Expectations of reciprocity when competitors share information: Experimental evidence

Ganglmair, Bernhard; Holcomb, Alex; Myung, Noah

doi:10.1016/j.jebo.2019.12.006

Cited by 6 publications

(4 citation statements)

References 71 publications

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“…Today, information about competitors is regarded as a critical benefit for organizations and can improve innovation management (Sutinen and Vanhalakka-Ruoho, 2014). Also, informal knowledge about trading and exchanging the information among competitors has been recorded for the diversity of businesses (Ganglmair et al , 2018). Knowing competitor’s information has a vital role in the success of business and organization.…”

Section: Introductionmentioning

confidence: 99%

A new conceptual framework for identifying the factors influencing the effectiveness of competitive intelligence

Asghari

Targholi

Kazemi

et al. 2020

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Purpose Competitive intelligence (CI) collects data through the distribution of knowledge to make decisions in a competitive environment. To better comprehend the concept of CI, the purpose of this paper is to determine the role of effective factors (knowledge sharing, competitor information, information technology (IT) and organizational culture) and their impact on CI. In addition, the paper provides a conceptual framework for determining the practical factors on CI. Design/methodology/approach For evaluating the elements of the model, a questionnaire is considered. Questionnaires were reread by specialists with significant experiences in the CI field. For statistical analysis, the SPSS 22 and SmartPLS 3.2 software package is also used. Findings Findings from the study showed the validity of the model for a CI assessment. Furthermore, the results confirmed that the competitor information significantly influenced CI. In addition, the obtained results implied that IT has a significant and positive influence on CI. The effect of organizational culture on CI also proved to be positive and significant. Research limitations/implications This paper makes significant contributions for both researchers and practitioners; however, the authors determine some limitations, which are as follows: First, the authors showed the result in a single region. It cannot be assured that the results are generalizable to other areas. Second, because of time and financial constraints, the authors gathered the data using a sample from a single location. Third, the use of variables to depict CI may be not exhaustive. The authors, therefore, encourage future research to study these CI dimensions. Practical implications This study meaningfully contributes to the knowledge and literature by focusing more on CI, examining other significant aspects and applying advanced statistical analysis method. Also, current research results suggest practical implications for marketing practitioners and managers who implemented tools and made strategic plans to enhance the organization’s performance. From a practical perspective, the statistical results support the crucial role of the following factors: IT, competitor information, organizational culture and knowledge sharing on CI. Originality/value Experts in the area of knowledge management, CI and strategic management can use this study to gain a competitive advantage based on knowledge and information resources. Organizations must have knowledge management function and CI to support the strategy formulation, implementation and evaluation.

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Section: Introductionmentioning

confidence: 99%

A new conceptual framework for identifying the factors influencing the effectiveness of competitive intelligence

Asghari

Targholi

Kazemi

et al. 2020

View full text Add to dashboard Cite

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“…To quote from Rabin (1993): “People are willing to sacrifice their own material well‐being to help those who are being kind and to punish those who are being unkind.” This reciprocal behavior is confirmed by substantial experimental literature (e.g., Berg et al, 1995; Chlass et al, 2019; Deng et al, 2021; Fehr & Gachter, 2000a, 2000b). The empirical evidence is also reported in the automotive, computer, and construction industries (Ganglmair et al, 2020; Lau et al, 2010; Morgan & Daniel, 2001). For example, in the automobile industry, the manufacturer often invests directly or exerts some effort to improve the supplier's production reliability and efficiency by providing engineering resources, information technology, or special training (Paulraj et al, 2008; Zhang et al, 2009).…”

Section: Introductionmentioning

confidence: 73%

Cost‐sharing procurement contracts with embedded reciprocal behavior

Jiang

2023

Manage Decis Econ

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This paper develops a simple linear formulation of cost-sharing procurement contracts into which reciprocal behavior is embedded. The comparative static analysis shows that the reciprocal behavior is negatively associated with the incentive. Both the cost uncertainty and the supplier's risk aversion have a positive influence on the reciprocal behavior and cooperation between buyers and suppliers. Compared to the second-best solution in the existing principalagent literature under asymmetric information, this paper finds that the presented model is a Pareto improvement under modest restriction even if cost-reducing effort is unverifiable. The reciprocal model has the properties of first-best optimal social surplus.

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“…In Ganglmair, et al; agents are willing to share information if they expect their opponents to reciprocate. Therefore, reciprocity and information create a "feedback loop" that affects the efficiency of the strategic interaction.…”

Section: Introductionmentioning

confidence: 99%

Reciprocity and Learning Effects in Price Competition

Nese,

O'Higgins,

Sbriglia

2023

SSRN Journal

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Expectations of reciprocity when competitors share information: Experimental evidence

Cited by 6 publications

References 71 publications

A new conceptual framework for identifying the factors influencing the effectiveness of competitive intelligence

A new conceptual framework for identifying the factors influencing the effectiveness of competitive intelligence

Cost‐sharing procurement contracts with embedded reciprocal behavior

Reciprocity and Learning Effects in Price Competition

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