Nonmarket strategy research: systematic literature review and future directions

Wrona, Thomas; Sinzig, Corinna

doi:10.1007/s11573-017-0875-3

Cited by 36 publications

(31 citation statements)

References 93 publications

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“…These concepts emphasize the firm's ability to build up assets-for example, financial resources or reputation/credibility-that can be used in critical phases of a project. This perspective supports organizations as they work to improve performance by shaping their environment via information exchange with critical stakeholders who would otherwise be neglected (Baron 1995;Wrona and Sinzig 2018).…”

mentioning

confidence: 72%

Project Stakeholder Management as the Integration of Stakeholder Salience, Public Participation, and Nonmarket Strategies

Joos

Knyphausen‐Aufseß

Pidun

2020

Schmalenbach Bus Rev

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Despite the increasingly active role of civic actors, there is often no possibility for them to participate in project planning and decision-making. This discrepancy leads to costly conflicts and even failures. Unfortunately, the literature on project stakeholder management does not have sufficient theoretical substantiation to address this issue. To fill this knowledge gap, we integrate the concepts of stakeholder salience, public participation, and nonmarket strategy, and apply them to two urban infrastructure projects in Germany. This study contributes to the literature in two dimensions. First, it offers a dynamic and conceptual model for project stakeholder management, providing explanations for different conflict intensities. Second, it advances each individual area of research. Examples include the identification and clustering of so-called nonmarket assets, an examination of the influence of nonmarket strategies on managers' perceptions of stakeholder salience, and the study of public participation in a corporate-political context, rather than a purely political one.

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confidence: 72%

Project Stakeholder Management as the Integration of Stakeholder Salience, Public Participation, and Nonmarket Strategies

Joos

Knyphausen‐Aufseß

Pidun

2020

Schmalenbach Bus Rev

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“…Algorithm. Traditional reinforcement learning has a perfect theoretical model, and the algorithm is universal, but it has disadvantages such as low training efficiency and difficulty in processing high-dimensional data [29,30]. Deep learning needs to go through complex network screening and physical linear transformation, can perform specific analysis of data, extract high-level representations of data, have powerful analysis capabilities for data, have a complete training mechanism, provide an approximate solution method for optimization problems, which can achieve the best results in many applications [31].…”

Section: Reinforcement Learning and Deep Learning Combinedmentioning

confidence: 99%

Application of Reinforcement Learning Algorithm in Delivery Order System under Supply Chain Environment

Huang

Tan²

2021

Mobile Information Systems

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With the intensification of market competition and the development of market globalization, the efficiency of supply chain management orders has become an important part of enterprise competition resources. The competition among enterprises is fierce. To achieve effective customer response quickly, the time for supply chain order management is minimized, and refine the order processing process. This article introduces the strategy research of supply chain management order based on a reinforcement learning algorithm. This article first combines the reinforcement learning algorithm and deep learning algorithm, using the optimal decision-making ability of reinforcement learning algorithm and deep learning algorithm. The combination of data perception and the optimal ability to analyze examine the data of the order process, order cycle, and order delivery process of the supply chain order management and give the optimal decision. The supply chain order management process conducts questionnaire surveys and seminars to understand the current process of supply chain order management and the problems derived from the analysis of data based on the deep learning algorithm. Finally, through the output of the optimal strategy of the reinforcement learning algorithm, the supply chain order management process was improved, and the satisfaction survey was conducted again. The survey showed that the satisfaction was improved, and the satisfaction reached more than 90%.

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“…Nonmarket strategic fit -that is, the alignment between a firm's business strategy and the institutional environment (Lux, 2016) -is particularly hard to achieve for subsidiaries of MNEs. In the case of domestic businesses, nonmarket strategies aim to address 'unidimensional' tensions between the firm's market strategy and its nonmarket environment (see Wrona & Sinzig, 2018 for a recent literature review). Conversely, MNEs and their subsidiaries develop nonmarket strategies to resolve various multidimensional tensions: between host and home institutional frameworks (Blumentritt, 2003;Keim & Hillman, 2008); between the business strategy of the corporation and constraints imposed by international institutions (Boddewyn & Doh, 2011;Doh, McGuire, & Ozaki, 2015); and between simultaneously pursuing a global market strategy and a multi-domestic nonmarket strategy (Kobrin, 2015;White, Boddewyn, & Galang, 2015).…”

Section: Introductionmentioning

confidence: 99%

Self-categorization as a nonmarket strategy for MNE subsidiaries: Tracking the international expansion of an online platform

Curchod

Patriotta

Wright

2020

Journal of World Business

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This article examines how MNE subsidiaries develop nonmarket strategies to create a fit between a global market strategy and a local nonmarket framework. Derived from an analysis of archives and interviews on eBay's expansion into France, our findings suggest that MNE subsidiaries engage in defensive and proactive self-categorization to create their nonmarket strategic fit. Specifically, through the purposeful use of labels, rhetoric and narratives, self-categorization enables subsidiaries to strategically position themselves vis-à-vis both regulators and local incumbents, thereby exercising agency to influence the nonmarket environment in their preferred direction. The findings contribute to the institution-based view of international strategy by shedding new light on the interaction between MNE subsidiaries and local institutional authorities in a context of international expansion. Furthermore, we theorize how subsidiaries use self-categorization to transfer global organizational practices to the host country.

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Nonmarket strategy research: systematic literature review and future directions

Cited by 36 publications

References 93 publications

Project Stakeholder Management as the Integration of Stakeholder Salience, Public Participation, and Nonmarket Strategies

Project Stakeholder Management as the Integration of Stakeholder Salience, Public Participation, and Nonmarket Strategies

Application of Reinforcement Learning Algorithm in Delivery Order System under Supply Chain Environment

Self-categorization as a nonmarket strategy for MNE subsidiaries: Tracking the international expansion of an online platform

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