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Research involving interorganizational relationships (IORs) has grown at an impressive rate. Several datasets have been used to understand the nature and performance implications of these relationships. Given the importance of such relationships, we describe a relatively new dataset, Bloomberg SPLC, which contains data regarding the percentage of costs and revenues attributed to suppliers and customers, as well as allows researchers to construct a comprehensive dataset of IORs of buyer–supplier networks. Because of this, Bloomberg SPLC data can be used to uncover new and exciting theoretical and empirical implications. This chapter provides background information about this dataset, guidance on how it can be leveraged, and new theoretical terrain that can be charted to better understand IORs.

This chapter describes the complementary benefits of model-building and data analysis using algorithm and statistical modeling methods in the context of unobtrusive marketing field experiments and in transforming findings into isomorphic-management models. Relevant for marketing performance measurement, case-based configural analysis is a relatively new paradigm in crafting and testing theory. Statistical testing of hypotheses to learn net effects of individual terms in MRA equations is the current dominant logic. Isomorphic modeling might best communicate what executives should decide using the findings from algorithm and statistical models. Data testing these propositions here uses data from an unobtrusive field experiment in a retailing context and includes two levels of expertise, four price points, and presence versus absence of a friend (“pal” condition) during the customer-salesperson interactions (n = 240 store customers). The analyses support the conclusion that all three approaches to modeling provide useful complementary information substantially above the use of one or the other alone and that transforming findings from such models into isomorphic-management models is possible.

The chapter pays specific attention to the organizing and reorganizing process of the embedding of new technology. The aim is to increase the understanding of how a focal technology is incrementally aligned into a customer’s different business settings. Embedding becomes subject to intense organizing efforts. It becomes a struggle with activating different features of the focal technology by forging and modifying the resource interfaces between the focal technology and customer resources.

The organizing efforts are about seeking, in an explorative mood, for resource interfaces between the focal technology and the customer resources. This organizing process enables the identification of new adaptation opportunities for technology embedding processes, whereby the focal technology obtains certain feature and values.

A systematically developed knowledge of resource interfaces is a key for activating different features of the focal technology and thereby facilitating its embedding into the customer’s various business settings. This is described in a single case study in the chapter. This case and the analysis show how a supplier and a customer struggle with developing resource interface knowledge to activate the different features of the focal technology, thereby facilitating its embedding process. The first part of the chapter establishes a theoretical framework, followed in the second part by the case study and analysis. The concluding discussion emphasizes the importance of understanding and managing various interfaces as part of the organizing processes.

Purpose – The aim of this chapter is to investigate how a focal market actor may design or redesign business models for improved value co-creation.

Findings – We posit that value is co-created in use as actors integrate resources in practices, which makes practices a fundamental unit of value creation. Greater density of resources, relevant to a specific practice and to the goals or mission of the actor, corresponds to greater value. The role of a provider is to support other actors in their value-creation processes by providing resources that ‘fit’ into their practices.

We identify 12 categories of business model design elements that need to be defined and developed in parallel. We conclude that a focal actor needs to strive for both intra-actor and inter-actor (meso-level) configurational fit of business model elements in order to enable purposeful co-creation in specific practices.

Finally, we propose that meso-level configurations develop in a three-phase process of origination, mobilization and stabilization. A focal actor wishing to improve co-creation in a network needs to develop value propositions not only for customers but also for other actor domains. Overall, the performative power of a market actor is dependent on its network position, the relative strength of its business model and the actor's ability to author compelling meanings.

Originality – The research contributes to the discussion on value co-creation by identifying three shifts in the unit of analysis: (1) we argue that use-value is co-created as actors integrate resources in practices, rendering practices a fundamental unit of analysis, (2) as practices are outcomes of business models, we identified business model design as a key unit of analysis for the improvement of value co-creation and (3) our view on business models is network-centric and we focus on how to introduce new business model elements in a specific actor network.

Practical implications – The realization of the fact that value creation occurs in networks of interdependent actors pinpoints the need for increased transparency both between functional silos and between actors. The business model framework identifies 12 design elements, which can act as a ‘checklist’ for managers wanting to engage in co-creative business models.

This paper reports the results of a three-year-long research on business relationships, relying on qualitative data gathered through multiple-case study research of four focal companies operating in Australia. The industry settings are as follows: steel construction, vegetable oils trading, aluminum and steel can manufacture, and imaging solutions. The research analyzes two main aspects of relationships: structure and process. This paper deals with structure describing it by the most desired features of intercompany relationships for each focal company. The primary research data have been coded drawing on extant research into business relationships. The main outcome of this part of the research is a five construct model composed by trust, commitment, bonds, distance, and information sharing that accounts for all informants’ utterances about relationship structure.

Marketing metrics represent a growing concern for practitioners and scholars alike. Among the performance measures at the individual account level, customer share emerges as a concept of growing interest, yet marketing lacks rigorous customer share metrics in business markets. In addition, the construct's position within the nomological net of relationship marketing in a business-to-business (B2B) context remains unclear. This research reports findings of a cross-sectional study among purchasing managers in U.S. manufacturing industries, which indicate a positive link between customer value and customer share in business relationships. Relationship benefits have a stronger impact on customer share than do relationship costs, such that sourcing and operations benefits appear to represent the most promising levers for effective customer share management. The results finally suggest that researchers should operationalize customer share in relative terms when investigating key supplier relationships across different industries.

We develop and test a model of how a software firm's business strategy (product scope and market scope) interacts with the firm's network position (alliance degree and structural holes) to impact performance. We test the joint-effects hypotheses on a sample 359 packaged software firms that have entered into 5,489 alliances involving 2,849 distinct firms during the time period, 1990–2002. While prior studies have demonstrated the importance of network positions as a determinant of firm strategy and performance, this chapter begins to examine the performance effects of how a firm's business strategy and network positions interact. We find support for three of the four hypotheses lending empirical support for our theoretical model. We develop implications for network-based perspectives of strategy and outline areas for further research.

Organizations across industries are increasingly using Artificial Intelligence (AI) systems to support their innovation processes, supply chains, marketing and sales and other business functions. Implementing AI, firms report efficiency gains from automation and enhanced decision-making thanks to more relevant, accurate and timely predictions. By exposing the benefits of digitizing everything, COVID-19 has only accelerated these processes. Recognizing the growing importance of AI and its pervasive impact, this chapter defines the “social value of AI” as the combined value derived from AI adoption by multiple stakeholders of an organization. To this end, we discuss the benefits and costs of AI for a business-to-business (B2B) firm and its internal, external and societal stakeholders. Being mindful of legal and ethical concerns, we expect the social value of AI to increase over time as the barriers for adoption go down, technology costs decrease, and more stakeholders capture the value from AI. We identify the contributions to the social value of AI, by highlighting the benefits of AI for different actors in the organization, business consumers, supply chain partners and society at large. This chapter also offers future research opportunities, as well as practical implications of the AI adoption by a variety of stakeholders.

What is the scope of brokerage network to be considered in thinking strategically? Given the value of bridging structural holes, is there value to being affiliated with people or organizations that bridge structural holes? The answer is “no” according to performance associations with manager networks, which raises a question about the consistency of network theory across micro to macro levels of analysis. The purpose here is to align manager evidence with corresponding macro evidence on the supplier and customer networks around four-digit manufacturing industries in the 1987 and 1992 benchmark input–output tables. In contrast to the manager evidence, about 24% of the industry-structure effect on industry performance can be attributed to structure beyond the industry's own buying and selling, to networks around the industry's suppliers and customers. However, the industry evidence is not qualitatively distinct from the manager evidence so much as it describes a more extreme business environment.