Internal Market Orientation in Nonprofit Service Organisations: Construct Validation and Scale Finalisation

Transcription

1 Internal Market Orientation in Nonprofit Service Organisations: Construct Validation and Scale Finalisation Nonprofit service organisations role and relevance in contemporary societies is increasing due to withdrawing governments and failing markets. Their success mainly owes to the frontline employees who deliver welfare services to the beneficiaries. However, these organisations find it difficult to attract and retain quality frontline employees, which negatively affect their mission achievement activities. We propose that the notion of internal market orientation (IMO), originally developed for for-profit service organisations, has relevance and application in nonprofit service organisation and it can help address the problem. To this end, we revise, refine and validate Lings and Greenley s (2005) IMO construct and scale in the nonprofit context and show that IMO improves staff retention and beneficiary satisfaction. We propose and establish that IMO in nonprofit contexts should be treated as the higher-order reflective construct having three first-order behavioral outcomes: intelligence generation, intelligence dissemination, and responsiveness. The study is based on data from nonprofit service organisations delivering welfare services to underprivileged sections of the society.

2 Internal Market Orientation in Nonprofit Service Organisations: Construct Validation and Scale Finalisation It profits us to strengthen nonprofits. Peter F. Drucker, The Wall Street Journal, 1991 Introduction Nonprofit organisations (referred to as NPOs) are predominantly service organisations. They emerge from state and market failures to provide services to a section of citizens (Weisbord, 1977), who are considered their target customers (Modi & Mishra, 2010). Like forprofit service organisations, NPOs have a high centrality of boundary spanning frontline employees referred to as field staff who deliver welfare services to beneficiaries (also referred to as target customers). Working in NPOs is quite challenging as it often involves working in remote places and/or working with marginalised, poor, or disadvantaged communities. The very nature and purpose of NPOs does not allow them to pay salaries anywhere comparable to their counterparts in for-profit sector. As a consequence, NPOs find it difficult not only to attract but also to retain the human resources of required quality. Such a situation negatively influences their mission related services. Internal market orientation (IMO) involves a set of behaviors aimed at generating requisite intelligence pertaining to the wants and needs of employees and responding to the same more efficiently and effectively than the competitors (Lings, 2004; Lings & Greenley, 2005). This focus makes employees feel cared for and valued, which brings about reciprocal commitment and retention of employees (Lings & Greenley, 2005; Piercy & Morgan, 1990). Internal market orientation has greater relevance in service industry due to the importance of frontline employee in delivering quality services to customers. In fact, the frontline employees are the services. The employees receive fair treatment and special attention from the managers in exchange for the delivery of quality services to the end customers (Frost & Kumar, 2000; Greene, Walls, & Schrest, 1994; Tortosa, Moliner, & Sánchez, 2009). Although IMO received significant attention in marketing literature, much of the work remains conceptual and confined to for-profit organisations. A few scales have also been developed for measuring IMO (e.g., Lings & Greenley, 2005; Gounaris, 2006); however, Lings and Greenley s (2005) scale has attracted more attention probably because it is modeled as the internal equivalent to Kohli and Jaworski s (1990) market orientation construct in the external context. Lings and Greenley (2005) empirically validates the IMO construct and scale in the context of retail industry of a developed country. They suggest that the IMO construct be further validated in different contexts and cultural settings. We take up this work in this paper and extend the boundary of the IMO construct to nonprofit service organisations. In doing so, we establish its relevance for NPOs. Andreasen, Goodstein, and Wilson (2005) emphasise the need for inter-sectoral transfer of marketing knowledge and practices from business to nonprofit sector. Given the managerial challenges facing NPOs, IMO may prove to be a highly relevant and important construct for them. Nonprofit managers may find gainful application of the IMO construct and scale, if available, in their practice. In our literature search, we did not come across any study that had

3 validated either the IMO construct or the scale in a nonprofit context. Hence, the purpose of this study is to (a) revise and validate the IMO construct in the nonprofit context (b) adapt and finalise the Lings and Greenley (2005) scale for measuring IMO of NPOs, and (c) provide evidence that IMO can help NPOs address the challenges of staff retention and beneficiary satisfaction. In the next section, we describe our research methodology and data collection process. Next, we validate the construct and finalise the scale using confirmatory factor analyses. And finally, we share conclusion, limitations and future research directions. Research Methodology We started our research in exploratory manner. After reviewing extant literature on IMO, we conducted semi-structured in-depth interviews of 10 senior executives from NPOs. We discussed with them the normative contents of the IMO construct, its relevance for and application in the nonprofit context, manifestation and consequences of this orientation, and suitability as well as adequacy of the scale items as valid indicators of IMO. These discussions revealed that the Lings and Greenley (2005) IMO construct and scale should be revised and refined before it is applied in the context of NPOs. Because NPOs are generally much smaller in size compared to business organisations, they generally source internal information in informal fashion. They are not compelled to generate information formally through either written or faceto-face means. Hence, we expected that unlike the IMO in business context which had five firstorder correlated factors, IMO in NPO would be a higher-order construct having only 3 first-order factors of informal information generation, information dissemination, and responsiveness. In this reconceptualisation of IMO in NPOs, we also corrected what we argue as Ling and Greenley s (2005) misspecification of the construct factor structure which was less restrictive and not representing the theoretical connection between the higher-order IMO construct and the lower-order dimensions. Lings and Greenley (2005) operationalised IMO on the lines of Kohli and Jaworski s (1990) market orientation, which is considered a higher-order construct causing first-order behavioural factors (e.g., Matsuno, Mentzer, & Ozsomer, 2002; Matsuno, Manzer, & Rentz, 2000; 2005). However, they empirically tested the five first-order correlated factors model (Lings & Greenley, 2005) which was less restrictive compared to the higher-order IMO factor structure model. Hence, we hypothesised IMO in NPO as the higher-order construct having three first-order factors of informal information generation, dissemination, and responsiveness. However, to enable comparisons with several alternative models of IMO, we decided to collect data on all the five dimensions of the Ling and Greenley (2005) IMO scale after adapting the wordings to suit the realities of the nonprofit context. The feedback from pilot study of the questionnaire with 20 NPOs led us to delete two items originally coded as EAINFLRS and EAIMR in the Lings and Greenely (2005, p. 296) scale. The questionnaire also contained scales for staff retention (Lings & Greenley, 2005) and beneficiary satisfaction (Padanyi & Gainer, 2004). We surveyed 1000 NPOs from India engaged in provision of welfare services to local communities or beneficiaries. We received more than 400 responses and after discarding those not meeting our sample criteria (mentioned below) we were left with 370 usable responses. We called 5% of the responding organisations to check if the responses and respondents were genuine. To our relief, we did not come across a single case of misrepresentation. Comparison of

4 early and late responses led us to believe that nonresponse was not an issue of concern for us (Armstrong & Overton, 1977). Our major concerns in data collection involved reducing method bias and increasing response rate. We translated the IMO scale in two additional Indian languages Hindi and Gujarati using back-to-back scale translation technique (Behling & Law, 2000). Further, we minimised method bias by (a) keeping predictor variables at the beginning of the questionnaire followed by the criterion variables so as not to artificially inflate their correlations; (b) removing responses from any office bearer other than the top management bearing one of the following designations chief executive officer, managing director, director, managing secretary, or board member; (c) removing responses of respondents with under 2 years of work experience within current organisations to ensure that the respondents had full organisational knowledge, and (d) removing responses indicating less than 5 points of confidence in the information provided on a scale of 1 to 7. In addition, we measured respondents social desirability trait so that we could control for any potential bias emanating from the trait (Fischer & Fick, 1993). Construct Validation and Scale Finalisations Less than 0.5% data were missing at random which were imputed using the mean score of the other items in the scale (Roth, Switzer & Switzer, 1999). All the items exhibited good variability and covered full range of responses. We put our hypothesised measurement model of IMO scale through confirmatory factor analysis (CFA) using maximum likelihood estimation method in AMOS 21 software which yielded a significant chi-square value and significant Bollen-Stine bootstrap p value (χ , df 33, CFI 0.93, TLI 0.91, GFI 0.93, RMSEA 0.098, SRMR 0.046). Since our data were multivariate non-normal and we were using maximum likelihood estimation method, we expected overrejection of the model due to inflated chi-square value and underestimated fit indices (Hu & Bentler, 1999; West, Finch & Curran, 1995). Our model yielded acceptable fit with the data (Hu & Bentler, 1999) indicating construct validity and support our hypothesised measurement model. Cronbach alpha of our IMO in NPO scale was 0.89 and construct reliability of the scale calculated as ( λ i ) 2 / [( λ i ) 2 + (1- λ i 2 )] was 0.93, significantly higher than the recommended value of 0.6 (Bagozzi & Yi, 1988), and average variance extracted (AVE) calculated by λ i 2 / [ λ i 2 + (1- λ i 2 )] was 0.83 meeting the recommended value by Fornell and Larcker (1981). All the item loadings are highly significant and substantial (standardised loadings greater than 0.6 for all the items), converging on their respective dimension providing the evidence of convergent validity (Netemeyer, Bearden & Sharma, 2003; Steenkamp & Trijp, 1991). Discriminant validity was checked by way of comparing the correlations between IMO, staff retention (Lings and Greenley, 2005 scale used) and beneficiary satisfaction (Padanyi & Gainer, 2004 scale used). For every pair of the scales, a constrained model (wherein the covariance between the IMO scale and one of the above mentioned scales is fixed to 1) is compared against an unconstrained model (wherein the covariance between the two scales is freely estimated) based on the chi-square difference test. The unconstrained models outperformed the constrained models in the chi-square difference test, providing the evidence of discriminant validity (Anderson & Gerbing, 1988; Dunn, Seaker & Waller, 1994). To get the evidence of nomological validity, we tested a structural model relating IMO with beneficiary satisfaction (equivalent of customer satisfaction in the nonprofit context) and staff retention. It showed acceptable fit with the data (χ , df 100, CFI 0.93, TLI 0.92, GFI 0.92, RMSEA 0.07, SRMR 0.05) and highly significant

5 standardised regression weights: 0.55 for the IMO beneficiary satisfaction path and 0.43 for the IMO staff retention path providing nomological validity for our measurement model. These links with beneficiary satisfaction and staff retention also shows that implementing IMO in NPOs can address their two major problems related to (a) retention of field staff crucially important for service delivery, and (b) satisfaction of beneficiaries with the quality of services provided. Alternative Models of IMO in NPOs Past studies have proposed several alternative models of IMO, which should be put through CFAs, and their results should be compared and evaluated against that of our trait-only model using the chi-square difference test. We identified the following alternative models of IMO (a) independence model (a model of no relationships), (b) general factor model (IMO being a uni-dimensional construct and all the items loading onto it), (c) three orthogonal factors model (three first-order dimensions of informal information generation, dissemination, and responsiveness without any covariance between them), (d) three correlated factors model (faceto-face, written, and informal information generation dimensions merged into a common factor of information generation {Carter & Gray, 2007; Gounaris, 2006}), (e) four correlated factors model (face-to-face and written information generation dimensions merged into a common factor of formal information generation {Tortosa, Moliner & Sánchez, 2009}), (f) five first-order correlated factors model (as in Lings & Greenley, 2005), and (g) second-order model having five first-order factors (extending the Lings and Greenley {2005} model to the higher-order factor). We created two more alternative models to check if method bias or social desirability bias had acted upon our data and the results (h) trait-only plus method factor model (we loaded a general method factor on our hypothesised model) and (i) trait-only plus social desirability bias model (we loaded the social desirability construct on the reference model). The chi-square difference test is not useful in comparing models (h) or (i) as these are non-nested models. These two models are compared with the trait-only model based on their fit indices, and their AIC and ECVI values which indicates the possibilities of the model being replicable (Browne & Cudeck, 1989). A better overall fit provided by models (h) or (i) over the trait-only model would indicate the possibility of method bias. The chi-square difference test is used for comparing models (a) to (g) as they are nested models. See annexure 1 for comparison of the alternative models. Our hypothesised model of IMO outperforms all the alternative models exhibiting the lowest chisquare value for the degree of freedom. It significantly reduces chi-square values for the lost degrees of freedom over the alternative models (a) to (g). Thus, we find strong support for our hypothesised factor structure of the IMO measurement model. The model (h) containing the unmeasured latent method factor loading on the trait-only model led to the Heywood case, most likely due to the over squeezing of information (Rindskopf & Rose, 1988). The model (i) containing the social desirability bias factor loading on the trait-only model led to deterioration of the fit indices and increased the ECVI and AIC values indicating lesser chances of finding such a model getting replicated (Browne & Cudeck, 1989). Hence, we can conclude that social desirability does not have any problematic influence on the scale. Conclusion, Limitations, and Future Research Directions Working with NPOs engaged in delivering community/ public/ welfare services to their beneficiaries (or target customers as we call them) is quite challenging as it often involves

6 working in remote places and/or working with marginalised, poor, or disadvantageous groups/communities. The very nature and purpose of the sector does not allow the organisations to pay salaries in any manner comparable to their counterparts in the for-profit sector. Hence, NPOs find it difficult to attract and retain professional human resource, which in turn negatively affects their mission achievement. The fast growing nonprofit sector demands high quality professional human resource. As a result, nonprofit practitioners concern with the issues related to training, motivating, attracting and retaining their staff and volunteers is increasing. In such a situation, IMO could prove to be one such important managerial input for NPOs. However, the IMO construct and scale which emerged in the for-profit context require construct and scale validation before it could be used and applied in the nonprofit context. We make a contribution by way of validating the IMO construct and finalising the Lings and Greenley (2005) scale in the nonprofit context. In the process, we also test and reject the alternative conceptualisations of IMO and show that it is a second-order construct causing three first-order behavioral dimensions of informal information sourcing, dissemination, and responsiveness. Nonprofit practitioners can use this scale for diagnostic purpose and to manage their focus on the internal exchange. The scale was validated based on the data collected from NPOs working in India. Future research should examine the properties of this scale in the context of other emerging countries, not only in Asia but also in Africa and Latin America. Our research was conducted in the context of NPOs that were actively engaged in delivering welfare/public services to beneficiaries mainly through paid employees. Nonprofit organizations have two kinds of employees paid professionals and volunteers. Work motivations for both may differ and future research should investigate whether the internal marketing practices should differ in each case, and how.

7 References Andreasen, A.R., Goodstein, R.C. & Wilson, J.W. (2005). Transferring Marketing Knowledge to the Nonprofit Sector. California Management Review, 47(4), Anderson, J.C. & Gerbing, D.W. (1988). Structural Equation Modeling in Practice: A Review and Recommended Two-step Approach. Psychological Bulletin, 103(3), Armstrong, J.S. & Overton, T.S. (1977). Estimating Non Response Bias in Mail Surveys. Journal of Marketing Research, 14(August), Bagozzi, R.P. & Yi, Y. (1988). On the Evaluation of Structural Equation Models. Journal of the Academy of Marketing Science, 16(Spring), Behling, O. and K. Law (2000), Translating Questionnaires and Other Research Instruments: Problems and Solutions. New Delhi: Sage Publications. Browne, M. & Cudeck, R. (1989). Single Sample Cross-validation Indices for Covariance Structures. Multivariate Behavioral Research, 24(4), Carter, L. & Gray, D. (2007). Relational Competence, Internal Market Orientation and Employees Performance. The Marketing Review, 7(4), Dunn, S.C., Seaker, R.F. & Waller, M.A. (1994). Latent Variables in Business Logistics Research: Scale Development and Validation. Journal of Business Logistics, 15(2), Fischer, D. and C. Fick (1993), Measuring Social Desirability: Short Forms of the Marlowe- Crowne Social Desirability Scale, Educational and Psychological Measurement, 53, Fornell, C. & Larcker, D. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), Frost, F. & Kumar, M. (2000). INTSERVQUAL: An Internal Adaptation of the Gap Model in a Large Service Organisation. Journal of Services Marketing, 14(5), Gounaris, S.P. (2006). Internal Market Orientation and its Measurement. Journal of Business Research, 59, Greene, W.E., Walls, G.D. & Schrest, L.J. (1994). Internal Marketing: The Key to External Marketing Success. Journal of Services Marketing, 8(4), Hu, L-T., & Bentler, P. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), Kohli, A. & Jaworski, B. (1990). Market Orientation: The Construct, Research Propositions, and Managerial Implications. Journal of Marketing, 54(2), Lings, I.N. (2004). Internal Market Orientation Construct and Consequences. Journal of Business Research, 57, Lings, I.N. & Greenley, G.E. (2005). Measuring Internal Market Orientation. Journal of Service Research, 7(3), Matsuno, K., Mentzer J.T. & Ozsomer A. (2002). The effects of entrepreneurial proclivity and market orientation on business performance. Journal of Marketing, 66(3), Matsuno, K., Mentzer, J.T. & Rentz, J.O. (2005). A conceptual and empirical comparison of three market orientation scales. Journal of Business Research, 58 (1), 1-8. Matsuno, K., Mentzer, J.T. & Rentz, J.O. (2000). A refinement and validation of the MARKOR scale. Journal of the Academy of Marketing Science, 28(4),

8 Modi, P. & Mishra, D. (2010). Conceptualising Market Orientation in Non-profit Organisations: Definition, Performance, and Preliminary Construction of a Scale. Journal of Marketing Management, 26(5/6), Netemeyer, R., Bearden, W. & Sharma, S. (2003). Scaling Procedures: Issues and Applications. Sage Publications, Thousand Oaks. Padanyi, P. & Gainer, B. (2004). Market Orientation in the Non-profit Sector: Taking Multiple Constituencies into Consideration. Journal of Marketing- Theory and Practice, 12(2), Piercy, N. F. and N. A. Morgan (1990), Internal Marketing: Making Marketing Happen, Marketing Intelligence and Planning, 8, 4-6. Rindskopf, D. & Rose, T. (1988). Some Theory and Applications of Confirmatory Second-order Factor Analysis. Multivariate Behavioral Research, 23(January), Roth, P., Switzer, F. & Switzer, D. (1999). Missing Data in Multiple Item Scales: A Monte-Carlo Analysis of Missing Data Techniques. Organizational Research Methods, 2(3), Singh, J. (2000). Performance Productivity and Quality of Frontline Employees in Service Organizations. Journal of Marketing, 64(April), Steenkamp, J.B. & Trijp, H.V. (1991). The Use of LISREL in Validating Marketing Constructs. International Journal of Research in Marketing, 8(4), Tortosa, V., Moliner, M. & Sánchez, J. (2009). Internal Market Orientation and its Influence on Organisational Performance. European Journal of Marketing, 43(11/12), Weisboard, B.A. (1977). The Voluntary Non-profit Sector. Heath and Company, Lexington, DC. West, S.G., Finch, J.F. & Curran, P.J. (1995). Structural Equations with Non-normal Variables: Problems and Remedies. In Hoyle, R. (Ed.), Structural Equation Modeling: Issues and Applications, Sage Publications, Newbury Park,

9 Annexure 1: Comparison of the alternative models of IMO Alternate Model (Source) Chi- Square DoF P Value CFI TLI GFI RMSEA; SRMR ECVI; AIC Chi-Square Difference (DDoF) The Hypothesised Model ; ; The reference model (a) Independence Model ; ; ** (12) (b) General Factor Model (c) Three Orthogonal Factors Model ; ; ; ; ** (2) ** (2) (d) Three Correlated Factors Model (Carter & Gray, 2007; Gounaris, 2006) ; ; ** (54) (e) Four Correlated Factors Model (Tortosa et al., 2009) ; ; ** (51) (f) Five Correlated Factors Model (Lings & Greenley, 2005) ; ; ** (47) (g) Second-order IMO having five first-order factors (The extended Lings & Greenley, 2005 model) ; ; ** (52) (h) Trait Plus Method Factor Model Heywood case (High negative error variances probably due to over squeezing the data) (i) Trait Plus Social Desirability Bias Factor Model (Non- Nested) ; ; Chi-square test not applicable. ECVI and AIC values higher and the fit indices lower than the hypothesised model.

10 DoF = Degree of Freedom; DDoF = Difference in the Degree of Freedom over the Trait Only Model; CFI = Comparative Fit Index, TLI = Tucker-Lewis Index, GFI = Goodness of Fit Index, RMSEA = Root Mean Squared Error of Approximation; SRMR = Standardised Root Mean Square Residual; ECVI = Expected Cross Validation Index; AIC = Akaike s Information Criterion; ** p <