Retailers’ communication support for their price promotions is shifting from traditional flyers and circulars (so-called feature ads) to conventional media channels, especially digital ads. It is not clear, if and how supporting price promotion with advertising in digital media benefits sales of the promoted product above and beyond the price promotion itself. Further, retail managers require guidance on whether only the promoted product or also their overall business gains from ad support (e.g., from category or cross-period expansions) to negotiate trade promotion support with manufacturers of the promoted products. Using a field experiment with a grocery retailer, we decompose the effects of the advertising support of price discount promotions across digital and print marketing channels. We find that the effectiveness assessment of the advertising channels depends on the beneficiary: while digital channels most effectively support sales of the promoted product (35 % uplift vs. non-promotion period) – especially for popular consumer-pull products (+85 %), traditional print channels improve the performance for the retailer as a whole (+3 % uplift of the total category sales), with a combination of ads having the largest effect (+5 % uplift of the total category sales). This research offers guidance for retail and manufacturer managers tasked with designing price promotions and configuring the ad support across channels, and negotiating trade promotion budgets or manufacturer support for the advertisements.

Innovation-focused co-creation between companies and individual external contributors is accompanied by the challenge of managing intellectual property (IP). The existing literature presents scattered evidence of various elements of the arrangements adopted by companies to manage their IP (such as a high or low degree of IP control, monetary or non-monetary compensation, non-disclosure agreements, additional agreements and the waiver option) in different co-creation settings (including crowdsourcing contests, virtual communities, single expert sessions and lead user workshops). However, the existing literature exhibits little understanding of how particular IP arrangements influence co-creation project performance in specific settings. Drawing upon contingency theory and configurational theory, we provide a framework that explains both the effectiveness of different IP configurations and the moderating role that co-creation settings may have on the relationship between IP arrangements and project performance. By the means of fuzzy-set Qualitative Comparative Analysis (fsQCA) on a sample of 116 co-creation projects, we determine the impact of various IP arrangements on project performance in different co-creation settings, and we show how this effect differs across those settings. Our study also demonstrates that IP matters for success in co-creation, while highlighting the interdependence of multiple elements of IP arrangements and their joint influence on co-creation project performance. Our study thus fills the gap in the literature where previous research failed to embrace the context-dependent and multidimensional effect of IP arrangements on co-creation project performance. Additionally, this study offers best-practice guidelines for managers for designing IP arrangements to meet the specific characteristics of their co-creation projects and to ensure their success.

This paper studies the identity transitions of East German audit recruits during the fundamental ideological, economic, and societal change brought about by the reunification of Germany in 1990. Integrating the identity work literature with key concepts from Pierre Bourdieu and Erving Goffman, we build on semi-structured interviews with two groups of recruits—university graduates and former state auditors—to explore and theorize the marked differences observed in these recruits' transition processes. In line with wider processes of territorial stigmatization, we argue that the West German audit firms pragmatically instrumentalized their local personnel, seeking to deploy them without intending to integrate them into the profession. In turn, the audit recruits met with this exertion of symbolic violence by managing a ‘spoiled identity’. The university graduates found it easier to recognize and accumulate legitimate forms of capital, thereby submitting themselves to the inculcation of the profession's socialization process, which ultimately yielded their institution into the profession. In contrast, the former state auditors' local knowledge and access to client networks provided immediately useful capital to the West German firms, which, however, sought to retain a status differential vis-à-vis these recruits. As a result of such strategies of condescension, the former state auditors maintained key aspects of their identity as a salient part of their self-conception. We further highlight the role of the local audit offices in the recruits' transition processes, as they evolved from flexible spaces, which allowed for experimentation and improvisation, into more structured units. This process embedded professional values and practices, thereby creating localized office identities.

Constraint programming solvers are known to perform remarkably well for most scheduling problems. However, when comparing the performance of different available solvers, there is usually no clear winner over all relevant problem instances. This gives rise to the question of how to select a promising solver when knowing the concrete instance to be solved. In this article, we aim to provide first insights into this question for the flexible job shop scheduling problem. We investigate relative performance differences among five constraint programming solvers on problem instances taken from the literature as well as randomly generated problem instances. These solvers include commercial and non-commercial software and represent the state-of-the-art as identified in the relevant literature. We find that two solvers, the IBM ILOG CPLEX CP Optimizer and Google’s OR-Tools, outperform alternative solvers. These two solvers show complementary strengths regarding their ability to determine provably optimal solutions within practically reasonable time limits and their ability to quickly determine high quality feasible solutions across different test instances. Hence, we leverage the resulting performance complementarity by proposing algorithm selection approaches that predict the best solver for a given problem instance based on instance features or parameters. The approaches are based on two machine learning techniques, decision trees and deep neural networks, in various variants. In a computational study, we analyze the performance of the resulting algorithm selection models and show that our approaches outperform the use of a single solver and should thus be considered as a relevant tool by decision makers in practice.

An extension operator assigns to any TU game its extension, a mapping that assigns a worth to any non-negative resource vector for the players. Algaba et al. (2004) advocate the Lovász extension (Lovász, 1983) as a natural extension operator. This operator is determined by the minimum operator representing one particular CES (constant elasticity of substitution) technology. We explore alternative extension operators, the dual Lovász extension and the Shapley extension, that are based on the only two alternative CES technologies that induce an economically sound behavior of extensions in some sense, the maximum operator and the average operator.