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In this chapter it is argued that there are notable parallels between the different strands within ethics on the one hand, and the discipline of accounting on the other hand that, in teaching, can be drawn upon to enhance students’ understanding of the latter. Accounting, part of economics, draws on utilitarian ethics, but not solely so. Accounting, in addition, draws also on deontological and communitarian strands in ethics. The chapter suggests that the teaching of accounting - especially to non-economists - would benefit substantially from highlighting and developing these parallels with ethics.

If employees have access to additional relevant knowledge and information, they can be more innovative, which is a reason for many firms to ally. Key in an alliance is the sharing of existing and creating of new knowledge, but transferring knowledge inside an alliance is not obvious or easy. Knowledge exchanged within an alliance must be perceived as relevant in order for it to be used. If individuals in an alliance can readily perceive knowledge to be relevant, the right knowledge is transferred and is transferred in an efficient manner. Building on stickiness of knowledge and knowledge relevance theory, we examine the relationship between an individual's job experience as well as intrinsic motivation on the one hand, on their perception of the relevance of others' knowledge on the other hand. These relationships are positively moderated by an individual's favourable position and connection strength in the network of individuals engaged in the alliance, respectively. We find a weak but insignificant negative relationship between job experience and perceived knowledge relevance. Being well-positioned in a network brings out the positive effect of job experience, as expected, whereas connection strength does not further enhance the positive effect of intrinsic motivation.

An important way for the agrifood industry to continue to feed a growing world population as well as reduce its environmental impact is to extensively adopt Information Technologies that will “datafy” the industry. While the promises of datafication are high, the realization is lagging behind. We identify and discuss from an institutional economics point of view three related issues that help explain needs run behind realization: supply of data is separated from use of data; data quality, interoperability and standardization; and data completeness.

In recent years, the rapid growth of user-generated content has led to much research evaluating the patterns of online information exchange. These studies demonstrate that online communities are valuable data sources which provide rich, longitudinal data that would otherwise be difficult, if not impossible to access. Given the increased research interest, mining and analysing online social networks has become an important research domain, encompassing a variety of approaches. To analyse the large number of observations commonly found in online communities, we propose to first mine the data using a so-called Webscraper and then combine Social Network Analysis (SNA) with Sentiment Analysis to explore both content and relationships. The hands-on approach described in this article is targeted at researchers without a background in technical disciplines. Instead of focusing on some of the specific algorithms that facilitate the mining and analysis of online data, we describe how to use and combine out-of-the-box solutions to collect and analyse the online network data. Moreover, we document the steps taken and present important lessons learnt throughout the process of collecting and analysing data from an online health community with 108,569 registered users who contributed to 197,980 discussions with a total of 484,250 replies. In sum, our method proposes to: • Extract all relevant data from an openly accessible online community using a Webscraper. • Determine and visualise the relationships between users and the properties of the social network as a whole using Social Network Analysis. • Conduct Sentiment Analysis to detect the emotional tone of the online contributions, and to possibly infer further variables from the text such as the personality characteristics of users.

Small and medium-sized enterprises (SMEs) are pushed to introduce new technologies due to different requirements and changes in the business setting. The SMEs' transformation to exploit new technologies is challenging given their lack of resources and the complexity of technological transformation, which encompasses technology assimilation and business model innovation (BMI). Although studies recognize the complementarity of technology assimilation and BMI for benefiting from technology, the literature is fragmented, and the technological transformation phenomenon remains abstract, especially in the SMEs' context. To improve understanding of technological transformation in SMEs, a systematic literature review was performed on 165 peer-reviewed papers published from 1999 to 2022, building upon BMI and technology assimilation constructs. The descriptive analysis outlines the field's evolution in terms of research and technological trends. The content analysis shows that: most papers focus on factors; the literature falls short of providing theoretical conceptualization and guidelines for the technological transformation process; only a few studies are dedicated to assessing the outcomes of technological transformation in SMEs; and the papers suggest that SMEs present a low transformation level. Finally, we inductively built a framework for technological transformation and suggest five research avenues.

Formal institutions, most prominently laws, impact society and also the economy by changing actors’ behavior. How that impact can be conceptualized is in need of more elaboration. Klammer & Scorsone’s (2022) recent publication opens avenues on how to understand the ways in which institutions can over-instruct, under-instruct and ambiguously instruct the behavior of actors. Sources of and direction for institutional change can then be better understood.

Purpose: All firms' business models are based on their interdependencies with other parties in their ecosystems. The Internet of Things (IoT) is beginning to fundamentally disrupt the agri-food industry, forcing the ecosystem to change. When an ecosystem is transforming, the interdependencies among its actors can create friction. Technology providers and core actors should consider these interdependencies as they update their strategies for value creation and capture. The purpose of the present research is to consider what it might take for agri-food firms to capitalize on these interdependencies by moving from traditional business models to business models based on collaboration and open innovation. Design/methodology/approach: The present paper draws on data from four online focus groups that we created to discuss how to co-create the business models agri-food firms need in a constantly changing environment. The paper presents an application of phenomenon-driven research (PDR), an engaged methodology. The study method enables novel pathways to develop and implement innovative solutions. This study draws on the interaction of theory and practice and involves multiple stakeholders with varying roles in the agri-food ecosystem. Findings: The authors found that any open innovation setup in agri-food needs to constantly reconfigure itself to balance the needs of farmers and the needs of the market. This interplay can only support the IoT-enabled ecosystem if continuous interaction and negotiation occur among various stakeholders of the food system. When the farmers' needs and the market's needs are aligned, the space for developing a collaborative and open business model is prepared. Originality/value: The insights gained from this study inspire action and commitment to common goals when developing collaboration-based business models (CBMs). The paper offers insights for players in the agri-food industry who are considering CBMs in the course of digital transformati