, our systematic literature review, based on the PRISMA protocol, consolidates the state of the art of AI with an explicit focus on SMEs and highlights the perceived challenges regarding implementation in this company size. This allowed us to identify various business activities that have been scarcely considered. Simultaneously, it led to the discovery of a total of 27 different challenges perceived by SMEs in the adoption of AI. This enables SMEs to apply the identified challenges to their own AI projects in advance, preventing the oversight of any potential obstacles or risks. The lack of knowledge, costs, and inadequate infrastructure are perceived as the most common barriers to implementation, addressing social, economic, and technological aspects in particular. This illustrates the need for a wide range of support for SMEs regarding an AI introduction, which covers various subject areas, like funding and advice, and differentiates between company sizes.

Purpose: The purpose of this paper is to evaluate the nature and characteristics of crowd logistics business models. Using this evaluation, a new concept for a sustainable implementation of crowd logistics services is proposed.Design/methodology/approach: The Design Science process was followed to develop the proposed crowd logistics business model concept. The data are derived from expert interviews and a document-based data analysis of 13 companies.Findings: Four relevant steps that companies should follow to implement sustainable crowd logistics services are identified. Open research questions are also identified and guide five research tasks, which may lead to a greater understanding of this emerging field.Research limitations/implications: The present research is based on data from companies operating in Germany. The holistic approach gives a broad overview but lacks detailed descriptions.Practical implications: Managers can use the four steps and the crowd logistics business model concept to plan future activities (e.g. new service provision). These steps increase the understanding, awareness and knowledge of opportunities and risks of specific crowd logistics services.Social implications: This paper provides initial insights into social changes in terms of drivers for the use of crowd logistics services. However, further research is needed to capture the social implications in detail.Originality/value: Crowd logistics is an emerging concept, and this paper is one of the first dealing with this topic generally and the first providing an analysis of crowd logistics business models. The developed concept includes implications for practice in the forms of common, and best practices, and science in the form of open research questions and tasks. Overall, the present research provides new insights into this emerging topic.

Background: Data security issues still constitute the main reason for the sluggish dissemination of electronic health records (EHRs). Given that blockchain technology offers the possibility to verify transactions through a decentralized network, it may serve as a solution to secure health-related data. Therefore, we have identified stakeholder-specific requirements and propose a blockchain-based architecture for EHRs, while referring to the already existing scientific discussions on the potential of blockchain for use in EHRs. Objective: This study aimed to introduce blockchain technology for EHRs, based on identifying stakeholders and systematically eliciting their requirements, and to discuss the key benefits (KBs) and key challenges (KCs) of blockchain technology in the context of EHRs. Methods: The blockchain-based architecture was developed in the framework of the design science research paradigm. The requirements were identified using a structured literature review and interviews with nine health care experts. Subsequently, the proposed architecture was evaluated using 4 workshops with 15 participants. Results: We identified three major EHR stakeholder groups and 34 respective requirements. On this basis, we developed a five-layer architecture. The subsequent evaluation of the artifact was followed by the discussion of 12 KBs and 12 KCs of a blockchain-based architecture for EHRs. To address the KCs, we derived five recommendations for action for science and practice. Conclusions: Our findings indicate that blockchain technology offers considerable potential to advance EHRs. Improvements to currently available EHR solutions are expected, for instance, in the areas of data security, traceability, and automation by smart contracts. Future research could examine the patient’s acceptance of blockchain-based EHRs and cost-benefit analyses.

Purpose: In recent years, the rise of big data has led to an obvious shift in the competence profile expected from the controller and management accountant (MA). Among others, business analytics competences and information technology skills are considered a “must have” capability for the controlling and MA profession. As it still remains unclear if these requirements can be fulfilled by today’s employees, the purpose of this study is to examine the supply of business analytics competences in the current competence profiles of controlling professionals in an attempt to answer the question whether or not a skills gap exists.Design/methodology/approach: Based on a set of 2,331 member profiles of German controlling professionals extracted from the business social network XING, a text analytics approach is conducted to discover patterns out of the semi-structured data. In doing so, the second purpose of this study is to encourage researchers and practitioners to integrate and advance big data analytics as a method of inquiry into their research process.Findings: Apart from the mediating role of gender, company size and other variables, the results indicate that the current competence profiles of the controller do not comply with the recent requirements towards business analytics competences. However, the answer to the question whether a skills gap exist must be made cautiously by taking into account the specific organizational context such as level of IT adoption or the degree of job specialization.Research limitations/implications: Guided by the resource-based view of the firm, organizational theory and social cognitive theory, an explanatory model is developed that helps to explain the apparent skills gap, and thus, to enhance the understanding towards the rationales behind the observed findings. One major limitation to be mentioned is that the data sample integrated into this study is restricted to member profiles of German controlling professionals from foremost large companies.Originality/value: The insights provided in this study extend the ongoing debate in accounting literature and business media on the skills changes of the controlling and MA profession in the big data era. The originality of this study lies in its explicit attempt to integrate recent advances in data analytics to explore the self-reported competence supplies of controlling professionals based on a comprehensive set of semi-structured data. A theoretically founded explanatory model is proposed that integrates empirically validated findings from extant research across various disciplines.