Are Market Orientation and Learning Orientation Necessary For Superior Organizational Performance? Mark Anthony Farrell* and Edward Oczkowski
Working Paper 52/02 December 2002
About the Authors Dr Mark Anthony Farrell, BA Warw, MSc Manc, PhD Monash, is an Associate Professor of Marketing, in the School of Management, Charles Sturt University, Wagga Wagga.
Dr Edward Oczkowski, MEc ANU, BEc PhD La Trobe, is an Associate Professor of Economics, in the School of Management, Charles Sturt University, Wagga Wagga.
*All correspondence to the first author, School of Management, Charles Sturt University, Wagga Wagga, NSW, PO BOX 588, NSW, 2678, AUSTRALIA. Tel: (02) 69 33 27 56; Fax: (02) 69 33 29 30; Email:
[email protected]
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Dr Pamela Mathews Professor Reg Mathews Dr Arthur Sweeney
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Working papers, as the title suggests, should not necessarily be taken as completed works or final expressions of opinions.
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Table of Contents Abstract
4
1. Introduction
5
2. Market Orientation
7
3. Market Orientation and Organizational Learning
7
4. Learning Orientation, Market Orientation and Organizational Performance
8
5. Research Question
11
6. Methodology
12
6.1 Data Collection
12
6.2 Measures
13
7. Preliminary Data Analysis
14
8. Encompassing Non-Nested Testing
16
9. Results
18
10. Discussion
20
11. Study Limitations and Further Research Priorities
23
12. Conclusion
24
13. Appendices
25
14. References
28
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Abstract There exists contradictory theoretical arguments and counter- intuitive empirical results regarding the market orientation, learning orientation and organizational performance nexus. We ask, can we simplify relations in this nexus? This study analyzes data from Australian organisations and employs non-nested encompassing tests.
Contrary to recent findings
extolling the virtues of a learning orientation, our results suggest that a market orientation may be the pre-eminent strategy to achieve superior organizational performance.
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1. Introduction An increasing number of empirical studies have demonstrated that an improvement in the level of market orientation will lead to superior organizational performance, (Narver and Slater, 1990; Oczkowski and Farrell, 1998; Slater and Narver, 2000). Despite these relatively consistent findings, a number of authors are questioning the benefits of being market oriented, suggesting that there may be several limitations to a market orientation. Hamel and Prahalad (1991, p. 83) suggest tha t market oriented firms may suffer from the "tyranny of the served market", ignoring or missing markets and competitors. Slater and Narver (1995, p. 68) argue that market oriented firms may fail to identify and capitalize on the latent needs of customers, due to their excessive focus on expressed needs. Similarly, several authors, (Achrol 1991; Dickson 1992; Kanter 1989; Webster 1992) argue that market oriented organizations may underestimate the potential contribution of other learning sources that possess knowledge useful to the organization.
This small, but increasing criticism of the market orientation literature suggests that organizations should aim to become learning oriented if they are to achieve a sustainable competitive advantage, (Slater and Narver, 1995). For example, Dickson (1996) suggests that learning is pre-eminent over other resources because only it enables firms to maintain longterm competitive advantages by continuously improving market information processing. Moreover, argues Dickson, (1996), a strong market orientation can be readily copied but the learning environment that organizes and translates the output of these behaviours into a comparative advantage, cannot (Baker and Sinkula, 1999a, p. 411).
In a recent study that exa mines both the effects of a learning orientation and a market orientation on performance, Baker and Sinkula (1999a, p. 423) find that “the direct independent effects of learning orientation on all three performance measures suggests, as others have theorized, that market oriented processes are necessary but not sufficient to maintain competitive advantage.”
However, although the emerging body of literature
concerned with learning orientation and organizational performance are yielding promising results, (Baker and Sinkula 1999a, b; Farrell, 2000) there appears to be some confusion as to whether a market orientation or a learning orientation is the pre-eminent strategy to achieve superior organizational performance. For example, referring to a market orientation and a learning orientation, Baker and Sinkula (1999a, p. 422) state that “In the absence of one or the
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other, it would be better for a firm to have a strong market orientation,” (emphasis added). Conversely, Baker and Sinkula (1999b, p. 305) argue and find support for the proposition that a market orientation does not have a direct effect on organizational performance, “that implies the potential pre-eminence of learning orientation over market orientation.”
Given the contradictory theoretical arguments being espoused, and the surprisingly counterintuitive
empirical
results
regarding
market
orientation,
learning
orientation
and
organizational performance, this study seeks to apply the Occam's Razor maxim 1 , (Keuzenkamp and McAleer, 1995) to the market/learning orientation - organizational performance nexus. Can we simplify relations in this nexus? Are all relations in this nexus necessary to adequately explain organizational performance?
This study examines and compares the performance of two rival models: the market orientation - organizational performance (MO-OP) model and the learning orientation organizational performance (LO-OP) model.
Specifically we ask, can either model
statistically encompass the other (Mizon and Richard, 1986)? That is, can one model explain all the salient features of the other model? If encompassing can be demonstrated then the need for organizations to pursue both a market and learning orientation maybe unnecessary for superior organizational performance.
The study contributes to the literature by building upon the conceptual work by Slater and Narver (1995), and empirical studies by Baker and Sinkula (1999a, b) and Farrell (2000). The explicit contribution of this study is to help clarify the specific relative role that both a market and learning orientation may play in enhancing organizational performance. In particular this study will assess whether adequate simpler models for the market/learning orientation organizational performance nexus can be identified.
The remainder of the paper is organized as follows. In the next sections, we briefly review the literature on market orientation, organizational learning, and the nexus between organizational learning, market orientation and performance. This is followed by the research question for the study, and the research methodology of the study. Results are then presented, along with conclusions and directions for further research.
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Entities are not to be multiplied beyond necessity.
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2. Market Orientation The stream of research that has developed, following the Narver and Slater (1990) and Jaworski and Kohli (1993) studies, has found that a market orientation is positively related to profitability (Narver and Slater, 1990; Ruekert 1992; Pitt, Caruana, and Berthon, 1996; Kumar, Subramanian and Yauger, 1998; Oczkowski and Farrell, 1998). In general, there is overwhelming support to suggest that being market oriented is beneficial for organizations, and that it is in the interests of companies seeking to become and remain competitive to increase their levels of market orientation. As Jaworski and Kohli (1993, p. 64) state, “managers should strive to improve the market orientation of their businesses in their efforts to attain higher business performance.”
However, despite the increasing body of evidence regarding the benefits of market orientation, there is a "shift" in the literature which argues that "creating a market orientation is only a start", Slater and Narver (1995, p. 63). Some scholars, argue that ‘organizational learning is seen as the key to future organizational success’ (Lukas, Hult and Ferrell 1996, p. 233). Indeed, as was articulated earlier, Slater and Narver (1995) argue that firms should become learning oriented if they are to compete successfully in the long run.
3. Market Orientation and Organizational Learning The emerging literature that examines both market orientation and organizational learning has struggled to reach a consensus regarding the nature of causality between the two constructs. Slater and Narver (1995) argue that market orie ntation is the principal foundation on which organizational learning occurs. In other words, market-oriented organizations provide the cultural framework from which a learning orientation can develop. Farrell (2000) argues that a market orientation is the underlying set of organizational values (Slater and Narver, 1995, Slater, Narver and Tietje, 1998) from which a learning orientation is developed. Thus, argues Farrell (2000, p. 208) “market oriented firms are effective in producing knowledge and this culture of knowledge production inevitably leads to knowledge questioning values”. In short, argues Farrell (2000), organizations that are able to appreciate the value of timely and relevant information (market oriented) will already be intelligent enough to challenge existing assumptions about the way the market operates (learning oriented).
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In contrast to the above view of causality, Day (1994) argues that a market-oriented or market-driven approach can emerge only if learning processes are examined and altered in a way that enables firms to "learn to learn" about markets.
Thus, in Day’s (1994) view,
organizational learning is the foundation for a market-oriented or market-driven strategic orientation (Bell, Whitwell and Lukas, 2002, p. 81). According to Bell, Whitwell and Lukas (2002) the issue of causality regarding market orientation and learning orientation remains unresolved. However, they suggest that both schools of thought agree on one issue, and that is the view that organizational learning and market orientation are mutually dependent.
4. Learning Orientation, Market Orientation and Organizational Performance Despite the numerous empirical studies that clearly demonstrate that market orientation leads to high levels of performance, a body of literature is emerging that questions the relationship between market orientation and organizational performance. The original exponents of the benefits of being market oriented (Slater and Narver 1995) express several concerns with what they term the possible limitations of a market orientation. For example, Slater and Narver (1995, p. 68) speculate that a:
“market orientation may not encourage a sufficient willingness to take risks … the result of narrowly focusing market intelligence efforts on current customers and competitors thus ignoring emerging markets and/or competitors.”
This view is similar to that of Hamel and Prahalad (1991), that a market orientation limits a company’s focus to only the expressed needs of customers, and therefore only adaptive learning. Slater and Narver’s (1995, p. 68) criticism that a “narrow construction of market orientation could lead to learning only within traditional boundaries” is premised on the argument that business might “underestimate the potential contributions of other learning sources such as suppliers, businesses in different industries, consultants, government agencies, and others that possess knowledge valuable to the business” (Slater and Narver 1995, p.68).
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Baker and Sinkula (1999a, p. 412) echo the concerns of Slater and Narver (1995), stating that “organizations have a higher likelihood of creating a sustainable competitive advantage if they have both a strong market orientation and a strong learning orientation.” However, they express concern that a market orientation may only lead to adaptive learning, whereas a learning orientation leads to generative learning.
Adaptive learning, argues Baker and
Sinkula (1999a, p. 412) “is capable of facilitating incremental innovation, but it is not intrinsically capable of facilitating discontinuous innovation”. Conversely, argues Baker and Sinkula (1999a, p. 412), a learning orientation “directly affects a firm’s ability to challenge old assumptions about the market and how a firm should be organised to address it”. Baker and Sinkula (1999a) also argue that (i) market orientation may lead to adaptive (or even generative) learning but learning is not a necessary outcome of the process; (ii) success of market-oriented firms may lead to resistance to learning. That is, in successful firms, argue Baker and Sinkula (1999a, p. 413) “the presumed effectiveness of past actions and interpretations is reinforced by repeated success, and the ensuing complacency breeds rejection of discrepant information that conflicts with conventional wisdom”.
Regarding the virtues of organizational learning, Slater and Narver (1995, p.66) argue that:
“organizational learning is valuable to a firm’s customers … because it focuses on understanding and effectively satisfying their expressed and latent needs, through new products, services, and ways of doing business … this should lead directly to superior outcomes, such as greater new product success, superior customer retention, higher customer defined quality, and ultimately superior growth and/or profitability”.
Farrell (1999) found that a learning orientation has a positive effect on organizational commitment, esprit de corps, and organizational innovativeness. Baker and Sinkula (1999b), based on the findings by Han, Kim and Srivastava (1998) argue that market orientation will have an indirect effect on organizational performance through product innovation, but that there is no direct effect of market orientation on organizational performance. This line of reasoning is based on a small number of previous studies.
For example, Hart and
Diamantopoulos, (1993) report no significant relationship between market orientation and organizational performance. Greenley (1995) reports mixed results. Jaworski and Kohli (1993) find a positive relationship between market orientation and organizational 9
performance, but no significant relationship between market orientation and market share. The findings support all of the study hypotheses, leading Baker and Sinkula (1999b, p. 305) to conclude tha t:
“… the key implication of this research is that a strong learning orientation may be more important to the firm than a strong market orientation … learning orientation had a direct effect on organizational performance but market orientation did not … This is a new result that implies the potential pre-eminence of learning orientation over market orientation” (emphasis added).
Despite the arguments and findings from the above study Baker and Sinkula (1999a) argue that there is a positive relationship between an organization’s market orientation and its overall performance, and that market orientation/learning orientation has a direct relationship with change in relative market share, and overall performance. Baker and Sinkula (1999a) also argue that a learning orientation moderates the relationship between market orientation and change in relative market share, and market orientation and overall performance. In general, results support the majority of the study hypotheses, with learning orientation having a greater relative impact (as evidenced by the higher beta values) on the dependent variables of, change in relative market share, overall performance, and new product success. However, no support was found for the hypothesis that the strength of the relationship between market orientation and overall performance would be heightened as learning orientation increased. In a later study, Farrell (2000) also found that both a market orientation and a learning orientation directly affect performance, with the beta values suggesting a slightly stronger effect for a learning orientation.
However, the conclusions that Baker and Sinkula (1999a) derive are unclear as to whether a market orientation or a learning orientation is preferable for superior performance. For example, Baker and Sinkula (1999a, p. 421) state that
“…In the absence of one or the other, it would be better for a firm to have a strong market orientation. A strong learning orientation may lead to an occasional “home run” but the beneficial effect of
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breakthrough innovations may be short- lived if they are not followed up by market-oriented processes …” (emphasis added).
Conversely, Baker and Sinkula (1999b, p.301) argue that a “learning orientation is a more pervasive resource than market orientation because it has bearing on more than marketingrelated activities in the firm”, and that a “learning orientation may be more important to the firm than a strong market orientation," (p.305).
In sum, the above literature review suggests the following competing models:
1.
organizational learning is the foundation for market orientation which in turn leads to organizational performance, (LO? MO? OP), Day (1994).
2.
market orientation is a precondition to a learning orientation, which in turn, has a positive relationship with organizational performance, (MO? LO? OP), (Slater and Narver, 1995; Farrell 2000).
3.
the comprehensive model where both a market orientation and a learning orientation have a direct positive relationship with organizational performance, (MO and LO? OP), (Baker and Sinkula, 1999a; Farrell 2000).
5. Research Question It is clear from the above discussion that there exists a degree of confusion concerning the direct effects of a learning orientation and a market orientation on organizational performance.
Whilst the above discussion illustrates a number of competing market
orientation, learning orientation, organizational performance models, the purpose of this study is to address the question by Baker and Sinkula (1999a,b) as to whether a market orientation (model 1) or a learning orientation (model 2) is the pre-eminent strategy to achieve superior organizational performance? Indirectly, by addressing this question through the proposed encompassing testing strategy, the comprehensive model (3) is also examined. Moreover, given the recursive nature of the identified relationships and the focus on the determinants of OP, the explicit modeling of the antecedents of MO and LO is not needed for consistent parameter estimation and testing. In other words, models (1) and (2) have no feedback loops, (i.e. OP does not influence LO or MO) and therefore the consistent estimation of models for
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OP does not require the simultaneous estimation of models explaining MO and LO. The focus needs only be on model (1) MO? OP versus model (2) LO? OP.
To help clarify and reconcile these contradictory theoretical arguments and empirical findings regarding the MO-OP and LO-OP relationships, our strategy is to apply principles of parsimony and simplicity, (Keuzenkamp and McAleer, 1995). In particular, given these rival models is it necessary for organizations to pursue behaviours associated with both a market and learning orientation to achieve superior organizational performance? The encompassing testing principle is employed to address this issue, see Mizon and Richard (1986). Encompassing is the notion that a superior model can explain all the salient features of a rival model. In other words, a superior model encompasses a rival if it can explain the results of the rival model, this implies that the rival model contains no information which can improve the superior model. If encompassing is confirmed then this makes the rival model redundant as all its information is contained in the superior model. Encompassing offers "a progressive research strategy" in the sense that a model which encompasses all previously developed rival models effectively summarises the current state of knowledge, Hendry (1995, chs 14 & 15).
Given the above, we pose the following research question:
Q1 : Given two rival models: (1) market orientation -organizational performance (MO–OP), and (2) learning orientation organizational performance (LO–OP), can it be demonstrated that one model encompasses the other?
6. Methodology 6.1 Data Collection The study involved a mail survey of the top 2,000 manufacturing organizations in Australia, as defined by annual revenue, using the Dun and Bradstreet database. If the organization had only one strategic business unit (SBU), respondents were asked to focus on the overall firm as the unit of analysis. If the organization had multiple SBU’s, “respondents were asked to focus on their SBU as the unit of analysis” (Moorman and Rust 1999, p. 185).
A
questionnaire and personal letter was mailed to the marketing manager/CEO/managing
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director of the respective organizations. These respondents were chosen because they would have suitable knowledge of both market and learning orientation, and also knowledge of the performance of the business. Use of key informants in such surveys is common, Phillips (1981), with Mitchell (1994) providing useful guidelines on selecting key informants within organizations. A second mail out two weeks later was conducted in order to improve the response rate. One hundred and forty three questionnaires were not completed, due to one of the following; returned to sender due to an incorrect address, the person had left the company, or that it was company policy not to complete such surveys. In total, 486 useable questionnaires were returned, resulting in an effective net response rate of 26.2%. A number of respondents failed to complete all questions resulting in a total of 340 'complete cases' on which the data analysis is performed.
Informants were told that the purpose of the survey was to investigate state of the art business practices. In order to minimize problems relating to memory, the questionnaire required respondents to complete the items based on the activities of the organization during the previous twelve months. Independent sample t-tests for differences between means of the key variables were conducted to check for non-response bias. Tests were performed between early and late respondents.
As per convention, (Armstrong and Overton, 1977), it is
postulated that the late respondents are relatively dis- interested respondents, similar in nature to non-respondents. All t- tests indicated an absence of significant differences between the means at a 0.1% level of significance. Thus the sample appears to be relatively free from non-response bias.
6.2 Measures The measure of market orientation used was MKTOR, (Narver and Slater 1990), given its overall psychometric qualities and superiority to the MARKOR scale (Jaworski and Kohli, 1993), (in the Australian context, see Oczkowski and Farrell 1998). It consists of three subconstructs: customer orientation, competitor orientation and interfunctional coordination. Consistent with previous studies it is operationalized as a single measure.
For learning
orientation, we use the measure developed by Sinkula, Baker and Noordewier (1997). It also consists of three sub-constructs: commitment to learning, open- mindedness, and shared vision. It is also operationalized as a single measure. There are five dimensions of business performance relative to all other competitors in the organization’s principal served market segment over the past year: (a) customer retention; (b) new product success; (c) sales growth; 13
(d) return on investment; (e) overall performance. These five dimensions served as individual indicators in a five-item scale named business performance, and were also employed individually in separate models. As with previous studies, Narver and Slater (1990), and Oczkowski and Farrell (1998) several variables were also included as control variables in analyzing the effect of a market and learning orientation on performance. In brief, these variables were as follows, relative size, relative cost, ease of entry, supplier power, buyer power, market growth, competitive intensity, market turbulence, technological turbulence, (see Oczkowski and Farrell 1998, p. 355, for a detailed explanation). In sum the rival models are, (1) performance is a function of market orientation and a set of control variables; (2) performance is a function of learning orientation and a set of control variables.
Six
regressions are estimated for each model, one for each of the five single item measures of performance and one for the five item summated measure termed business performance.
7. Preliminary Data Analysis The rival models are analyzed and estimated via Bollen' (1996) two-stage least squares (2SLS) estimator for latent variable models. This consistent estimator is an alternative to the standard maximum likelihood estimator (MLE) for structural equation models (SEM). Given the focus on model testing and encompassing in this paper, the 2SLS estimator is particularly attractive as it easily permits diagnostic testing for assumptions such as adequacy of scale items (instruments), model specification error, heteroscedasticity and non- nested model alternatives, see Bollen (1996), Pesaran and Taylor (1999) and Oczkowski (2001). These issues are routinely ignored in standard MLE applications for SEM, including previous studies in the market and learning orientation literature. Moreover, 2SLS possibly has better small sample properties than MLE, requires fewer distributional assumptions, is computationally simpler to implement and isolates specification errors to the equation of interest, see Bollen (1996).
Simply put the 2SLS estimator requires the specification of a scaling (or reference) variable for each latent independent variable, this acts as the regressor and the remaining non-scaling items act as instruments in the 2SLS regression. For the dependent variable either a scaling variable or a summated (factor-score) based scale can be employed, as measurement error in the dependent variable does not effect the consistency of the 2SLS estimator.
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Initially the rival models were estimated using all the items in the chosen scales. These models were subject to overidentifying restrictions tests, see Davidson and MacKinnon (1993, p236), and Bollen (1996, p 117), and the heteroscedasticity robust version, Wooldridge (1995, p 83). Unlike the chi-square test for MLE SEMs, these tests are useful in identifying the source specification error and aid particularly well in choosing which inadequate scale items to remove from the measures. These tests regress the residuals from the estimated model against all the instruments, if any instrument has a significant role in explaining these residuals then this can be identified as an invalid instrument or as evidence of specification error. In our case a number of items were identified as inadequate and subsequent analysis was conducted on refined measures for the key constructs. The refined measures and their descriptive statistics are outlined in Table 1. The employed items are listed in the appendix.
To provide further evidence of the validity of the refined measures a scale validation procedure was accomplished using exploratory factor analysis, and confirmatory factor analysis (CFA). After the deletion of items, the resulting exploratory factor analysis of the items used to measure market orientation and learning orientation, produced an unrestricted six-factor structure, with the items loading on the a priori dimensions, and accounting for 71% of the variance explained.
Next, we followed the procedure by Noble and Mokwa (1999), and performed a series of separate confirmatory factor analyses on the construct measures and related items using the EQS program. In general, the properties of the measures are acceptable, with the majority of the key constructs having overall acceptable fit indices, (see Table 1). The average variance extracted for the sub-constructs of learning orientation are as follows: vision sharing (.72), commitment to learning (.73), open mindedness (.65). The average variance extracted for the sub-constructs of market orientation are as follows: customer orientation (.52). competitor orientation (.53), inter- functional coordination (.56).
The average variance extracted for
business performance was (.45). The average variance extracted for the control variables was as follows, technological turbulence, (.69), market turbulence, (.48), and competitive intensity (.45).
With regard discriminant validity we tested whether the correlation coefficients
between all pair-wise constructs (and sub-constructs) was unity or not (Steenkamp and Van Trijp, 1991). In all cases (sub) constructs were found not to be perfectly correlated at the 1% level. For example, the χ 2 (df = 1) difference test statistic was 12.42 for testing the null of a
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perfect correlation between the market and learning orientation constructs. Thus, results provide evidence of both convergent and discriminant validity.
Table 1: Descriptive and Goodness of Fit Statistics Reliability
0.931 (0.153)
χ2 (d.f) 36.62 (5)
0.96 1.41
0.943 (0.083)
87.86 (32)
0.879
4.42 4.58
1.09 1.36
0.954 (0.104)
108.84 (32)
0.929
Relative Size Relative Cost Ease of Entry Supplier Power Buyer Power Market Growth
4.07 4.18 3.08 3.90 4.06 14.48
2.34 2.02 1.75 1.20 1.20 10.83
Competitive Intensity (4 items) Scaling Item Market Turbulence (3 items) Scaling Item Technological Turbulence (3 items) Scaling Item All measures are single items unless specified
4.15 5.27 4.01 4.15 4.35 4.15 otherwise.
Measure
Mean
Standard Deviation
CFI (RMSEA)
Business Performance (5 items) Customer Retention New Product Success Sales Growth Return on Investment Overall Performance
4.29 5.52 4.36 3.79 3.59 4.18
1.17 1.24 1.54 1.75 1.83 1.64
Market Orientation (10 items) Scaling Item
4.76 4.77
Learning Orientation (10 items) Scaling Item
0.779
1.01 0.998 2.64 0.443 1.55 (0.041) (2) 1.28 0.715 1.63 1.57 0.868 1.77 CFA results for over-identified models based on
robust ma ximum likelihood. Cronbach Alphas are reported for reliabilities. Items listed in the data appendix.
8. Encompassing Non-Nested Testing The hypothesized rival models represent non-nested alternatives (i.e., one model can not be gained via suitable parameter restrictions from the other) and therefore appropriately defined non-nested testing principles must be performed. Oczkowski and Farrell (1998) developed Cox and encompassing non-nested tests relevant for alternative latent variable models by combining the techniques developed by Smith (1992) and Bollen (1996). Oczkowski (2002) evaluated these tests using Monte Carlo techniques and found that the 'augmented' encompassing test performed better than various other tests and has good small sample properties for certain design parameters.
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To describe the mechanics of the augmented encompassing test consider the following simplified models. Label the two competing models as H 0 the null and H 1 the alternative: H0 : H1 :
y = β 0ξ 0 + u 0 y = β 1ξ1 + u1
(1)
where, y is an observed dependent variable; ξ i are latent unobserved variables; β i are estimable parameters; and u i are independently and identically distributed error terms with zero mean and constant variance. Assume that the corresponding measurement models for ξ i are: X 0 i = λ0 i ξ 0 + δ 0 i X 1i = λ1iξ1 + δ 1i
i = 1,......k 0 i = 1,......k1
(2)
where, X ij are the observed indicators; λ ij are estimable factor loadings; and δ ij are independently and identically distributed error terms with zero mean and constant variance. All measurement error terms are assumed to be independent of each other and with the error terms from the structural equations. Let X 01 for ξ 0 and X 11 for ξ1 be the identified scaling variables 2 , then the remaining items represent the instruments: Z 0 = ( X 02 ,.... X 0k 0 1 )' and Z 1 = ( X 12, X 13 ,...X 1k1 )' .
The augmented encompassing test H 0 against H 1 requires the
following two regressions: (i)
OLS regression: X 11 on Z1 and save predictions p ,
(ii)
2SLS regression: y on X 01 and p ( Z 0 and p as instruments), and test for the significance of p .
If p is statistically insignificant, then H 0 is not rejected by H 1 , or H 0 does encompass H 1 . The tests described are for a specific null H 0 against a specific alternative H 1 . For two rival models, paired tests should be performed where the null and alternative models alternate.
The presented test is a 'mean parametric' encompassing test, (Hendry, 1995, ch. 15). Conceptually, if a relation is assumed to implicitly exist between the two independent variables, then parameter restrictions implied by one model can be imposed on the other. If these implied restrictions are valid then this provides evidence of encompassing. That is, if 2
We choose scaling variables by regressing each item against all others in a measure and choosing as scaling variable that with the highest R 2 .
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imposing the structure of (say) model B on model A does not affect the performance of model A, then model A is said to encompass model B. In other words, model A encompasses model B, if model A can predict the parameters of model B by only estimating model A.
If
encompassing can be demonstrated then effectively, model B is redundant, all its information is contained in model A.
9. Results The results for the augmented encompassing tests 3 for all six measures of OP are presented in Table 2. Table 2 indicates that for all six measures of organizational performance, the MO-OP model always encompasses the LO-OP model. The LO-OP model encompasses the MO-OP model only for two measures (i.e., new product success and sales growth). In other words, for customer retention, return of investment, overall performance and business performance the MO-OP model encompasses the LO-OP model, while the LO-OP model cannot encompass the MO-OP model. For these four measures the MO-OP is clearly superior and it would be erroneous to employ the LO-OP model. For the remaining two measures (new product success and sales growth) both models encompass each other and therefore either are acceptable specifications for these two measures, no useful discrimination exists here.
Table 2: Non-Nested Augmented Encompassing 2SLS Tests Dependent Variable
Market Learning Conclusion Orientation Orientation Null Model Null Model Customer Retention 0.308 3.216** Accept MO (0.758) (0.001) Reject LO New Product Success 0.139 0.428 Accept MO (0.890) (0.669) Accept LO Sales Growth 0.419 1.533 Accept MO (0.675) (0.125) Accept LO Return on Investment -0.555 2.846** Accept MO (0.579) (0.004) Reject LO Overall Performance 0.954 2.712** Accept MO (0.340) (0.007) Reject LO Business Performance 0.330 3.072** Accept MO (Ave: 5 items) (0.741) (0.002) Reject LO Reported values are asymptotic t-statistics, distributed as N(0,1). Customer retention and new product success results based on White's heteroscedastic consistent covariance matrix. P-values are in parenthesis, * p < 0.05 ** p < 0.01
3
Rival models for all six measures of performance are free from specification error, invalid instruments and are tested for heteroscedasticity. If heteroscedasticity was found to be a problem, RESET and non-nested test results are based on White's heteroscedastic consistent covarinace matrix, and Wooldridge (1995) is employed for the OIR test: see the notes to table 4 for test details.
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Table 3: Organizational Performance - Market and Learning Orientation Estimates Dependent Variable
Model 1 Model 2 Model 3 Market Learning Market and Learning Orientation Orientation Orientation Customer Retention 0.346** 0.186** MO: 0.341** (3.75) (4.41) (2.96) LO: 0.012 (0.16) New Product Success 0.083 0.044 MO: 0.106 (0.85) (0.85) (0.64) LO: -0.011 (-0.12) Sales Growth 0.235* 0.142 MO: 0.132 (1.04) (2.31) (1.90) LO: 0.082 (0.83) Return on Investment 0.372** 0.153 MO: 0.339* (2.37) (3.29) (1.82) LO: -0.016 (-0.15) Overall Performance 0.424** 0.279** MO: 0.314* (2.44) (4.11) (3.71) LO: 0.122 (1.23) Business Performance 0.292** 0.161** MO: 0.246** (2.83) (Ave: 5 items) (4.21) (3.17) LO: 0.038 (0.56) T-ratios are in parentheses. Customer retention and new product success results based on White's heteroscedastic consistent covariance matrix. * p < 0.05 ** p < 0.01
Summary 2SLS coefficient estimates for the three models: MO-OP, LO-OP and MO and LO – OP are presented in Table 3. 4 An example of the full 2SLS regression results is presented in Table 4 for the multi- item business performance summated scale. Models 1 and 2 in Table 3 illustrate the importance of MO and LO when they appear alone for all OP measures, except new product success. However, when both MO and LO are included in the performance model (model 3) MO remains significant (for four measures) but LO becomes insignificant for all measures.
As expected, the results from the encompassing tests (Table 2) are
consistent with the coefficient estimates for MO and LO (Table 3). That is, for the four cases where MO encompasses LO but LO does not encompass MO, MO is significant but LO is insignificant, see model 3 in Table 3. While for the two cases (new product success and sales growth) where both models encompass each other, both MO and LO are insignificant for model 3.
4
All the discussed independent variables are included in the estimated models, but to simplify presentation only the coefficients associated with LO and MO are presented in table 3.
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Table 4: 2SLS Regressions for Business Performance Variables Constant Market Orientation
Model 1 2.355** (4.98) 0.292** (4.21)
0.046 (1.46) -0.040 (-1.91) -0.001 (-0.02) 0.031 (0.54) 0.100 (1.93) 0.028** (4.88) -0.178** (-2.60) 0.026 (0.41) 0.012 (0.22) 0.237
0.161** (3.17) 0.074* (2.53) -0.052 (-1.58) 0.003 (0.09) 0.002 (0.05) 0.108* (2.15) 0.031** (5.59) -0.118 (-1.87) 0.047 (0.75) -0.033 (-0.61) 0.219
Model 3 2.308** (4.96) 0.246** (2.83) 0.038 (0.56) 0.052 (1.68) -0.040 (-1.21) 0.001 (0.01) 0.021 (0.37) 0.104* (2.03) 0.029** (5.06) -0.148* (-2.24) 0.030 (0.49) -0.004 (-0.16) 0.235
-0.253 -0.612 14.60
-0.530 -0.106 14.34
-0.021 -0.263 31.53*
Learning Orientation Relative Size Relative Cost Ease of Entry Supplier Power Buyer Power Market Growth Competitive Intensity Market Turbulence Technological Turbulence
GR 2 RESET Het OIR
Model 2 2.799** (6.31)
T-ratios are in parentheses. GR 2 is the generalized R 2 for IV regressions, Pesaran and Smith (1994). RESET is the FF2 specification error test and Het is the HET1 heteroscedasticity test for IV regressions in Pesaran and Taylor (1999), both distributed as N(0,1). OIR is the over-identifying restrictions test Davidson and Mackinnon (1993, p 232) based on the Gauss-Newton regression and is distributed as: χ 2 (12) for models (1) and (2), and χ 2 ( 20) for model (3). * p < 0.05 ** p < 0.01
10. Discussion The purpose of this study was to examine and compare the performance of two riva l models: the market orientation–organizational performance (MO–OP) model, and the learning orientation–organizational performance (LO–OP) model. Analysis of the data, employing two–stage least squares (2SLS) estimators for latent variables demonstrates that on the single item performance measures of customer retention, return on investment, overall performance, and the multi- item measure of business performance, market orientation was able to encompass learning orientation, but learning orientation was no t able to encompass market orientation.
That is, market orientation was better able to explain variations in those 20
indicators, than was learning orientation. With regards the single item measures of new product success and sales growth, both market orientation and learning orientation were equally effective in explaining variations in the dependent variable of interest. Thus, on balance, these findings suggest that market orientation is the pre–eminent strategy, given its overall explanatory power regarding the aforementioned dependent variables of interest. These findings support those by Baker and Sinkula (1999a), and contradict those by Baker and Sinkula (1999b).
Given the above findings, our view is that with respect to organizational performance, “in the absence of one or the other,” (Baker and Sinkula, 1999a, p. 427) it is preferable for a firm to have a strong market orientation.
Our reasoning is based on the accumulated empirical
evidence, and what we perceive to be the strengths of market orie nted firms, which we shall now articulate.
In their desire to develop a promising stream of literature on organizational learning, we believe that marketing scholars have made flawed criticisms of market-orientation. For example, we disagree with Slater and Narver’s (1995) argument that market-oriented firms may "not take risks, only focus their market intelligence on current customers and competitors, thus ignoring emerging markets and/or competitors."
This contradicts the
powerful argument developed by Levitt (1960) that market-oriented firms will challenge existing assumptions about customers, products, and industry paradigms. Moreover, we know of no study that demonstrates that market-oriented firms do not consider the potential contributions of othe r learning sources, leading to learning only within traditional boundaries. This line of reasoning by Slater and Narver (1995) ignores the argument by Kohli and Jaworski (1990). They conducted field interviews with 62 managers from 47 organisations. The respondents stated that customer focus "goes far beyond customer research" and that “market intelligence is a broader concept in that it includes consideration of (1) exogenous market factors (eg. competition regulation) that affect customers needs and preferences, and (2) current as well as future needs of customers” (Kohli and Jaworski, 1990, p. 3). In other words, state Kohli and Jaworski (1990, p. 5):
“…the important point is that generation of market intelligence does not stop at obtaining customer opinions, but also involves careful analysis and
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subsequent interpretation of the forces that impinge on customer needs and preferences.”
Moreover, Slater and Narver (1995, p. 71) argue that “learning organizations are guided by a shared vision that focuses the energies of organizational members on creating superior value for customers.” However, in an earlier paper, Narver and Slater (1990, p.21) state that a market orientation is “the organizational culture that most effectively creates the necessary behaviours for the creation of superior value for buyers and, thus, continuous superior performance for the business”. In other words, both orientations are theorised to have almost identical effects.
We believe that a strong market orientation is likely to breed the type of adaptive learning that can keep a firm competitive in a dynamic market. We also argue that organizational learning, without the accompaniment of market oriented behaviours may lead to declines in organizational performance. That is, although the NPD process may indicate that a new product should not be released on the market, a strong learning oriented organisation may ignore these signals. It is also our view that market-oriented firms that ‘pass’ on speculative new products will be more successful than learning oriented firms that simply have ‘faith’ and ‘belief’ in the product. Even if we accept the proposition that "generative learning is frame breaking and more likely to lead to competitive advantage than adaptive learning," (Slater and Narver 1995), generative learning is difficult to sustain. Moreover, as Slater and Narver (1995, p. 64) state, eventually, the knowledge of innovations inevitably diffuses to competitors.
With regards the competing models discussed earlier in the paper, we argue that the operative causal flow is (LO? MO? OP). Despite the fact that our findings suggest that MO? OP is superior to LO? OP, it is still important to have a learning orientation to mediate the influence of a market orientation. In other words, the effect of LO is mediated by MO, i.e. LO values strengthen and shape MO behaviours. From this perspective, a learning orientation is the underlying set of organizational values (Sinkula, Baker and Noordeweir, 1997) from which a market orientation is developed. This is based on the following assumptions: (i) values drive behaviour (McClelland, 1985); (ii) a market orientation is a culture that creates market oriented behaviours (Slater and Narver, 1995); (iii) organizational learning ultimately manifests itself through internal and external organizational actions (such as a market 22
orientation) that reflect the operationalization of changes in theory in use (Argyris and SchÖ n, 1978; Senge, 1990).
11. Study Limitations and Further Research Prio rities As with the studies by Baker and Sinkula (1999a,b), this study measured learning orientation, but can only infer that organisations have actually learned. Future research should consider ways to operationalize measures of adaptive and generative learning, and model their effects as intervening constructs that mediate the effects of market/learning orientation on organizational performance. In other words, is it the case that a market orientation only leads to adaptive learning, and if so, what is the effect on performance? Similarly, are learning oriented organisations capable of generative learning, and what is the effect on performance?
A fruitful line of inquiry would be to build upon the conceptual work by Baker and Sinkula (2002) and their concept of different types of marketing firms. In brief, research should examine phase II (strong market orientation and weak learning orientation) and phase III marketing firms, (strong market orientation and strong learning orientation). It would be interesting to determine whether our results hold for the different types of marketing firms as outlined above.
Our study should also be replicated in other contexts. For example, is it the case that a market orientation is always able to outperform a learning orientation with regard to performance? Although our study did not include a specific measure of market share, as per the Baker and Sinkula (1999a) study, we did measure sales growth relative to all other competitors in the organisations principal served market segment. As such, this is a useful proxy for market share. Our results suggest that a market orientation can encompass a learning orientation in explaining variations in market share, which begs the question, is a market orientation sufficient to achieve a sustainable competitive advantage? Our results would seem to indicate that the answer is yes. However, future studies should consider a longitudinal approach to measure both changes in market/learning orientation and changes in market share over time.
Our research contradicts the findings by Baker and Sinkula (1999a,b) in that we find that a market orientation is able to outperform a learning orientation in relation to organizational performance. Contrary to the Baker and Sinkula (1999a,b) studies, which utilized the
23
MARKOR scale, our study adopted the MKTOR scale, based on previous studies, (see Farrell and Oczkowski, 1997, Oczkowski and Farrell, 1998). We believe that MKTOR, with its emphasis on orientations towards the customer and competitor, captures the construct of market orientation better than MARKOR, with its emphasis on specific behaviours concerned with the gathering, dissemination and response to market intelligence. Given this, it may be the case that the studies by Baker and Sinkula (1999a,b) found stronger effects of learning orientation due to a value deficit in the context of the MARKOR scale. Future research should examine this issue by simultaneously incorporating the MARKOR and MKTOR scales.
Future research should also address some of the criticisms of a market orientation. Studies that examine whether market oriented firms have an equal focus on both expressed and latent needs of consumers would be a useful contribution. Similarly, research that examines whether market oriented firms underestimate the potential contribution of other learning sources and whether market oriented firms become complacent and reject "discrepant information that conflicts with conventional wisdom", (Baker and Sinkula, 1999a) would help shed light on the comparative merits of a market/learning orientation.
12. Conclusion In conclusion, our results support Baker and Sinkula (1999a) that "in the absence of one or the other it would be better for a firm to have a strong market orientation." The results clearly demonstrate that the MO-OP model in general, encompasses the LO-OP model, but the LOOP model does not encompass the MO-OP model.
Our results suggest that for those
organisations seeking to improve their performance, a market orientation may be more important than a learning orientation, not because it is easier to develop, but because a strong market orientation reflects both behaviour and values, whilst a learning orientation only reflects values. That is, a strong market orientation embraces learning orientation values and channels them into behaviours that directly enhance the firm.
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13. Appendices Measures Market Orientation 1. Our business objectives are driven by customer satisfaction * 2. We monitor our level of commitment and orientation to serving customers’ needs # 3. Our strategy for competitive advantage is based on our understanding of customer needs 4. Our business strategies are driven by our beliefs about how we can create greater value for customers 5. We measure customer satisfaction systematically and frequently* 6. We give close attention to after-sales service 7. Our salespeople share information within our business concerning competitors’ strategies 8. We respond to competitive actions that threaten us 9. We target customers and customer groups where we have, or can develop, a competitive advantage * 10. The top management team regularly discusses competitors’ strengths and strategies 11. Our top managers from every function visit our current and prospective customers 12. We communicate information about our successful and unsuccessful customer experiences across all business functions * 13. All of our business functions (eg. marketing/sales, manufacturing, R&D, finance/accounting, etc.) are integrated in serving the needs of our target markets 14. All of our managers understand how everyone in our company can contribute to creating customer value
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Learning Orientation 1. Managers basically agree that our organisation’s ability to learn is the key to our competitive advantage 2. The basic values of this organisation include learning as key to improvement # 3. The sense around here is that employee learning is an investment, not an expense 4. Learning in my organisation is seen as a key commodity necessary to guarantee organizational survival 5. There is a commonality of purpose in my organisation 6. There is total agreement on our organizational vision across all levels, functions and divisions 7. All employees are committed to the goals of this organisation 8. Employees view themselves as partners in charting the direction of the organisation * 9. We are not afraid to reflect critically on the shared assumptions we have made about our customers 10. Personnel in this enterprise realise that the very way they perceive the marketplace must be continually questioned 11. We rarely collectively question our own biases about the way we interpret customer information
Market Turbulence 1. In our kind of business, customers’ product preferences change quite a bit over time # 2. Our customers tend to look for new product all the time 3. We are witnessing demand for our products and services from customers who never bought them before *
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4. New customers tend to have product-related needs that are different from those of our existing customers 5. We cater to many of the same customers that we used to in the past *
Competitive Intensity 1. Competition in our industry is cutthroat # 2. There are many ‘promotion wars’ in our industry * 3. Anything that one competitor can offer, others can match readily * 4. Price competition is the hallmark of our industry 5. One hears of a new competitive move almost every day 6. Our competitors are relatively weak @
Technological Turbulence 1. The technology in our industry is changing rapidly 2. Technological changes provide big opportunities in our industry 3. A large number of new product ideas have been made possible through technological breakthroughs in our industry # 4. Technological developments in our industry are rather minor *
* Denotes deleted item @ Denotes reverse scored # Denotes scaling item
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Contributing to the Faculty of Commerce Working Paper Series 1. Contributions are welcome from current and past staff and students of the Faculty of Commerce, and others interested in contributing.
2. A copy of the article should be emailed to: Dr Arthur Sweeney Lecturer in Marketing School of Marketing and Management Charles Sturt University Bathurst NSW 2795 Australia Phone: 026338 4294 Fax: 026338 4769 Email:
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2001 Working Papers 1/01 Wickrarnasekera, R. and Barnbeny, G. Born Globals Within the Australian Wine Industry: An Exploratory Study.
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3/01 Sweeney, A. and Perry, C. High Tech -High Touch: Can the Internet Facilitate Customer Relationships?
4/01 Morrison, M. and Boyle, K. Comparative Reliability of Rank and Choice Data in Stated Preference Models.
5/01 Mathews, M.R. Externalities Revisited: The Environmental Equity Account.
6/01 Mathews, M.R. Whither (or Wither) Accounting Education in the New Mi1lenium
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8/01 Gunasekara, C. Project-Based Workplace Learning: A Case Study.
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2002 Working Papers (continued) 24/02 Smith, G. & Smith, J. An Examination of the Relationship between the Cultural and Accounting Environments: Accounting Authority Structures and Goodwill (Part I)
25/02 Smith, G. & Smith, J. An Examination of the Relationship between the Cultural and Accounting Environments: Accounting Authority Structures and Goodwill (Part 2)
26/02 POH-LING H. & Mathews, M. R. Comprehensiveness of Disclosure of Non-Financial Companies: Some Empirical Evidence from Annual Reports (2000) of Companies Listed on the Kuala Lumpur Stock Exchange
27/02 Bisman, J. Accounting, A Convict and Society: A Case Study in Australian Accounting History
28/02 Millmow, A. The Power and Triumph of Economic Ideas: Australian Economists in the Thirties
29/02 Kent, J. The Public Sector Accounting Standards Board: Constructing Regulatory Space for Accounting Change
30/02 Kent, J. The PSASB: The Accounting Profession in Regulatory Space
31/02 Petzke, S & Murphy, D. A Survey of Small Regional Businesses and the Impact of the GST
32/02 Gunasekara, C. Employer Branding – The Perils of Transdisciplinary Extension
33/02 Menchin, M. The Carver Model of Corporate Governance in Community Based Organisations
34/02 Mathews, P. Mentoring in an academic environment: Towards increased efficiency in the use of scarce resources.
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2002 Working Papers (continued) 35/02 Millmow, A. The Teaching of Economics in Schools: A Problem in the Making?
36/02 Oczkowski, E. Modelling Winegrape Prices in Disequilibrium.
37/02 Fish, A. Building and Sustaining Business and Personal Associations - Value Orientations and the Identification of Cross-Border Managers.
38/02 Sharkie, R. The Knowledge Sharing Filter.
39/02 Latham, C. The Legal Nature of Domain Names.
40/02 Macklin, R. The efficacy of Agnes Heller’s Moral Philosophy for HRMs
41/02 Goela, N & Bisman, J.E. Financial and accounting aspects of leasing decision- making in Australia
42/02 Bartley, M., Le Marchant, M. & Simmons, K. Implementation of a Real World Information Technology Project at the University Level
43/02 Sharma, K. Pattern and Determinants of Intra-Industry Trade in Trans-Tasman Bilateral Trade
44/02 Fuming, J. An Exploratory Investigation of International Pharmaceutical Firms’ FDI Decision into China
45/02 Higson, A. Continuous reporting and auditing: Conceptual considerations
46/02 Higson, A. An exploration of the financial reporting expectations gap
47/02 Sharma, K. Horizontal and Vertical Intra-Industry Trade in Australian Manufacturing: Does Trade Liberalization have any Impact?
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2002 Working Papers (continued) 48/02 Sharma, K. The Impact of Policy Reforms on Labour Productivity, Price Cost Margins and Total Factor Productivity: The Nepalese Experience
49/02 Fuming, J. Factors Affecting the Effectiveness of FDI Venture Operations in China: A Comparison between Early-Entrants and Late-Entrants
50/02 Jin, M & Li, F. The recent development of accounting education in China
51/02 Hardy, C & Marzilli, C. Making audit critical: Relevance lost or found? 52/02 Farrell, M.A. & Oczkowski, E. Are Market Orientation and Learning Orientation Necessary For Superior Organizational Performance?
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