Environments, unobserved heterogeneity, and the effect of market orientation on outcomes for high-tech firms

Rajdeep Grewal; Murali Chandrashekaran; Jean L. Johnson; Girish Mallapragada

doi:10.1007/s11747-011-0295-9

Journal of the Academy of Marketing Science

March 2013, Volume 41, Issue 2, pp 206–233 | Cite as

Environments, unobserved heterogeneity, and the effect of market orientation on outcomes for high-tech firms

Authors
Authors and affiliations

Rajdeep GrewalEmail author
Murali Chandrashekaran
Jean L. Johnson
Girish Mallapragada

Original Empirical Research

First Online: 13 December 2011

Received: 16 March 2010

Accepted: 20 October 2011

Abstract

The interaction between market orientation and facets of the environment is theoretically compelling and is hence the primary interaction studied in market orientation literature. Yet empirical literature offers mixed findings regarding these interaction effects. We suggest that these mixed findings may result from the failure of extant research to control for unobserved heterogeneity that may mask the true relationships among market orientation, facets of the environment, and firm outcomes. Such unobserved heterogeneity might arise due to presence of higher order (e.g., three-way, four-way) moderators (e.g., firm size and innovativeness). To illustrate our assertions on unobserved heterogeneity and the role of firm size and innovativeness, we present two studies that use firm performance or new product performance as the outcome variable; the studies (1) include market orientation, two facets of the environment (technological turbulence and market dynamism), and the interactions between market orientation and facets of the environment as explanatory variables, (2) employ finite mixture regression models to estimate the relationships of interest while explicitly accounting for unobserved heterogeneity in the form of latent regimes (segments), and (3) use firm size and innovativeness as concomitant profiling variables in the finite mixture model specification. The results indicate that disaggregate models (i.e., multi-regime solutions) offer the best fit in both studies. The effects across the latent regimes differ, demonstrating the possibility of an aggregation bias in empirical literature and suggesting the need for using disaggregated analyses to study important marketing phenomena. In theoretical terms, these results also suggest the possibility of developing theories that incorporate unobserved heterogeneity and perhaps higher order (e.g., three-way) interaction effects.

Keywords

Market orientation Environment Latent class analysis Unobserved heterogeneity

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Appendix A: Latent-class regression analysis

Model structure

We propose a finite mixture of linear regressions, also referred to as latent-class regressions (DeSarbo and Cron 1988; Hutchinson et al. 2000; Wedel and Kamakura 2000), based on finite mixture distribution theory (Everitt and Hand 1981; Titterington et al. 1985). Specifically, we assume R regimes, and the response function for regime r with mixing proportions θ_f can be represented as:

$$ F{O_f} = \sum\limits_{{r = 1}}^R {{\theta_r}\left( {{\beta_{{r0}}} + {\beta_{{r1}}} \cdot M{O_f} + \overline {{\alpha_r}} \cdot \overline {{X_f}} + \overline {{\delta_r}} \cdot M{O_f} \cdot \overline {{X_f}} + {\varepsilon_{{rf}}}} \right)}, $$

(A1)

where, $ {\bar{X}_f} $ denotes the vector of environmental variables (market dynamism and technological turbulence), β _r1 is the main effect of market orientation (MO) in regime r, $ {\bar{\alpha }_r} $ denotes the vector of parameters capturing the effect of the vector of environmental variables ($ {\bar{X}_f} $) in regime r, and $ {\bar{\delta }_r} $ captures the vector of parameters for the interaction between MO _f and $ {\bar{X}_f} $ in regime r.

By relying on the Bayes rule, the posterior probability that regime r is representative of firm f is given as:

$$ P[f \in r|F{O_f}] = \frac{{{\theta_{{r|f}}}{L_{{f|r}}}}}{{\sum\limits_{{r = 1}}^R {{\theta_{{r|f}}}{L_{{f|r}}}} }}, $$

(A2)

where, θ _r|f denotes the prior probability that regime r is representative of firm f, and L _f|r is the likelihood value for firm f for regime r. Consistent with extant literature (Dayton and MacReady 1988; Gupta and Chintagunta 1994; Kamakura et al. 1994), we use the logit formulation to specify the prior probabilities as:

$$ {\theta_{{r|f}}} = \frac{{{e^{{{\lambda_r}}}}}}{{\sum\limits_{{r = 1}}^R {{e^{{{\lambda_r}}}}} }}, $$

(A3)

where, λ _r is a parameter to be estimated for each regime. Equation A3 then can be standardized as (e.g., Gupta and Chintagunta 1994):

$$ {\theta_{{r|f}}} = \frac{{{e^{{{\lambda_r}}}}}}{{\left[ {1 + \sum\limits_{{r = 1}}^{{R - 1}} {{e^{{{\lambda_r}}}}} } \right]}}. $$

(A4)

We treat the Rth regime as the base and need to estimate only R-1 parameters. The likelihood for each regime is specified on the basis of the standard normal density as:

$$ {L_{{f|r}}} = \varphi * ({\varepsilon_r}), $$

(A5)

where, ϕ*(.) is the standardized normal density function, and ε _r is residual error, such that ε _r ∼N(0, σ _r). Thus, the likelihood function can be written as:

$$ L = \prod\limits_{{f = 1}}^F {\sum\limits_{{r = 1}}^R {{\theta_{{r|f}}}{L_{{f|r}}}} }, $$

(A6)

where we have F firms in our data set and estimate R regimes. The preceding equation or its natural logarithm can be maximized to obtain parameter estimates for the R-regime solution.

Determining number of relationships

The maximization of Eq. A6 rests on the assumption that we know the number of regimes R to be estimated. If not, the obvious question becomes how to determine R. It is not possible to perform formal tests to determine the number of relationships, because the asymptotic assumptions of these tests are no longer valid (e.g., Aitkin and Rubin 1985). Researchers generally use information criteria, such as the Akaike information criterion (AIC; e.g., Akaike 1974), the Bayesian information criterion (BIC; see Schwarz 1978), or the consistent Akaike information criterion (CAIC; Bozdogan 1987) to infer the number of regimes (e.g., Helsen et al. 1993; Ramaswamy et al. 1993).

Yet these information criteria are based on asymptotic assumptions as likelihood ratio tests and can, at best, be used as heuristics to determine the number of regimes (Sclove 1987). True to the heuristic nature of these model selection criteria, in an extensive simulation study, Bozdogan (1987) concludes that BIC and CAIC tend to underfit models (i.e., underestimate the number of regimes), and the AIC tends to overfit models (i.e., overestimate the number of regimes). To overcome these problems, scholars recommend information complexity-based criteria that provide a “numerical measure to assess parameter redundancy and stability uniquely all in one measure” (Bozdogan 2000, p. 75; also see Bozdogan 1994). Information complexity measures rely on the model covariance structure (usually defined in terms of the inverse of the Fisher information matrix), with the recommended measure defined as (Bozdogan 2000; Bozdogan 1994):

$$ ICOMP = - 2 * \ln (L) + \kappa * \ln (tr(\Sigma )/\kappa ) - \ln |\Sigma |, $$

(A7)

where L is the model likelihood, κ is the number of parameters estimated in the model, Σ is the parameter covariance matrix, tr(Σ) is the trace of the matrix, and |Σ| is its determinant. Bozdogan (2000) shows that ICOMP outperforms AIC-based criteria (i.e., AIC, BIC, and CAIC) in selecting models in problems similar to ours (i.e., multivariate normal linear or nonlinear structural models). Thus, we use ICOMP as our model selection criteria, but we also report AIC and BIC for illustrative purposes (to the best of our knowledge, Kamakura et al. [1994] provide the only use of ICOMP in existing marketing literature).

We also report an entropy measure of separation (ES) to assess the extent of separation of the regimes (Wedel and Kamakura 2000). We calculate ES (bounded in the range 0–1, such that a value closer to 1 indicates good separation of latent regimes) as:

$$ ES = 1 - \frac{{\sum\limits_{{f = 1}}^F {\sum\limits_{{r = 1}}^R { - {p_{{f|r}}}\ln ({p_{{f|r}}})} } }}{{F\ln (R)}}, $$

(A8)

where, p _f|s is the probability of firm f belonging to regime r, which we calculate using the Bayes rule specified in Eq. B4.

Estimation strategy

We maximize the log of the likelihood function for R regimes to obtain parameter estimates (see Equation A8). Specifically, the log-likelihood is given as:

$$ \ln (L) = \sum\limits_{{f = 1}}^F {\ln \left[ {\sum\limits_{{r = 1}}^R {{\theta_{{r|f}}}{L_{{f|r}}}} } \right]} . $$

(A9)

We start with the homogenous solution (R = 1) and then estimate the two-regime solution (i.e., R = 2). If, according to ICOMP, model fit improves, we estimate the solution with one more regime (i.e., R = 3). We continue to add regimes until there is no improvement in model fit in terms of the information criterion. We use the expectation-maximization (E-M) algorithm to maximize the log-likelihood function (see DeSarbo and Cron 1988; Wedel and Kamakura 2000). Finally, to ensure that we obtain the optimal solution for each regime, we use 50 randomly selected starting values (i.e., random seed to obtain initial partitioning of firms into regimes for the E-M algorithm).

Appendix B: Measures

Market Orientation (ρ₁ = .94; ρ₂ = .94). Please respond to the following statements by circling the most appropriate number (Deshpandé and Farley 1998):

I believe that this business exists primarily to serve customers. ^*#

We freely share information about our customer experiences, even those that may not be positive, across all areas of the firm.

Our strategy for competitive advantage is based on our understanding of customer needs. ^#

Our business objectives are driven primarily by customer satisfaction.

We constantly monitor our level of commitment to serving customer needs.

We measure customer satisfaction systematically and frequently. ^*

We are more customer-focused than our competitors.

Our salespeople regularly share information concerning competitors’ strategies. ^*#

We rapidly respond to competitors’ actions. ^#

Top management regularly discusses competitors’ strengths and strategies. ^*#

Data on customer satisfaction are disseminated regularly at all levels in our firm. ^*

Firm Performance (ρ₁ = .86). ^c Please rate each aspect of your firm’s performance on the scale provided (e.g., Grewal and Tansuhaj 2001):

Profitability relative to goals.

Sales volume relative to goals.

ROI relative to goals.

Profits relative to competitors. ^*

Market share of main products.

New Product Performance (ρ₂ = .96). ^c Regarding your firm’s recent major new product introduction, please use the scale provided to rate each aspect of performance (e.g., Gatignon and Xuereb 1997):

Sales relative to objective.

Profit margin relative to objective. ^#

Return on assets relative to objective.

Return on investment relative to objective.^#

Market acceptance.

General success.

Technological Turbulence (ρ₁ = .90; ρ₂ = .89). Please use the scale provided to indicate the extent to which you agree or disagree with the following statements (e.g., Jaworski and Kohli 1993):

Technology in our industry is rapidly changing.

Technological change provides big opportunities in our business. ^#

The basic technologies in our business have remained unchanged for some time. ^r

It is very difficult to forecast what will happen in our technologies over the next five years. ^*

There have been many changes in our industry because of recent technological breakthroughs.

Market Dynamism (ρ₁ = .78; ρ₂ = .72). Please use the scale provided to indicate the extent to which you agree or disagree with the following statements (e.g., Jaworski and Kohli 1993):

In our business, customers’ preferences tend to change radically.

Our customers tend to look for something new all the time.

We are seeing demand for our products from customers who have never bought them before.

In our business the customer base has remained unchanged. ^r

^aWe calculate reliability as $ \rho = {\left[ {\sum\limits_{{i = 1}}^I {{\lambda_i}} } \right]^2}/\left( {{{\left[ {\sum\limits_{{i = 1}}^I {{\lambda_i}} } \right]}^2} + \left[ {\sum\limits_{{i = i}}^I {1 - \lambda_i^2} } \right]} \right) $, where ρ is reliability, λ is the standardized factor loading (obtained by standardizing the latent variable), and I denotes the number of items in the scale (Fornell and Larcker 1981). We use ρ₁ to denote the reliability for Study 1 and ρ₂ to represent the reliability for Study 2. We use the asymptotic covariance matrix as input for the confirmatory factor analysis.

^bAll items are measured on a five-point Likert scale, with 1 = strongly agree and 5 = strongly disagree, unless otherwise indicated.

^cAll items are measured on a five-point Likert scale, with 1 = very low and 5 = very high.

^dAll items are measured on a five-point Likert scale, with 1 = not at all and 7 = to a large extent.

^rReverse-scored item.

^*Item deleted in Study 1.

^#Item deleted in Study 2.

Appendix C: Latent-class confirmatory factor analysis and factor scores

Before we can meaningfully establish that the structural parameters (i.e., influence of the latent explanatory variables on the latent dependent variable) vary across regimes, we must establish measurement invariance, namely, that the measures (e.g., factor loadings, residual variances) do not vary across latent classes (Meredith 1993; Steenkamp and Baumgartner 1998). Steenkamp and Baumgartner (1998) discuss various types of measurement invariance, such that the appropriate form depends on the research goals. Because factor score calculations require factor loadings and residual variances, we resort to latent-class confirmatory factor analysis, in which we allow the (1) factor loadings, (2) error variances, and (3) both factor loadings and error variances to vary across the latent classes (e.g., Meredith 1993).

Typically, the measurement model can be represented as:

$$ x = \nu + {\Lambda_x}\xi + \delta, $$

(C1)

where, x is a (q × 1) vector of observable indicator variables, ξ is the (n × 1) vector of latent constructs, Λ_x is the (q × n) matrix of factor loadings, ν is the (p × 1) vector of measurement intercepts, and δ is (p × 1) vector of measurement errors.

Assuming c latent classes, a latent-class-specific measurement model can be written as (e.g., Jedidi et al. 1997; Yung 1997):

$$ x|c = {\nu^c} + \Lambda_x^c{\xi^c} + {\delta^c}, $$

(C2)

where, E(ξ ^c) = τ ^c, $ E[({\xi^c} - {\tau^c})({\xi^c} - {\tau^c})\prime = {\Phi^c} $, $ E({\delta^c}{\delta^{{c\prime }}}) = \Theta_{\delta }^c $, and we assume that E(δ ^c) = 0.

Thus, conditional on latent-class membership, the conditional mean vector (μ ^c) is:

$$ {\mu^c} = {\nu^c} + \Lambda_x^c{\xi^c}. $$

(C3)

The corresponding covariance matrix Σ^c (q × q) is given by:

$$ {\Sigma^c} = \Lambda_x^c{\Phi^c}\Lambda_x^{{c\prime }} + \Theta_{\delta }^c. $$

(C4)

We assume that Δ|c, the joint vector of observable indicators that is conditional on latent class c, has a multivariate normal distribution, which indicates the unconditional distribution of Δ is a finite mixture of these conditionals, such that

$$ \Delta \sim \sum\limits_{{c = 1}}^C {{\kappa^c}{f^c}\left( {\Delta |{\mu^c}{\Sigma^c}} \right)}, $$

(C5)

where κ = (κ ¹, κ ², … κ ^C)′ is vector of C mixing proportions, κ ^c>0 ∀ c = 1, 2, …, C, and $ \sum\limits_{{c = 1}}^C {{\kappa^c} = 1} $.

Thus, the likelihood function for c latent classes and sample size of N can be written:

$$ \prod\limits_{{i = 1}}^N {\left[ {\sum\limits_{{c = 1}}^C {{\kappa^c}{{(2\pi )}^{{ - q/2}}}|{\Sigma^c}{|^{{ - 1/2}}}{e^{{\{ - 1/2({\Delta_i} - {\mu^c})\prime {{({\Sigma^c})}^{{ - 1}}}({\Delta_i} - {\mu^c})\} }}}} } \right]} . $$

(C6)

We use the E-M algorithm (Dempster et al. 1977) to maximize the log-likelihood (Jedidi et al. 1997) and calculate posterior latent-class membership according to the Bayes rule; we also evaluate the entropy of separation of the latent class (see Appendix A). We use the BIC (Schwarz 1978), as suggested by Jedidi et al. (1997), to select the optimal number of latent classes. Measurement invariance exists if a single latent-class solution outperforms a solution with more than one latent class.

Factor scores: accounting for measurement error

Given our multi-item measures, we account for measurement errors by using factor scores to compute an aggregate score for each construct before proceeding to estimate the composed error structure models. However, the indeterminacy of factor scores makes it difficult to select the appropriate method to calculate the scores (e.g., Acito and Anderson 1986; Lastovicka and Thamodaran 1991). One recommended approach is blockwise factor scores, which we use, where multiple factor scores are utilized in tandem to obtain the factor scores that most closely approximate the measurement error corrected covariance matrix among latent constructs (McDonald and Burr 1967; Skrondal and Laake 2001). In a recent study of blockwise factor scoring, Skrondal and Laake (2001) recommend the use of regression factor scores for explanatory latent variables and Bartlett factor scores for response (dependent) latent variables. For the explanatory latent variables, the regression factor scores can be calculated as (Thomson 1934; Thurstone 1935):

$$ {{\rm A}_{\xi }} = [\Phi \Lambda_x^{\prime }{({\Lambda_x}\Phi \Lambda_x^{\prime })^{{ - 1}}}{\rm X}\prime ]\prime $$

(C7)

where, ξ represents the explanatory latent variables, A_ξ is the (n * k) matrix of factor scores for the explanatory latent variables such that n is the sample size and k is the number of explanatory latent variables, Φ (k * k) is the covariance matrix of ξ, Λ_x is the matrix of factor loadings (q * k) such that q is the number of items used to measure the k explanatory latent variables, and X is the (n * q) data matrix of items for the explanatory variables.

For the dependent variables, the Bartlett factor scores can be calculated as (Bartlett 1938; Bartlett 1937):

$$ {{\rm A}_{\eta }} = {(\Lambda_y^{\prime }\Theta_{\varepsilon }^{{ - 1}}{\Lambda_y})^{{ - 1}}}\Lambda_y^{\prime }\Theta_{\varepsilon }^{{ - 1}}\Upsilon \prime $$

(C8)

where, η represents the dependent latent variables, A_η is the (n * j) matrix of factor scores for the dependent latent variables such that n is the sample size and j is the number of dependent latent variables, Λ_y is the matrix of factor loadings (p * j) such that p is the number of items used to measure the j dependent latent variables , Θ_ε is the (p * p) covariance matrix of errors of measurement for items used to measure the dependent latent variables, Y is the (n * p) data matrix for the items of the dependent variables.

Matrices for Study 1

The matrices for Study1 for market orientation, technological turbulence, and market dynamism in that order are (we use these two to calculate regression factor scores—Eq. D7; the items for latent constructs are in the same order as these are depicted in Appendix B):

$$ {{{\Lambda }}_x} = \left[ {\begin{array}{*{20}{c}} {.6610} {.0000} {.0000} \\ {.7760} {.0000} {.0000} \\ {.8196} {.0000} {.0000} \\ {.7018} {.0000} {.0000} \\ {.6810} {.0000} {.0000} \\ {.5067} {.0000} {.0000} \\ {.0000} {.9311} {.0000} \\ {.0000} {.7692} {.0000} \\ {.0000} {.6477} {.0000} \\ {.0000} { - .5236} {.0000} \\ {.0000} {.0000} {.7996} \\ {.0000} {.0000} {.7366} \\ {.0000} {.0000} {.7715} \\ {.0000} {.0000} { - .6099} \\ \end{array} } \right] $$

$$ {{\Phi }} = \left[ {\begin{array}{*{20}{c}} {1.0000} { - .1542} { - .0526} \\ { - .1542} {1.0000} {.3183} \\ { - .0526} {.3183} {1.0000} \\ \end{array} } \right] $$

Bartlett factor scores for performance using Eq. D8:

$$ \Lambda _{y} = {\left[ {.8826,\;.7104,\;.8389,\;.6387} \right]} $$

$$ \Theta_{\varepsilon } = {\left[ {.2210,\;.4953,\;.2962,\;.5997} \right]} $$

Matrices for Study 2

The matrices for Study 2 for market orientation, technological turbulence, and market dynamism in that order are (we use these two to calculate regression factor scores—equation D7; the items for latent constructs are in the same order as these are depicted in Appendix B):

$$ {{{\Lambda }}_x} = \left[ {\begin{array}{*{20}{c}} {.5240} {.0000} {.0000} \\ {.6114} {.0000} {.0000} \\ {.8347} {.0000} {.0000} \\ {.7986} {.0000} {.0000} \\ {.6297} {.0000} {.0000} \\ {.6856} {.0000} {.0000} \\ {.0000} {.7306} {.0000} \\ {.0000} { - .6317} {.0000} \\ {.0000} {.8094} {.0000} \\ {.0000} { - .7855} {.0000} \\ {.0000} {.0000} {.7094} \\ {.0000} {.0000} {.6650} \\ {.0000} {.0000} {.7464} \\ {.0000} {.0000} { - .6192} \\ \end{array} } \right] $$

$$ {{\Phi }} = \left[ {\begin{array}{*{20}{c}} {1.0000} { - .0070} { - .0866} \\ { - .0070} {1.0000} {.4069} \\ { - .0866} {.4069} {1.0000} \\ \end{array} } \right] $$

Bartlett factor scores for performance using Eq. D8:

$$ \Lambda_{y} = {\left[ {.5751,\;.5927,\;.6453,\;.7834} \right]}$$

$$ \Theta_{\varepsilon } = {\left[ {.6693,\;.6488,\;.5836,\;.3863} \right]} $$

References

Acito, F., & Anderson, R. D. (1986). A simulation study of factor score indeterminancy. Journal of Marketing Research, 23, 111–118.Google Scholar
Aitkin, M., & Rubin, D. B. (1985). Estimation and hypothesis testing in finite mixture distributions. Journal Royal Statistical Society B., 47, 67–75.Google Scholar
Akaike, H. (1974). A new look at statistical model identification. IEEE Transactions Automatic Control, 19, 716–723.Google Scholar
Ansari, A., Jedidi, K., & Jagpal, S. (2000). A hierarchical Bayesian methodology for treating heterogeneity in structural equation models. Marketing Science, 19(4), 328–347.CrossRefGoogle Scholar
Appiah-Adu, K., & Ranchhod, A. (1998). Market orientation and performance in biotechnology industry: an exploratory empirical analysis. Technology Analysis & Strategic Management, 10(2), 197–210.CrossRefGoogle Scholar
Arrow, K. (1962). Economic welfare and the allocation of resources for invention. The rate and direction of economic activity. Princeton: Princeton University Press.Google Scholar
Atuahene-Gima, K., Slater, S. F., & Olson, E. M. (2005). The contingent value of responsive and proactive market orientations for new product program performance. Journal of Product Innovation Management, 22(6), 464–482.CrossRefGoogle Scholar
Bartlett, M.S. (1938). Methods of estimating mental factors. Nature, 141, 609–610.Google Scholar
Bartlett, M.S. (1937). The statistical conception of mental factors. British Journal of Psychology, 28, 97–104.Google Scholar
Blau, P. M., & Schoenherr, R. (1971). The structure of organizations. New York: Basic Books.Google Scholar
Bozdogan, H. (1987). Model selection and the Akaike’s Information Criterion (AIC): the general theory and its analytic extensions. Psychometrika, 52(3), 345–370.CrossRefGoogle Scholar
Bozdogan, H. (1994). Mixture model cluster analysis using model selection criteria and a new information measure of complexity in multivariate statistical modeling, Hamparsum Bozdogan, Ed. Vol. 2. Boston, MA: Kluwer Academic Press.Google Scholar
Bozdogan, Hamparsum (2000). Akaike’s information criterion and recent developments in information complexity. Journal of Mathematical Psychology, 44(1), 62–91.Google Scholar
Brown, S. L., & Eisenhardt, K. M. (1995). Product development: past research, present findings, and future directions. Academy of Management Review, 20, 343–378.Google Scholar
Brown, S. L., & Eisenhardt, K. (1998). Competing on the edge: Strategy as structured chaos. Boston: Harvard Business School Press.Google Scholar
Cano, C. R., Carrillat, F. A., & Jaramillo, F. (2004). A meta-analysis of the relationship between market orientation and business performance: evidence from five continents. International Journal of Research in Marketing, 21(2), 179–200.CrossRefGoogle Scholar
Carroll, G. R., & Hannan, M. T. (2000). The demography of corporations and industries. Princeton: Princeton University Press.Google Scholar
Chandrashekaran, M., Mehta, R., Chandrashekaran, R., & Grewal, R. (1999). Market motives, distinctive capabilities, and domestic inertia: a hybrid model of innovation generation. Journal of Marketing Research, 36, 95–112.CrossRefGoogle Scholar
Chandy, R. K., & Tellis, G. J. (1998). Organizing for radical product innovation: the overlooked role of willingness to cannibalize. Journal of Marketing Research, 35, 474–487.CrossRefGoogle Scholar
Chandy, R. K., & Tellis, G. J. (2000). The incumbent’s curse? Incumbency, size, and radical product innovation. Journal of Marketing, 64(3), 1–18.CrossRefGoogle Scholar
Day, G. S. (1999). The market driven organization: understanding, attracting, and keeping valuable customers. New York: Free.Google Scholar
Day, G. (2006). Aligning the organization with the market. MIT Sloan Management Review, 48(1), 41–49.Google Scholar
Dayton, C. M., & MacReady, G. B. (1988). Concomitant variables latent class models. Journal of the American Statistical Association, 83(401), 173–178.CrossRefGoogle Scholar
Dempster, A. P., M. N. Laird, & D.B. Rubin (1977). Maximum likelihood estimation from incomplete data via the E-M algorithm. Journal of the Royal Statistical Society, Series B, 39, 1–38.Google Scholar
DeSarbo, W. S., & Cron, W. L. (1988). A maximum likelihood methodology for clusterwise linear regression. Journal of Classification, 5(2), 249–282.CrossRefGoogle Scholar
DeSarbo, W. S., & Grewal, R. (2008). Hybrid strategic groups. Strategic Management Journal, 29(3), 293–317.CrossRefGoogle Scholar
DeSarbo, W. S., Jedidi, K., & Sinha, I. (2001). Customer value analysis in a heterogeneous market. Strategic Management Journal, 22, 845–857.CrossRefGoogle Scholar
DeSarbo, W. S., Di Benedetto, C. A., Jedidi, K., & Song, M. (2006). Identifying sources of heterogeneity for empirically deriving strategic types: a constrained finite-mixture structural-equation methodology. Management Science, 52(6), 909–924.CrossRefGoogle Scholar
DeSarbo, W. S., Grewal, R., & Wang, R. (2009). Dynamic strategic groups: spatial evolutionary paths. Strategic Management Journal, 30, 1420–1439.CrossRefGoogle Scholar
Deshpandé, R., & Farley, J. U. (1998). Measuring market orientation: generalization and synthesis. Journal of Market-Focused Management, 2, 213–232.CrossRefGoogle Scholar
Deshpandé, R., & Webster, F. E. (1989). Organizational culture and marketing: defining the research agenda. Journal of Marketing, 53, 3–15.CrossRefGoogle Scholar
Doty, H. D., & Glick, W. H. (1998). Common method bias: does common methods variance really bias results? Organizational Research Methods, 1(4), 374–406.CrossRefGoogle Scholar
Dranove, D., Peteraf, M. A., & Shanley, M. (1998). Do strategic groups exist: an economic framework for analysis. Strategic Management Journal, 19(11), 1029–1044.CrossRefGoogle Scholar
Eisenhardt, K. M., & Martin, J. A. (2000). Dynamic capabilities: what are they.? Strategic Management Journal, 21, 1105–1121.CrossRefGoogle Scholar
Evans, S. J. (1991). Strategic flexibility for high technology maneuvers: a conceptual framework. Journal of Management Studies, 28, 69–89.CrossRefGoogle Scholar
Everitt, B. S., & Hand, D. J. (1981). Finite mixture distributions. London: Chapman and Hall.CrossRefGoogle Scholar
Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: algebra and statistics. Journal of Marketing Research, 18, 382–388.CrossRefGoogle Scholar
Galbraith, J. R. (1974). Organization design: an information processing view. Interfaces, 4, 28–36.CrossRefGoogle Scholar
Gatignon, H., & Xuereb, J.-M. (1997). Strategic orientation of the firm and new product performance. Journal of Marketing Research, 34, 77–90.CrossRefGoogle Scholar
Grewal, R., & Tansuhaj, P. (2001). Building organizational capabilities for managing economic crises: the role of market orientation and strategic flexibility. Journal of Marketing, 65, 67–80.CrossRefGoogle Scholar
Grewal, R., Cote, J. A., & Baumgartner, H. (2004a). Multicollinearity and measurement error in structural equation models: implications for theory testing. Marketing Science, 23(4), 519–529.CrossRefGoogle Scholar
Grewal, R., Mehta, R., & Kardes, F. R. (2004b). The timing of repeat purchases of consumer durable goods: the role of functional bases of consumer attitudes. Journal of Marketing Research, 41(1), 101.CrossRefGoogle Scholar
Grewal, R., Lilien, G. L., & Mallapragada, G. (2006). Location, location, location: how network embeddedness affects project success in open source systems. Management Science, 52(7), 1043–1056.CrossRefGoogle Scholar
Grinstein, A. (2008). The effect of market orientation and its components on innovation consequences: a meta-analysis. Journal of the Academy of Marketing Science, 36(2), 166–173.CrossRefGoogle Scholar
Gupta, S., & Chintagunta, P. (1994). On using demographic variables to determine segment membership in logit mixture models. Journal of Marketing Research, 16, 128–136.Google Scholar
Han, J. K., Kim, N., & Srivastava, R. K. (1998). Market orientation and organizational performance: is innovation a missing link. Journal of Marketing, 62, 30–45.CrossRefGoogle Scholar
Hannan, M. T., & Freeman, J. (1989). Organizational ecology. Cambridge: Harvard University Press.Google Scholar
Hansen, G. A., & Wernerfelt, B. (1990). Determinants of firm performance: the relative importance of economic and organizational factors. Strategic Management Journal, 10(5), 399–411.CrossRefGoogle Scholar
Haveman, H. A. (1993). Organizational size and change: diversification in the savings and loan industry after deregulation. Administrative Science Quarterly, 38(1), 20–50.CrossRefGoogle Scholar
Helsen, K., Jedidi, K., & DeSarbo, W. S. (1993). A new approach to country segmentation utilizing multinational diffusion patterns. Journal of Marketing, 57, 60–71.Google Scholar
Henard, D. H., & Szymanski, D. M. (2001). Why some new products are more successful than others. Journal of Marketing Research, 38, 362–375.CrossRefGoogle Scholar
Homburg, C., & Pflesser, C. (2000). A multiple-layer model of market-oriented organizational culture: measurement issues and performance outcomes. Journal of Marketing Research, 37, 449–362.CrossRefGoogle Scholar
Hrebiniak, L. G., & Joyce, W. F. (1985). Organizational adaptation: strategic choice and environmental determinism. Administrative Science Quarterly, 30, 336–349.CrossRefGoogle Scholar
Hu, L.-T., & Bentler, P. M. (1999). Cutoff criteria for fit indices in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55.CrossRefGoogle Scholar
Hult, G. T. M., David, J. K., & Slater, S. F. (2005). Market orientation and performance: an integration of disparate approaches. Strategic Management Journal, 26(12), 1173–1181.CrossRefGoogle Scholar
Hurley, R. F., & Hult, G. T. M. (1998). Innovation, market orientation, and organizational learning: an integration and empirical examination. Journal of Marketing, 62(3), 42–54.CrossRefGoogle Scholar
Hutchinson, J. W., Kamakura, W. A., & Lynch, J. G. (2000). Unobserved heterogeneity as an alternative explanation for ‘reversal’ effects in behavioral research. Journal of Consumer Research, 27, 323–344.CrossRefGoogle Scholar
Jacobson, R. (1990). Unobservable effects and business performance. Marketing Science, 9(1), 74–85.CrossRefGoogle Scholar
Jaworski, B. J., & Kohli, A. K. (1993). Market orientation: antecedents and consequences. Journal of Marketing, 57, 53–70.CrossRefGoogle Scholar
Jaworski, B., Kohli, A. K., & Sahay, A. (2000). Market-driven versus driving markets. Journal of the Academy of Marketing Science, 28, 45–54.CrossRefGoogle Scholar
Jayachandran, S., & Varadarajan, R. (2006). Does success diminish competitive responsiveness? Reconciling conflicting perspectives. Journal of the Academy of Marketing Science, 34(3), 284–294.CrossRefGoogle Scholar
Jedidi, K., Jagpal, H. S., & DeSarbo, W. S. (1997). Finite-mixture structural equation models for response-based segmentation and unobserved heterogeneity. Marketing Science, 16(1), 39–59.CrossRefGoogle Scholar
Johnson, J. L., Sohi, R. S., & Grewal, R. (2004). The role of relational knowledge stores in interfirm partnering. Journal of Marketing, 68, 21–36.CrossRefGoogle Scholar
Kamakura, Wagner A., Michel Wedel, & Jagadish Agrawal (1994). Concomitant variable latent class models for conjoint analysis. International Journal of Research in Marketing, 11, 451–464.Google Scholar
Ketchen, D. J., Hult, G. T. M., & Slater, S. F. (2007). Toward greater understanding of market orientation and the resource-based view. Strategic Management Journal, 28(9), 961–964.CrossRefGoogle Scholar
Kimberly, J. (1976). Organization size and the structuralist perspective: a review, critique, and proposal. Administrative Science Quarterly, 21, 571–597.CrossRefGoogle Scholar
Kirca, A. H., Jayachandran, S., & Bearden, W. O. (2005). Market orientation: a meta-analytic review and assessment of its antecedents and impact on performance. Journal of Marketing, 69(2), 24–41.CrossRefGoogle Scholar
Kohli, A. K., & Jaworski, B. J. (1990). Market orientation: the construct, research propositions, and managerial implications. Journal of Marketing, 54, 1–18.CrossRefGoogle Scholar
Kohli, A. K., Jaworski, B. J., & Kumar, A. (1993). MARKOR: a measure of market orientation. Journal of Marketing Research, 30, 467–477.CrossRefGoogle Scholar
Lastovicka, J. L., & Thamodaran, K. (1991). Common factor score estimates in multiple regression problems. Journal of Marketing Research, 28, 105–112.CrossRefGoogle Scholar
Lawrence, P. R., & Lorsch, J. E. (1986). Organization and environment. Cambridge: Harvard Business School Press.Google Scholar
Lee, R. P., & Grewal, R. (2004). Strategic responses to new technologies and their impact on firm performance. Journal of Marketing, 68, 157–171.CrossRefGoogle Scholar
Lindell, M. K., & Whitney, D. J. (2001). Accounting for common method variance in cross-sectional research designs. Journal of Applied Psychology, 86(1), 114–121.CrossRefGoogle Scholar
Mason, C. H., & Perreault, W. D. (1991). Collinearity, power, and interpretation of multiple regression analysis. Journal of Marketing Research, 28, 268–280.CrossRefGoogle Scholar
Matsuno, K., & Mentzer, J. T. (2000). The effect of strategy type on market orientation–performance relationship. Journal of Marketing, 64, 1–16.CrossRefGoogle Scholar
McDonald, Roderick P. & E.J. Burr (1967). A comparison of four methods of constructing factor scores. Psychometrika, 32(4), 381–401.Google Scholar
McGee, J., & Thomas, H. (1986). Strategic groups: theory, research and taxonomy. Strategic Management Journal, 7(2), 141–160.CrossRefGoogle Scholar
Meredith, W. (1993). Measurement invariance, factor analysis and factorial invariance. Psychometrika, 58(4), 525–543.CrossRefGoogle Scholar
Moorman, C., & Rust, R. T. (1999). The role of marketing. Journal of Marketing, 63(Special Issue), 180–197.CrossRefGoogle Scholar
Morgan, N. A., Vorhies, D. W., & Mason, C. H. (2009). Market orientation, marketing capabilities and firm performance. Strategic Management Journal, 30(8), 909–920.CrossRefGoogle Scholar
Narver, J. C., & Slater, S. F. (1990). The effect of a market orientation on business profitability. Journal of Marketing, 54(4), 20–35.CrossRefGoogle Scholar
Narver, J. C., Slater, S. F., & MacLachlan, D. L. (2004). Responsive and proactive market orientation and new product success. Journal of Product Innovation Management, 21(5), 334–347.CrossRefGoogle Scholar
Nelson, R. R., & Winter, S. G. (1982). An evolutionary theory of economic change. Cambridge: Harvard University Press.Google Scholar
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory. New York: McGraw Hill.Google Scholar
Podasakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903.CrossRefGoogle Scholar
Ramaswamy, V., DeSarbo, W. S., Reibstein, D. J., & Robinson, W. T. (1993). An empirical pooling approach for estimating marketing mix elasticities with PIMS data. Marketing Science, 12(1), 103–124.CrossRefGoogle Scholar
Rossi, P. E., & Allenby, G. M. (2003). Bayesian statistics and marketing. Marketing Science, 22(3), 304–328.CrossRefGoogle Scholar
Ruekert, R. W. (1992). Developing a market orientation: an organizational strategy perspective. International Journal of Research in Marketing, 9, 225–245.CrossRefGoogle Scholar
Schwarz, G. (1978). Estimating the dimensions of a model. Annals of Statistics, 6, 461–464.Google Scholar
Sclove, S. L. (1987). Application of model selection criteria to some problems in multivariate analysis. Psychometrika, 52(3), 333–343.Google Scholar
Scott, W. R. (1995). Organizations: Rational, natural, and open systems. Englewood Cliffs: Prentice Hall.Google Scholar
Sigauw, J. A., Brown, G., & Widing, R. E. (1994). The influence of market orientation of the firm on sales force behavior and attitudes. Journal of Marketing Research, 31, 106–116.CrossRefGoogle Scholar
Skrondal, A., & Laake, P. (2001). Regression among factor scores. Psychometrika, 66, 563–576.Google Scholar
Slater, S. F., & Narver, J. C. (1994). Does competitive environment moderate the market orientation–performance relationship? Journal of Marketing, 58, 46–55.CrossRefGoogle Scholar
Slater, S. F., & Narver, J. C. (1995). Market orientation and the learning organization. Journal of Marketing, 59, 63–74.CrossRefGoogle Scholar
Slater, S. F., & Narver, J. C. (2000). The positive effect of a market orientation on business profitability: a balanced replication. Journal of Business Research, 48(1), 69–73.CrossRefGoogle Scholar
Srinivasan, R. (2006). Dual distribution and intangible firm value: franchising in restaurant chains. Journal of Marketing, 70(3), 120–135.CrossRefGoogle Scholar
Steenkamp, JB. E. M., & Baumgartner, H. (1998). Assessing measurement invariance in cross-national consumer research. Journal of Consumer Research, 25(1), 78–90.CrossRefGoogle Scholar
Teece, D., Pisano, G., & Schuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533.CrossRefGoogle Scholar
Thomson, G.H. (1934). The meaning of i in the estimate of g. British Journal of Psychology, 25, 92–99.Google Scholar
Thurstone, L.L. (1935). The vectors of mind. Chicago. IL: University of Chicago Press.Google Scholar
Titterington, D., Smith, A., & Makov, U. (1985). Statistical analysis of finite mixture distributions. Chichester: Wiley.Google Scholar
Verhoef, P. C., & Leeflang, P. S. H. (2009). Understanding the marketing department’s influence within the firm. Journal of Marketing, 73(2), 14–37.CrossRefGoogle Scholar
Voss, G. B., & Voss, Z. G. (2000). Strategic orientation and firm performance in an artistic environment. Journal of Marketing, 64, 67–83.CrossRefGoogle Scholar
Wedel, M., & Kamakura, W. A. (2000). Market segmentation: Conceptual and methodological foundations. Boston: Kluwer.CrossRefGoogle Scholar
Wei, Y., & Atuahene-Gima, K. (2009). The moderating role of reward systems in the relationship between market orientation and new product performance in China. International Journal of Research in Marketing, 26(2), 89–96.CrossRefGoogle Scholar
Wernerfelt, B. (1984). A resource-based view of the firm. Strategic Management Journal, 5, 171–180.CrossRefGoogle Scholar
Wernerfelt, B., & Montgomery, C. A. (1988). Tobin’s q and the importance of focus on firm performance. American Economic Review, 78(1), 246–250.Google Scholar
Workman, J. P. (1993). Marketing’s limited role in new product development in one computer systems firm. Journal of Marketing Research, 30, 405–421.CrossRefGoogle Scholar
Yung, Y-F. (1997). Finite mixtures in confirmatory factor-analysis models. Psychometrika, 62(3), 297–330.Google Scholar

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Rajdeep Grewal
- 1
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Murali Chandrashekaran
- 2
Jean L. Johnson
- 3
Girish Mallapragada
- 4

1.Department of Marketing, Smeal College of Business AdministrationPennsylvania State UniversityUniversity ParkUSA
2.Robert H. Lee Graduate School, at the Sauder School of BusinessUniversity of British ColumbiaVancouverCanada
3.College of Business and EconomicsWashington State UniversityPullmanUSA
4.Kelley School of BusinessIndiana UniversityBloomingtonUSA

Environments, unobserved heterogeneity, and the effect of market orientation on outcomes for high-tech firms

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