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Strategic fit to resources versus NPD execution proficiencies: what are their roles in determining success?

  • Original Empirical Research
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Abstract

This research examines the roles of strategic ‘fit’ versus execution proficiency in creating superior performance for new products. Specifically, we compare main effects versus moderation effects models of execution proficiency within a resource-based view (RBV) framework. Four new product success dimensions are outcomes. Marketing ‘fit’ and technological ‘fit’ are viewed as resource fit advantages and are antecedents in the model; marketing versus technical execution proficiencies relate to the project’s execution. The results show that the proficiencies-as-moderators model is the better fitting one; marketing but not technical proficiency is the key moderator. The results regarding resource fit advantage show that (1) both marketing fit and technological fit were positively related directly to profitability and to new product advantage; (2) marketing fit had direct positive effects on customer need met; and (3) neither marketing fit nor technological fit predicted speed. Concerning execution proficiencies: (1) technical execution proficiencies led to higher profitability and customer needs met, as well as speed; and (2) marketing execution proficiency was the only construct that led directly to increased success on all four dimensions examined in this research. Overall, support was found for the general premise that both marketing and technological resource fit advantages and marketing and technical execution proficiencies are significant predictors of new product success factors, with marketing proficiencies having additional moderating effects on the relationship of resource fit to performance.

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Notes

  1. $${\text{F = }}{{\left[ {{{\left( {{\text{R}}_{\text{2}} ^2 - {\text{R}}_{\text{1}} ^2 } \right)} \mathord{\left/ {\vphantom {{\left( {{\text{R}}_{\text{2}} ^2 - {\text{R}}_{\text{1}} ^2 } \right)} {\left( {{\text{k}}_{\text{2}} - {\text{k}}_{\text{1}} } \right)}}} \right. \kern-\nulldelimiterspace} {\left( {{\text{k}}_{\text{2}} - {\text{k}}_{\text{1}} } \right)}}} \right]} \mathord{\left/ {\vphantom {{\left[ {{{\left( {{\text{R}}_{\text{2}} ^2 - {\text{R}}_{\text{1}} ^2 } \right)} \mathord{\left/ {\vphantom {{\left( {{\text{R}}_{\text{2}} ^2 - {\text{R}}_{\text{1}} ^2 } \right)} {\left( {{\text{k}}_{\text{2}} - {\text{k}}_{\text{1}} } \right)}}} \right. \kern-\nulldelimiterspace} {\left( {{\text{k}}_{\text{2}} - {\text{k}}_{\text{1}} } \right)}}} \right]} {\left[ {{{\left( {1 - {\text{R}}_{\text{2}} ^2 } \right)} \mathord{\left/ {\vphantom {{\left( {1 - {\text{R}}_{\text{2}} ^2 } \right)} {\left( {{\text{n}} - {\text{k}}_{\text{2}} - 1} \right)}}} \right. \kern-\nulldelimiterspace} {\left( {{\text{n}} - {\text{k}}_{\text{2}} - 1} \right)}}} \right]}}} \right. \kern-\nulldelimiterspace} {\left[ {{{\left( {1 - {\text{R}}_{\text{2}} ^2 } \right)} \mathord{\left/ {\vphantom {{\left( {1 - {\text{R}}_{\text{2}} ^2 } \right)} {\left( {{\text{n}} - {\text{k}}_{\text{2}} - 1} \right)}}} \right. \kern-\nulldelimiterspace} {\left( {{\text{n}} - {\text{k}}_{\text{2}} - 1} \right)}}} \right]}}$$

    where R1 2 = R-square for the baseline main effects model, R2 2 = R-square for the interaction effects model, n = total sample size, k1 = number of predictors in the baseline model, and k2 = number of predictors in the interaction model. F has (k2 - k1) and (n - k2 -1) degrees of freedom.

  2. We employed the procedure suggested by Wynne W. Chin (2000). The comparison test is based on the t-statistic calculated as:

    $$t = \frac{{\beta _{1} - \beta _{2} }}{{{\left[ {{\sqrt {\frac{{{\left( {m - 1} \right)}^{2} }}{{{\left( {m + n - 2} \right)}}} \times S.E_{1} ^{2} + \frac{{{\left( {n - 1} \right)}^{2} }}{{{\left( {m + n - 2} \right)}}} \times S.E_{2} ^{2} } }} \right]} \times {\left[ {{\sqrt {\frac{1}{m} + \frac{1}{n}} }} \right]}}}$$

    where df = m+n-2 [m is the sample size of the first (success) sample and n is the sample size of the second (failure) sample].

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Acknowledgement

The authors would like to acknowledge the Academy of Marketing Science and Product Development and Management Association for their support.

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Authors and Affiliations

  1. College of Administrative Sciences and Economics, Koc University, Sariyer, Istanbul, 34450, Turkey

    Nukhet Harmancioglu

  2. Department of Marketing and Supply Chain Management, Eli Broad College of Business, Michigan State University, N324 Business Complex, East Lansing, MI, 48824, USA

    Cornelia Droge

  3. Department of Marketing and Supply Chain Management, Eli Broad College of Business, Michigan State University, N307 Business Complex, East Lansing, MI, 48824, USA

    Roger J. Calantone

Authors
  1. Nukhet Harmancioglu
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Correspondence to Nukhet Harmancioglu.

Appendix A. Descriptive statistics for firms in the sample

Appendix A. Descriptive statistics for firms in the sample

 

Annual Company Sales (millions)

Number of Employees

R&D Percent

% Sales by New Products

% Profits by New Products

Mean

380006

1468

3.92

18.93

22.18

Median

200000

400

3.10

15.00

18.28

Mode

100000

400

2.00

50.00

5.00

Std Deviation

627089

4216

2.83

16.63

20.27

Minimum

40

11

0.10

0.00

0.00

Maximum

3100000

30000

15.00

80.00

90.00

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Harmancioglu, N., Droge, C. & Calantone, R.J. Strategic fit to resources versus NPD execution proficiencies: what are their roles in determining success?. J. of the Acad. Mark. Sci. 37, 266–282 (2009). https://doi.org/10.1007/s11747-008-0125-x

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