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The paper discusses the rationale for a widespread reliance on Codes of Conduct (CoC) in European crowdfunding through the lenses of economic theories of self-regulation. By analysing the institutional design of CoCs in crowdfunding, the paper illustrates the differences in their regulatory context, inclusiveness, monitoring and enforcement. It offers the first systematic overview of substantial rules of CoCs in crowdfunding.

A comparative case study of nine CoCs in Europe is used to illustrate differences in their institutional design and discern the economic purpose of the CoC.

The institutional design of different CoCs in Europe mainly supports voluntary theories of self-regulation. In particular, the theory of reputation commons has the most explanatory power. The substantial rules of CoC in different markets show the potential sources of market failure through the perspectives of platforms.

CoCs appear in various regulatory, cultural, and industry contexts of different countries. Some of the institutional design features of CoC might be a result of these characteristics.

Crowdfunding associations wishing to develop their own CoC may learn from a comparative overview of key provisions.

For governments in Europe, contemplating creating or revising bespoke crowdfunding regimes, the paper identifies areas where crowdfunding platforms perceive market failure.

This paper is the first systematic study of self-regulatory institutions in European crowdfunding. The paper employs a theoretical framework for the analysis of self-regulation in crowdfunding and provides a comparison of a regulatory context, inclusiveness, monitoring and enforcement of different CoCs in Europe.

Drawing from legitimacy and institutional entrepreneurship theory, this study assesses the naming patterns of entrepreneurial firms in the US biotechnology industry.

The authors use a mixed-methods design of content analysis and regression to analyze a sample of 441 entrepreneurial biotechnology firms, for which data were obtained from Net Advantage. The authors track changes to the proportion of firms with naming attributes, such as name length and type of name. The authors also examine variability in those characteristics during the industry's evolution, comparing freestanding to acquired start-ups.

Start-ups select names that are longer, more descriptive, begin with rare sounds or hard plosives and have stronger discipline- or technology-specific links during nascent years of the industry. As the industry evolves, entrepreneurs are more likely to select names that are shorter, more abstract, begin with hard plosives and have stronger industry-specific links. The naming patterns of freestanding and acquired companies differ, and companies that conform to industry pressures tend to remain independent.

Unlike extant studies that assess established industries, the current study identifies shifting trends in the naming patterns of entrepreneurial firms in an emerging industry. By focusing on start-ups, the authors expand research on organizational naming practices, which focuses traditionally on name choices and name change patterns of incumbents. By using marketing and linguistics methods when analyzing organizational name attributes, naming patterns in these attributes are identified, including name length, name type, starting letter of the name and link to the industry.

The Overall Equipment Effectiveness (OEE) is considered a standard for measuring equipment productivity in terms of efficiency. Still, Artificial Intelligence solutions are rarely used for analyzing OEE results and identifying corrective actions. Therefore, the approach proposed in this paper aims to provide a new rule-based Machine Learning (ML) framework for OEE enhancement and the selection of improvement actions.

Association Rules (ARs) are used as a rule-based ML method for extracting knowledge from huge data. First, the dominant loss class is identified and traditional methodologies are used with ARs for anomaly classification and prioritization. Once selected priority anomalies, a detailed analysis is conducted to investigate their influence on the OEE loss factors using ARs and Network Analysis (NA). Then, a Deming Cycle is used as a roadmap for applying the proposed methodology, testing and implementing proactive actions by monitoring the OEE variation.

The method proposed in this work has also been tested in an automotive company for framework validation and impact measuring. In particular, results highlighted that the rule-based ML methodology for OEE improvement addressed seven anomalies within a year through appropriate proactive actions: on average, each action has ensured an OEE gain of 5.4%.

The originality is related to the dual application of association rules in two different ways for extracting knowledge from the overall OEE. In particular, the co-occurrences of priority anomalies and their impact on asset Availability, Performance and Quality are investigated.

This study aims to examine the relationships between the rules that a cooperative membership decides upon and members' motives for action. It considers individual self-interest in relation with motives that are consistent with the values of cooperation.

This paper comprises two parts. The first is theoretical and discusses cooperative governance's features in the context of individual motives. The second part is empirical and based on survey data from Italian multistakeholder, worker-run social cooperatives. It uses cross-sectional data gathered from 4,134 workers and 310 managers in 310 cooperatives in Italy to provide evidence of rules and individual motives. Regression analysis confirms the existence of a linkage between individual self-interest and motives.

Rules mainly, but not exclusively, play an enabling function, which implies responding to both nonmonetary and monetary individual motives. With greater articulation within institutions – through the definition of multiple rights for accessing decision-making – the authors expect increases in individual capabilities to match motives with specific organizational rules in pursuit of consistent ends. This is confirmed by the association that the authors found between individual motives and commitment.

The authors’ illustration is limited to one specific type of cooperative, the social cooperative, in which prosocial motives are expected to be stronger than in other cooperative forms, although one could say that all cooperative models emphasize procommunity and prosocial aims. Data are cross-sectional and do not allow for the identification of causality, only of statistical relations' strength.

The continuous scrutiny and adaptation of motives and means imply that cooperators communicate and engage in a learning process.

While the institutional spheres that support investor-owned organizations and self-interested profit-maximizing behavior have been analyzed, a framework that accommodates personal control rights and a richer view of individual motives is lacking. The value added from the paper is to suggest one.

Given the limited studies that have started to focus on contexts where integrated reporting (IR) is voluntarily adopted, this paper aims to explore the moderating role of institutional characteristics on the association between voluntary report release and analyst forecast accuracy.

This study uses a quantitative empirical research method grounded on voluntary disclosure theory to provide empirical evidence on an international sample of companies choosing to release integrated reports. Preliminarily, a cluster analysis is used to group countries according to institutional patterns. Multivariate analyses detect the associations between report release choice and analysts’ forecast accuracy across clusters. Multiple econometric approaches are used to address the endogeneity concerns.

IR release is not informative for the market unless considering systematic variations across different institutional settings. Analysts’ forecast is more accurate for IR adopters located in strong institutional enforcement settings than for all the other companies. In the strong institutional setting that is also characterized by a pluralistic society, IR release benefits for the market are conditioned by the fact that the choice to release IR depends on environmental, governance and social disclosure-based managers remuneration and disclosure requirements. In weak institutional settings, IR release is not beneficial for the forecast accuracy.

Academics and practitioners can gain understanding of the usefulness of voluntary IR across different institutional settings.

The study advances the understanding of the IR’s informativeness, overcoming the common dichotomous distinctions between strong and weak institutional settings.

China is the world’s largest user market for digital technologies and experiencing unprecedented rates of rural-urban migration set to create the world’s first “urban billion”. This is an important context for studying nuanced adoption behaviours that define a digital divide. Large-scale studies are required to determine what behaviours exist in such populations, but can offer limited ability to draw inferences about why. The purpose of this paper is to report a large-scale study inside China that probes a nuanced “digital divide” behaviour: consumer demographics indicating ability to pay by electronic means but behaviour suggesting lack of willingness to do so, and extends current demographics to help explain this.

The authors report trans-national access to commercial “Big Data” inside China capturing the demographics and consumption of millions of consumers across a wide range of physical and digital market channels. Focusing on one urban location we combine traditional demographics with a new measure that reflecting migration: “Distance from Home”, and use data-mining techniques to develop a model that predicts use behaviour.

Use behaviour is predictable. Most use is explained by value of the transaction. “Distance from Home” is more predictive of technology use than traditional demographics.

Results suggest traditional demographics are insufficient to explain “why” use/non-use occurs and hence an insufficient basis to formulate and target government policy.

The authors understand this to be the first large-scale trans-national study of use/non-use of digital channels within China, and the first study of the impact of distance on ICT adoption.