Resource Allocation Decisions Under Imperfect Evaluation and Organizational Dynamics

Schlapp, Jochen; Oraiopoulos, Nektarios; Mak, Vincent

doi:10.1287/mnsc.2014.2083

Cited by 48 publications

(24 citation statements)

References 36 publications

(48 reference statements)

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“…These studies indicate that the presence of champions increases the likelihood of resource allocation. Relatively little is, however, known about how champions' presence exactly influence resource allocation (Markham, 1998;Schlapp et al, 2015). The present study provides two propositions that provide a potential and grounded explanation for the effect found in earlier studies.…”

Section: Discussionmentioning

confidence: 48%

“…In response to the results, Markham argued that future research should address the mechanism by which champions affect resource allocation. In recent literature it is argued that champions' advocacy behaviour plays an important role in managers' project funding decisions (Schlapp et al, 2015). A deeper understanding of how champions' presence exactly influences resource allocation is, however, still lacking.…”

Section: Study Findingsmentioning

confidence: 99%

“…Studies conducted by Chakrabarti (1974), Markham et al (1991) and Markham (2000) indicate that the presence of a champion in an innovation project makes it more likely that resources will be allocated to the innovation project. It has been argued in recent literature that champions' advocacy behaviour may explain this effect (Schlapp et al, 2015). A deeper understanding of how champions influence resource allocation is, however, lacking.…”

Section: Introductionmentioning

confidence: 99%

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Towards a deeper understanding of how champions influence the allocation of resources to innovation projects

Rutten¹,

Dorée²,

Halman³

2019

EPOJ

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Previous research indicates that the presence of a champion in an innovation project increases the likelihood that firms will allocate resources to the innovation project. Relatively little is, however, known about how champions’ presence exactly influences resource allocation. A case study of two innovation projects in the construction industry was conducted to further explore this question. The findings suggest that it is not so much champions’ presence as such, but one of champions’ prototypical behaviours that influences firms’ willingness to allocate resources. Here we refer to champions’ expression of enthusiasm and confidence about the success of an innovation. Further, the findings suggests that the effect might be explained by the mediating role of firms’ expectations of the rate of adoption. Overall, the case study provides a step towards a deeper understanding of how champions induce firms to allocate resources to innovation projects.

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Section: Discussionmentioning

confidence: 48%

Section: Study Findingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Towards a deeper understanding of how champions influence the allocation of resources to innovation projects

Rutten¹,

Dorée²,

Halman³

2019

EPOJ

View full text Add to dashboard Cite

show abstract

“…Markham (1998) argued that future research should address the mechanism by which champions influence resource allocation. Recent literature suggests that champions' advocacy skills play an important role in managers' project funding decisions (Schlapp et al, 2015). A deeper understanding of how champions influence resource allocation is, however, still lacking.…”

Section: Key Actorsmentioning

confidence: 99%

“…These studies indicate that the presence of champions increases the likelihood of resource allocation during the development stage of collaborative innovation projects. Little is, however, known about how champions' presence exactly affects resource allocation (Markham, 1998;Schlapp et al, 2015). By developing three propositions that address this issue, the case study provides a step towards a deeper understanding of how champions influence resource allocation.…”

Section: Contributions To the Understanding Of The Role Of Systems Inmentioning

confidence: 99%

Collaboration for innovation in the construction sector: key actors and resource allocation decisions

Rutten¹

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The work contained in this thesis has been financially supported by PSIBouw. This support is gratefully acknowledged. i Further, of course, I would like to thank the many firms that participated in the research that led to this thesis. I appreciate the time you took for answering my questions. Finally, I want to thank Margreet, Jannah, Carlijn, my family and my friends for being in my life and making life a great time. Thank you! iii Contents Acknowledgements i List of publications v List of figures vii List of tables viii Chapter 1-Introduction 1.1 Construction: a fragmented sector 1.2 Collaboration for innovation 1.3 A barrier to collaborative innovation 1.4 Field of study: key actors and resource allocation decisions 1.5 Objectives of the thesis 1.6 Four studies 1.7 Outline of the thesis Chapter 2-The role of systems integrators in collaborative innovation in the construction sector (study I) 2.1 Introduction 2.2 A classification of firms in CoPS industries 2.3 Systems integrators in construction industry 2.4 Understanding interorganizational innovation 2.5 Conclusion Chapter 3-Towards a deeper understanding of how champions influence the allocation of resources to collaborative innovation projects (study II) 29 3.1 Introduction 3.2 Previous research on champions 3.3 Method 3.4 The collaborative innovation projects 3.5 The model that emerged 45 3.6 Contributions, limitations and future research 3.7 Conclusion iv Chapter 4-Exploring the value of narrative-based decision theory in understanding the decision to allocate resources to an innovation project (study III) 51 4.1 Introduction 4.2 The NBDT view 53 4.3 A finding from the RBP experiments 56 4.4 Expanding on the underlying mechanism 4.5 Discussion and conclusion Chapter 5-Together on the path to construction innovation: Yet another example of escalation of commitment? (study IV) Chapter 6-Main contributions and implications 85 6.1 Research overview 86 6.2 Summary of the main contributions 6.3 Directions for future research 6.4 Managerial implications 6.5 Conclusion Appendix A-PhD coursework Appendix B-Magnitude of the sunk cost effect in the RBP experiments Appendix C-Survey instrument References Summary Nederlandse samenvatting 131 Curriculum vitae v List of publications The research presented in this thesis led to the following publications.

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Effects of behavioral bias regarding demand forecasting in a competitive market

Li,

Zhao,

Zheng

2024

Naval Research Logistics

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Considerable human judgment is involved in demand forecasting. When managers judge demands under uncertainty, they inevitably use signals to update their demand information. These signals are seldom perfect; hence, managers hold behavioral bias about the signal fidelity, that is, over‐ or under‐estimating the signal fidelity. This article models managers' behavioral bias about signal fidelity in Bayesian demand forecasting and explores its impact on competitive firms. We find that no matter whether the competitor's manager is unbiased or biased, a firm can benefit from its manager's slight overestimation, but the competitor can benefit from the firm's manager's underestimation. However, when one firm's manager is biased, improving the signal fidelity may not constantly improve firms' profits, revealing the potential risk of behavioral bias on the efficiency of the forecasting systems. We further consider the diversity of biased managers and the information asymmetry regarding the bias. Except that the benefits of behavioral bias exist, we additionally find that managers' heterogeneous behavioral bias can form a hedge effect and bring a win‐win situation. Under asymmetric information, managers' inference bias on the competitor's type may benefit firms by easing the negative impact of managers' behavioral bias about signal fidelity. We finally analyze the social welfare and consumer surplus, check the robustness of the main results and deliver additional findings by considering competing firms, different signal fidelity measures, and the signal‐dependent behavioral bias.

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Resource Allocation Decisions Under Imperfect Evaluation and Organizational Dynamics

Cited by 48 publications

References 36 publications

Towards a deeper understanding of how champions influence the allocation of resources to innovation projects

Towards a deeper understanding of how champions influence the allocation of resources to innovation projects

Collaboration for innovation in the construction sector: key actors and resource allocation decisions

Effects of behavioral bias regarding demand forecasting in a competitive market

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