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Recent labor market research has called into question whether social capital effects are causal, or are spuriously due to the influence of social homophily. This essay adopts the demand-side perspective of organizations to examine the causal status of social capital. In contrast with supply-side approaches, we argue that homophily is a key mechanism by which organizations derive social capital. We develop an approach to bolster inferences about the causal status of social capital, and illustrate these ideas using data from a retail bank.

Examines the issue of explanation in causal networks and expert systems through the perspective of solution reconstruction. Such reconstruction is complementary to the notion of system reconstruction as studied in systems theory. Compares explanations in causal networks with explanations in expert systems. Proposes the general concept of viewing explanation as user‐oriented solution‐reconstruction. In other words, the explanation is a solution deconstructed not only in terms of system variables, but also in terms of factors involving the user’s understanding of the problem solved by the system.

In continuous improvement (CI) projects, cause-and-effect diagrams are used to qualitatively express the relationship between a given problem and its root causes. However, when data collection activities are limited, and advanced statistical analyses are not possible, practitioners need to understand causal relationships. The paper aims to discuss these issues.

In this research, the authors present a framework that combines cause-and-effect diagrams with Bayesian belief networks (BBNs) to estimate causal relationships in instances where formal data collection/analysis activities are too costly or impractical. Specifically, the authors use cause-and-effect diagrams to create causal networks, and leverage elicitation methods to estimate the likelihood of risk scenarios by means of computer-based simulation.

This framework enables CI practitioners to leverage qualitative data and expertise to conduct in-depth statistical analysis in the event that data collection activities cannot be fully executed. Furthermore, this allows CI practitioners to identify critical root causes of a given problem under investigation before generating solutions.

This is the first framework that translates qualitative insights from a cause-and-effect diagram into a closed-form relationship between inputs and outputs by means of BBN models, simulation and regression.

At the level of a cognitive schema, a business model is a mental map of a firm’s value-creating, value-delivering, and value-capturing activities and the linkages between them. An important question in the study of business models as cognitive schemas is whether and how schemas differ across industry actors and whether the differences are connected to the variation observed in actual business models in the industry. This chapter examines, in particular, the ways in which business model schemas of industry insiders differ from those of industry outsiders. Using data from interviews with chief executive officers (CEOs) of 30 legal-tech firms, we graphically construct and analyze the CEOs’ schemas of important causal interdependencies between their firms’ activities. The analysis shows systematic differences between insiders and outsider CEOs’ schemas. We theorize that these differences underlie insider and outsider CEOs’ distinct approaches to opportunity recognition, expertise perception, and value framing, and have consequences for actual business model evolution in the industry.

The purpose of this paper is to highlight the ontological implications of combining network and system ontology to conceptualize industrial networks as the empirical manifestations of complex adaptive economic systems.

This paper contributes with a systematic discussion on how network and system ontology can be combined to produce better understandings of business networks. It also provides a review of the state-of-the art research literature on the topic as a starting point for the discussion.

Findings indicate that networks may be enclosed in each other constituting sub- and supra-networks comprising increasing complexity. In these cases, sub-networks that are black-boxed can be seen as entities in themselves producing inputs and outputs to the supra-network. Networks, once they become black-boxed, can assume the functions of generative mechanisms within a wider supra-network.

This research is conceptual in nature and needs to be complemented with empirical research. In addition, the literature review used one database complemented with papers from the IMP journal. A wider search could reveal additional research that can be of relevance for the development of the field.

This paper addresses the ontological and methodological issues arising from a mixed system and network ontology. These issues are commonly ignored or dealt with indirectly in extant literature. For any accumulation of knowledge in the field to be possible, the explication of a mixed ontology is important as it have conceptual and methodological consequences. Adopting such a mixed ontological position provides an ontology in line with empirical research of business practice.

This paper seeks to investigate the role of social capital in facilitating the entry of new business ventures into service networks.

The empirical work is undertaken via case study‐based research, featuring three service businesses, each entering and operating in a different marketplace.

Results show that new service businesses are not necessarily able to draw on existing social capital in order to enter a business network and build relationships with potential customers and suppliers.

Future empirical work should re‐examine the distinctions between the role and nature of social capital for new service businesses.

The paper suggests how the new service entrepreneur might invest personal resources in networking to initiate relationships and build a network of customers and suppliers.

The paper presents the little researched area of networking and relationship initiation as a means of developing social capital for new service businesses.

The purpose of this paper is to explore the logics that expert entrepreneurs use when faced with a critical incident threat.

Attempts have been made to define “entrepreneurial logic”. This paper is influenced by Sarasvathy’s work on high-performance entrepreneurs, which finds that when faced with uncertainty entrepreneurs employ unconventional logic, and encompasses later research acknowledging social contexts where entrepreneurs operate. A typology of decision-making logics is developed, taking into account the situation of crisis. Seven expert entrepreneurs who faced crisis and, despite this, are still successfully operating businesses were interviewed. The paper develops a critical incidents methodology.

Experienced entrepreneurs were found to tend towards causal logic when “the stakes were high” and the decision may affect the survival of their business. They also weigh up options before acting and tend to seek advice from trusted “others” within their network before or after they have made a decision. A mixture of causal and intuitive logic is evident in decisions dealing with internal business problems.

The decisions that entrepreneurs make shape and define their business and their ability to recover from crisis. If researchers can develop an understanding of how entrepreneurs make decisions – what information they draw upon, what support systems they use and the logic of their decision-making and rationalisation – then this can be used to help structure support.

By exploring decision-making through critical incidents we offer an innovative way to understand context-rich, first-hand experiences and behaviours of entrepreneurs around a focal point.

The purpose of this paper is to propose a data-driven scheme for identifying critical project complexity dimensions and establishing the trade-off across multiple project performance criteria.

This paper adopts a hybrid approach using Bayesian Belief Networks (BBNs) and Artificial Neural Networks (ANNs). The output of the ANN model is used as input to the BBN model for prioritizing project complexity dimensions relative to multiple project performance criteria. The proposed process is demonstrated through a real application in the construction industry.

With a number of nonlinear interactions involved within and across project complexity and performance, it is not feasible to model and assess the strength of these interactions using conventional techniques. The proposed process helps in effectively mapping a “multidimensional complexity” space to a “multidimensional performance” space and makes use of data from past projects for operationalizing this mapping scheme by means of ANNs. This obviates the need for developing a parametric model that is both challenging and computationally cumbersome. The mapping function can be used for generating all possible scenarios required for the development of a data-driven BBN model.

This paper introduces a data-driven process for operationalizing the mapping of project complexity to project performance within a network setting of interacting complexity dimensions and performance criteria. The results of the application study manifest the importance of capturing the interdependency across project complexity and performance. Ignoring the underlying interdependencies and relying exclusively on conventional correlation-based techniques may lead to making suboptimal decisions.

This study aims to provide an ontology based Baysian network for clinical specialty supporting. As a knowledge base, ontology plays an essential role in domain applications especially in expert systems. Interactive question answering systems are suitable for personal domain consulting and recommended for real-time usage. Clinical specialty supporting for dispatching patients can assist hospitals to locate desired treatment departments for individuals relevant to their syndromes and disease efficiently and effectively. By referring to interactive question answering systems, individuals can understand how to alleviate time and medical resource wasting according to recommendations from medical ontology-based systems.

This work presents an ontology based on clinical specialty supporting using an interactive question answering system to achieve this aim. The ontology incorporates close temporal associations between words in input query to represent word co-occurrence relationships in concept space. The patterns defined in lexicon chain mechanism are further extracted from the query words to infer related concepts for treatment departments to retrieve information.

The precision and recall rates are considered as the criteria for model optimization. Finally, the inference-based interactive question answering system using natural language interface is adopted for clinical specialty supporting, and indicates its superiority in information retrieval over traditional approaches.

From the observed experimental results, we find the proposed method is useful in practice especially in treatment department decision supporting using metrics precision and recall rates. The interactive interface using natural language dialogue attracts the users’ attention and obtains a good score in mean opinion score measure.

Information analysis tools enhance the possibilities of firm competition in terms of knowledge management. However, the generalization of decision support systems (DSS) is still far away from everyday use by managers and academicians. This paper aims to present a framework of analysis based on Bayesian networks (BN) whose accuracy is measured in order to assess scientific evidence.

Different learning algorithms based on BN are applied to extract relevant information about the relationship between operations strategy and flexibility in a sample of engineering consulting firms. Feature selection algorithms automatically are able to improve the accuracy of these classifiers.

Results show that the behaviors of the firms can be reduced to different rules that help in the decision‐making process about investments in technology and production resources.

Contrasting with methods from the classic statistics, Bayesian classifiers are able to model a variety of relationships between the variables affecting the dependent variable. Contrasting with other methods from the artificial intelligence field, such as neural networks or support vector machines, Bayesian classifiers are white‐box models that can directly be interpreted. Together with feature selection techniques from the machine learning field, they are able to automatically learn a model that accurately fits the data.