A scale for testing of knowledge on the effective marketing research processes: multiple-group confirmatory (MGCFA) and multiple indicators-multiple causes (MIMIC) approach

  • Piotr TarkaEmail author
Original Article


This paper has two main objectives. The first contributes to the development of marketing theory by proposing a measurement instrument, allowing for the testing of knowledge on the effective marketing research processes, while the second focuses on the verification of that knowledge across two groups: managers and researchers in business organizations from the perspective of a two-community theory of management. In particular, the level of compliance between information-users and information-producers on their knowledge of these processes is investigated. With these objectives in mind, theoretical assumptions of the effective marketing research processes from the methodological perspective are discussed. Next, methodological problems appearing in marketing research processes are characterized. Further discussion speaks about a need for stronger integrity of researchers and managers on the basis of methodological knowledge of effective research processes in order to enhance these processes and improve activities related to planning marketing strategies and decision-making. Finally, when comparing that knowledge on the basis of the empirical study conducted, we implemented the following analytical strategies based on Multi-Group Confirmatory Factor Analysis (MGCFA) and Multiple Indicators-Multiple Causes (MIMIC) model in reference to a sample 391 of respondents working in multinational companies in a European country (Poland). The results confirmed the psychometric quality of the developed scale of testing knowledge on effective marketing research processes, but also revealed that managers and researchers share slightly different points of view on the effective marketing research processes. Managers exhibit an even greater level of instability than researchers when evaluating these processes, while their insufficient knowledge is due to a lack of conviction that marketing research can be effective in general. Moreover, managers assume that marketing research and research information does not always play a significant role in the activities related to planning marketing strategies and decision-making. Our findings also have practical implications for organizations that wish to improve their internal information policy and make the marketing research more effective with equal engagement of both groups.


Effective marketing research processes Information-users (managers) Market researchers Invariance measurement CFA MGCFA and MIMIC models Information and knowledge 



  1. Aaker, D.A., and G.S. Day. 1980. Increasing the effectiveness of marketing research. California Management Review. 23 (2): 59–65.CrossRefGoogle Scholar
  2. Aaker, D.A., V. Kumar, G.S. Day, and R. Leone. 2012. Marketing research. Hoboken: Wiley.Google Scholar
  3. Arbuckle, J.L. 2007. Amos:16 user’s guide. Chicago: SPSS.Google Scholar
  4. Ashill, N.J., and D. Jobber. 2014. The effects of the external environment on marketing decision maker uncertainty. Journal of Marketing Management. 30 (3/4): 268–294.CrossRefGoogle Scholar
  5. Bagozzi, R., and Y. Yi. 1988. On the evaluation of structural equation models. Journal of the Academy of Marketing Science 16 (1): 74–94.CrossRefGoogle Scholar
  6. Barnham, C. 2010. Qualis? The qualitative understanding of essence. International Journal of Market Research. 52 (6): 757–773.CrossRefGoogle Scholar
  7. Barnham, C. 2015. Quantitative and qualitative research: perceptual foundations. International Journal of Market Research. 57 (6): 837–854.CrossRefGoogle Scholar
  8. Bearden, W.O., R.G. Netemeyer, and K.L. Haws. 2011. Handbook of marketing scales: multi-item measures for marketing and consumer behavior research. Thousand Oaks: SAGE Publications.Google Scholar
  9. Bellenger, D.N. 1979. The marketing manager’s view of marketing research. Business Horizons 22: 59–65.CrossRefGoogle Scholar
  10. Billiet, J. 2002. Cross-cultural equivalence with structural equation modeling. In Cross-cultural survey methods, ed. P.P. Mohler, 247–264. New Jersey: Wiley.Google Scholar
  11. Bingham, C.B., and K.M. Eisenhardt. 2011. Rational heuristics: the ‘simple rules’ that strategists learn from process experience. Strategic Management Journal 32 (13): 1437–1464.CrossRefGoogle Scholar
  12. Birgelen, M., K. Ruyter, and M. Wetzels. 2001. What factors determine the use of quality-related marketing research information? An empirical investigation. Total Quality Management. 12 (4): 521–534.CrossRefGoogle Scholar
  13. Birnbaum, M.H. 2004. Human research and data collection via the internet. Annual Review of Psychology 55 (1): 803–832.CrossRefGoogle Scholar
  14. Brace, I. 2018. Questionnaire design: how to plan, structure and write survey material for effective market research. London: Kogan Page.Google Scholar
  15. Brick, J.M. 2011. The future of survey sampling. Public Opinion Quarterly 75 (5): 872–888.CrossRefGoogle Scholar
  16. Browne, M.W., and R. Cudeck. 1993. Alternative ways of assessing model fit. In Testing structural equation models, ed. K.A. Bollen and J.S. Long, 136–162. Newbury Park: Sage Publications.Google Scholar
  17. Bryman, A., and E. Bell. 2015. Business research methods. Oxford: Oxford University Press.Google Scholar
  18. Byrne, B.M., and F.J.R. Van de Vijver. 2010. Testing for measurement and structural equivalence in large-scale cross-cultural studies: addressing the issue of nonequivalence. International Journal of Testing 10 (2): 107–132.CrossRefGoogle Scholar
  19. Byrne, B.M., R.J. Shavelson, and B.O. Muthén. 1989. Testing for equivalence of factor covariance and mean structures: the issue of partial measurement invariance. Psychological Bulletin 105 (3): 456–466.CrossRefGoogle Scholar
  20. Cacciolatti, L.A., and A. Fearne. 2013. Marketing intelligence in SMEs: Implications for the industry and policy makers. Marketing Intelligence and Planning 31 (1): 4–26.CrossRefGoogle Scholar
  21. Case, D.O. 2012. Looking for information: a survey of research on information seeking, needs, and behavior. Emerald: Bingley.Google Scholar
  22. Cerny, C.A., and H.F. Kaiser. 1977. A study of a measure of sampling adequacy for factor-analytic correlation matrices. Multivariate Behavioral Research 12 (1): 43–47.CrossRefGoogle Scholar
  23. Cheung, G.W., and R.B. Rensvold. 2002. Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling 9 (2): 233–255.CrossRefGoogle Scholar
  24. Choo, C.W. 1996. The knowing organization: how organizations use information to construct meaning, create knowledge and make decisions. International Journal of Information Management 16 (5): 329–340.CrossRefGoogle Scholar
  25. Choo, C.W. 2013. Information culture and organizational effectiveness. International Journal of Information Management 33 (5): 775–779.CrossRefGoogle Scholar
  26. Choo, C.W., and E. Auster. 1993. Environmental scanning: acquisition and use of information by managers. Annual Review of Information Science and Technology 28: 279–314.Google Scholar
  27. Churchill, G.A. 1979. A paradigm for developing better measures of marketing constructs. Journal of Marketing Research 16 (1): 64–73.CrossRefGoogle Scholar
  28. Churchill, G.A., and D. Iacobucci. 2005. Marketing research: methodological foundations. London: Harcourt Publishing.Google Scholar
  29. Churchman, W.C., and A.H. Schainblatt. 1965. The researcher and the manager: a dialectic of implementation. Management Science. 11 (4): 69.CrossRefGoogle Scholar
  30. Citroen, C.L. 2011. The role of information in strategic decision-making. International Journal of Information Management 31 (6): 493–501.CrossRefGoogle Scholar
  31. Clark, L.A., and D. Watson. 1995. Constructing validity: basic issues in objective scale development. Psychological Assessment 7 (3): 309–319.CrossRefGoogle Scholar
  32. Cobanoglu, C., B. Warde, and P.J. Moreo. 2001. A comparison of mail, fax, and web-based survey methods. International Journal of Market Research 43 (4): 441–452.CrossRefGoogle Scholar
  33. Cook, C., F. Heath, and R.L. Thompson. 2000. A meta-analysis of response rates in web- or internet-based surveys. Educational and Psychological Measurement 60 (6): 821–836.CrossRefGoogle Scholar
  34. Cronbach, L.J. 1951. Coefficient alpha and the internal structure of tests. Psychometrika 16 (3): 297–334.CrossRefGoogle Scholar
  35. Cui, A.P., M.Y. Hu, and D.A. Griffith. 2014. What makes a brand manager effective? Journal of Business Research 67 (2): 144–150.CrossRefGoogle Scholar
  36. Culkin, N., D. Smith, and J. Fletcher. 1999. Meeting the information needs of marketing in the twenty-first century. Marketing Intelligence and Planning 17 (1): 6–12.CrossRefGoogle Scholar
  37. Curren, M.T., V.S. Folkes, and J.H. Steckel. 1985. Explanation for successful and unsuccessful marketing decisions: the decision maker’s perspective. Journal of Marketing 56 (2): 18–21.CrossRefGoogle Scholar
  38. Dane, E., and M.G. Pratt. 2007. Exploring intuition and its role in managerial decision making. Academy of Management Review 32 (1): 33–54.CrossRefGoogle Scholar
  39. Davidov, E., B. Meuleman, J. Cieciuch, P. Schmidt, and J. Billet. 2014. Measurement equivalence in cross-national research. The Annual Review of Sociology 40: 55–75.CrossRefGoogle Scholar
  40. Dawes, J. 2008. Do data characteristics change according to the number of scale points used? International Journal of Market Research 50 (1): 61–77.CrossRefGoogle Scholar
  41. Dawes, J., D. Lee, and G. Dowling. 1998. Information control and influence in emergent buying centers. Journal of Marketing 62 (3): 55–68.CrossRefGoogle Scholar
  42. Dawis, F.D., S.L. Golicic, C.N. Boerstler, S. Choi, and H. Oh. 2013. Does marketing research suffer from methods myopia? Journal of Business Research 66: 1245–1250.CrossRefGoogle Scholar
  43. De Beuckelaer, A., and G. Swinnen. 2011. Biased latent variable mean comparisons due to measurement noninvariance: a simulation study. In Cross-cultural analysis: methods and applications, ed. E. Davidov, P. Schmidt, and J. Billiet, 117–147. New York: Routledge.Google Scholar
  44. DeCarlo, L.T. 1997. On the meaning and use of kurtosis. Psychological Methods 2 (3): 292–307.CrossRefGoogle Scholar
  45. Del Vecchio, E. 1991. Market research as a continuous process. Journal of Consumer Marketing 8 (1): 53–59.CrossRefGoogle Scholar
  46. Dennis, A.R. 1996. Information exchange and use in group decision making: You can lead a group to information, but you cannot make it think. MIS Quarterly 20: 74–81.CrossRefGoogle Scholar
  47. Deshpande, R., and G.R. Zaltman. 1984. A comparison of factors affecting researcher and manager perceptions of market research use. Journal of Marketing Research 2 (11): 32–38.CrossRefGoogle Scholar
  48. Deshpande, R., and F.E. Webster Jr. 1989. Organizational culture and marketing: defining the research agenda. Journal of Marketing. 53 (1): 3–15.CrossRefGoogle Scholar
  49. Diamantopoulos, A., and A. Souchon. 1998. Information utilisation by exporting firms: Conceptualisation, measurement, and impact on export performance. In Information and management: Utilisation of technology, structural and cultural impact, ed. S. Urban and C. Nanopoulos, 111–140. Wiesbaden: Gabler.CrossRefGoogle Scholar
  50. Diamantopoulos, A., and J.A. Siguaw. 2002. The impact of research design characteristics on the evaluation and use of export marketing research: an empirical study. Journal of Marketing Research 18 (1/2): 73–104.Google Scholar
  51. Diaz Ruiz, C., and M. Holmlund. 2017. Actionable marketing knowledge: a close reading of representation, knowledge and action in market research. Industrial Marketing Management 66: 172–180.CrossRefGoogle Scholar
  52. Donnelly, M., S. Van’t Hull, and V. Will. 2000. Assessing the quality of service provided by market research agencies. Total Quality Management 11 (4/6): 490–500.CrossRefGoogle Scholar
  53. Dubof, R., and J. Spaeth. 2000. Marketing research matters: tools and techniques for aligning your business. New York: Wiley.Google Scholar
  54. Dunn, W. 1980. The two community metaphor and models of knowledge utilization: an exploratory case study. Knowledge: Creation, Diffusion, Utilization. 1 (4): 575–586.CrossRefGoogle Scholar
  55. Easterby-Smith, M., R. Thorpe, and R.P. Jackson. 2015. Management and business research. Thousand Oaks: Sage Publications.Google Scholar
  56. Edmunds, A., and A. Morris. 2000. The problem of information overload in business organizations: a review of the literature. International Journal of Information Management 20 (1): 17–28.CrossRefGoogle Scholar
  57. Esomar 2014. Global market research report: an Esomar industry report, downloaded from the world research group at
  58. Fisher, R.J., E. Maltz, and B.J. Jaworski. 1997. Enhancing communication between marketing and engineering: the moderating role of relative functional identification. Journal of Marketing 61 (3): 54–71.CrossRefGoogle Scholar
  59. Fornell, C., and D.F. Larcker. 1981. Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research 18 (1): 39–50.CrossRefGoogle Scholar
  60. Frishammar, J. 2002. Characteristics in information processing approaches. International Journal of Information Management 22 (2): 143–156.CrossRefGoogle Scholar
  61. Ganeshasundaram, R., and N. Henley. 2006. The prevalence and usefulness of market research: an empirical investigation into background versus decision research. International Journal of Market Research. 48 (5): 525–550.CrossRefGoogle Scholar
  62. Gerard, G., M.R. Haas, and A. Pentland. 2014. Big data and management: from the editors. Academy of Management Journal 57 (2): 321–326.CrossRefGoogle Scholar
  63. Glazer, R., and A.M. Weiss. 1993. Marketing in turbulent environments: Decision processes and the time-sensitivity of information. Journal of Marketing Research 30 (4): 509–521.CrossRefGoogle Scholar
  64. Goodman, S. 1993. Information needs for management decision-making. Records Management Quarterly. 27 (4): 12–23.Google Scholar
  65. Greg, S., and M. Callingham. 1994. Quality comes to the market research world: just in time or just too late? International Journal of Market Research. 36 (4): 269–294.Google Scholar
  66. Grossnickle, J., and O. Raskin. 2000. The handbook of online marketing research. New York: McGraw-Hill.Google Scholar
  67. Guo, C. 2004. Marketing research: Cui bono? Business Horizons 47 (5): 33–38.CrossRefGoogle Scholar
  68. Hair, J.F., M.F. Wolfinbarger, D.J. Ortinau, and R.P. Bush. 2008. Essentials of marketing research. New York: McGraw-Hill.Google Scholar
  69. Hair Jr., J., J. Celsi, M. Wolfinbarger, H.A. Money, P. Samouel, and M.J. Page. 2015. The scientific method and business research in essentials of business research methods, 37–39. New York: M.E. Sharpe.CrossRefGoogle Scholar
  70. Hart, S., and A. Diamantopoulos. 1993. Marketing research activity and company performance: evidence from manufacturing industry. European Journal of Marketing 27 (5): 54–72.CrossRefGoogle Scholar
  71. Holbert, N.B. 1974. How managers see marketing research. Journal of Advertising Research 14: 41–46.Google Scholar
  72. Hu, L.-T., and P.M. Bentler. 1999. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling 6 (1): 1–55.CrossRefGoogle Scholar
  73. Hu, M.Y. 1986. An experimental study of managers’ and researchers’ use of consumer market research. Journal of the Academy of Marketing Science 14 (3): 44–51.CrossRefGoogle Scholar
  74. Hult, G.M.T., F.V. Morgeson, N.A. Morgan, S. Mithas, and C. Fornell. 2017. Do managers know what their customers think and why? Journal of the Academy Marketing Science 45 (1): 37–54.CrossRefGoogle Scholar
  75. Huppertz, J.W. 2003. Passion vs. dispassion. Marketing Research 15 (2): 16–21.Google Scholar
  76. Jackson, T.W., and P. Farzaneh. 2012. Theory-based model of factors affecting information overload. International Journal of Information Management 32 (6): 523–532.CrossRefGoogle Scholar
  77. John, G., and J. Martin. 1984. Effects of organisational structure of marketing planning on credibility and utilisation of plan output. Journal of Marketing Research 21 (2): 170–183.CrossRefGoogle Scholar
  78. Jones, S. 1985. Problem definition in marketing research: facilitating dialog between clients and researchers. Psychology in Marketing 2 (2): 83–92.CrossRefGoogle Scholar
  79. Jöreskog, K.G. 1971a. Statistical analysis of sets of congeneric tests. Psychometrika 36 (2): 109–133.CrossRefGoogle Scholar
  80. Jöreskog, K.G. 1971b. Simultaneous factor analysis in several populations. Psychometrika 36: 409–426.CrossRefGoogle Scholar
  81. Jöreskog, K.G., and A.S. Goldberger. 1975. Estimation of a model with multiple indicators and multiple causes of a single latent variable. Journal of the American Statistical Association 70 (351A): 631–639.CrossRefGoogle Scholar
  82. Kent, R. 1999. Marketing research: measurement, method and application. London: Thomson Learning.Google Scholar
  83. Keszey, T. 2011. How market information is transformed into marketing knowledge? Acta Oeconomica 61 (3): 313–336.CrossRefGoogle Scholar
  84. Keszey, T. 2015. The role of market researchers in managerial use of market research information. Trziste 27 (1): 43–56.Google Scholar
  85. Keszey, T., and W. Biemans. 2017. Trust in marketing’s use of information from sales: the moderating role of power. Journal of Business and Industrial Marketing 32 (2): 258–273.CrossRefGoogle Scholar
  86. Kharti, N., and H. Alvin. 2000. The role of intuition in strategic decision making. Human Relations 53 (1): 57–86.CrossRefGoogle Scholar
  87. Kline, P. 1986. A handbook of test construction. New York: Methuen.Google Scholar
  88. Kline, R.B. 2005. Principles and practice of structural equation modeling. New York: Guilford Press.Google Scholar
  89. Krishnan, S. 1989. Making more effective use of market information: a conference summary, Report No. 89-113, September, Cambridge, MA: Marketing Science Institute.Google Scholar
  90. Kumar, N., l Scheer, and P. Kotler. 2000. From market driven to market driving. European Management Journal 18 (2): 129–142.CrossRefGoogle Scholar
  91. Kumar, V., D. Aaker, and G. Day. 1999. Essentials of marketing research. New York: Wiley.Google Scholar
  92. Lee, H., J.D. Lindquist, and F. Acito. 1997. Managers’ evaluation of research design and its impact on the use of research: an experimental approach. Journal of Business Research 39 (3): 231–240.CrossRefGoogle Scholar
  93. Lee, H., F. Acito, and R.L. Day. 1987. Evaluation and the use of marketing research by decision makers: a behavioral simulation. Journal of Marketing Research 24 (2): 187–196.CrossRefGoogle Scholar
  94. Lesca, N., M.-L. Caron-Fasan, and S. Falcy. 2012. How managers interpret scanning information. Information and Management 49 (2): 126–134.CrossRefGoogle Scholar
  95. Li, T., and R. Calantone. 1998. The impact of market knowledge competence on new product advantage: conceptualization and empirical examination. Journal of Marketing 62 (4): 13–29.CrossRefGoogle Scholar
  96. Lietz, P. 2010. Research into questionnaire design: a summary of the literature. International Journal of Market Research 52 (5): 249–272.Google Scholar
  97. Little, T.D. 1997. Mean and covariance structures (MACS) analyses of cross-cultural data: practical and theoretical issues. Multivariate Behavioral Research 32 (1): 53–76.CrossRefGoogle Scholar
  98. Low, G.S., and J.J. Mohr. 2001. Factors affecting the use of information in the evaluation of marketing communications productivity. Journal of the Academy of Marketing Science 29 (1): 70–88.CrossRefGoogle Scholar
  99. MacCallum, R.C., M. Roznowski, and L.B. Necowitz. 1992. Model modifications in covariance structure analysis: the problem of capitalization on chance. Psychological Research 111 (3): 490–504.Google Scholar
  100. MacKenzie, S.B., P.M. Podsakoff, and N.P. Podsakoff. 2011. Construct measurement and validation procedures in MIS and behavior research: integrating new and existing techniques. MIS Quarterly 35 (2): 293–334.CrossRefGoogle Scholar
  101. Malcomson, J.M. 2011. Do managers with limited liability take more risky decisions? An information acquisition model. Journal of Economics and Management Strategy 20 (1): 83–120.CrossRefGoogle Scholar
  102. Malhotra, N.K. 2004. Marketing research: an applied orientation. Englewood Cliffs: Prentice-Hall.Google Scholar
  103. Malhotra, N.K., and M. Peterson. 2001. Marketing research in the new millennium: emerging issues and trends. Market Intelligence and Planning 19 (4): 216–235.CrossRefGoogle Scholar
  104. Malhotra, N.K., M. Peterson, and C. Uslay. 2006. Helping marketing research earn a seat at the table for decision-making: an assessment and prescription for the future. European Business Review 18 (4): 294–306.CrossRefGoogle Scholar
  105. Maltz, E., and A.K. Kohli. 1996. Market intelligence dissemination across functional boundaries. Journal of Marketing Research 33 (1): 47–61.CrossRefGoogle Scholar
  106. Mardia, K.V. 1970. Measures of multivariate skewness and kurtosis with applications. Biometrika 57 (3): 519–530.CrossRefGoogle Scholar
  107. Menon, A., and R. Varadarajan. 1992. A model of marketing knowledge use within firms. Journal of Marketing 56 (4): 53–71.CrossRefGoogle Scholar
  108. Meredith, W. 1993. Measurement invariance, factor analysis and factorial invariance. Psychometrika 58 (4): 525–543.CrossRefGoogle Scholar
  109. Miller, T.W. 2001. Can we trust the data of online research? Marketing Research 3 (2): 26–32.Google Scholar
  110. Mitchell, J.R., D.A. Shepherd, and M.P. Sharfman. 2011. Erratic strategic decisions: when and why managers are inconsistent in strategic decision making. Strategic Management Journal 32 (7): 683–704.CrossRefGoogle Scholar
  111. Moorman, C. 1995. Organizational market information processes: cultural antecedents and new product outcomes. Journal of Marketing Research 32 (3): 318–355.CrossRefGoogle Scholar
  112. Moorman, C., R. Deshpande, and G. Zaltman. 1993. Factors affecting trust in market research relationships. Journal of Marketing 57 (1): 81–101.CrossRefGoogle Scholar
  113. Muthén, L.K., and B.O. Muthén. 2014. Mplus user’s guide. Muthén and Muthén: Los Angeles.Google Scholar
  114. Myers, J.G., S.A. Greyser, and W.F. Massy. 1979. The effectiveness of marketing’s R and D for marketing management. Journal of Marketing 43 (1): 17–29.Google Scholar
  115. Nunnally, J.C. 1978. Psychometric theory. New York: McGraw-Hill.Google Scholar
  116. Oppenheim, C. 1997. Managers’ use and handling of information. International Journal of Information Management 17 (4): 239–248.CrossRefGoogle Scholar
  117. O’Reilly, C.A., and J.A. Chatman. 1994. Working smarter and harder: a longitudinal study of managerial success. Administrative Science Quarterly 39 (4): 603–627.CrossRefGoogle Scholar
  118. Parasuraman, A., D. Grewal, and R. Krishnan. 2007. Marketing research. New York: Houghton Mifflin.Google Scholar
  119. Payne, J.W., J.R. Bettman, and E.J. Johnson. 1993. The adaptive decision maker. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
  120. Perkins, W.S., and R.C. Rao. 1990. The role of experience in information use and decision making by marketing managers. Journal of Marketing Research 27 (1): 1–10.CrossRefGoogle Scholar
  121. Perreault Jr., W.D. 1992. The shifting paradigm in marketing research. Journal of the Academy of Marketing Science 20 (4): 369.CrossRefGoogle Scholar
  122. Raphael, J., and I.R. Parket. 1991. The need for market research in executive decision making. Journal of Business and Industrial Marketing 6 (1/2): 15–21.CrossRefGoogle Scholar
  123. Rapp, A., R. Agnihotri, T.L. Baker, and J. Andzulis. 2014. Competitive intelligence collection and use by sales and service representatives: how managers’ recognition and autonomy moderate individual performance. Journal of the Academy of Marketing Science 43 (3): 357–374.CrossRefGoogle Scholar
  124. Rivers, D. 2012. Notes on inferences with non-probability samples, presentation at the Annual Conference of the American Association of Public Opinion Research.Google Scholar
  125. Robinson, C.V., and J.E.L. Simmons. 2017, Organizing environmental scanning: exploring information source, mode and the impact of firm size. Long Range Planning. online first, ISSN 0024-6301.Google Scholar
  126. Rossiter, J.R. 2002. The C-OAR-SE procedure for scale development in marketing. International Journal of Research in Marketing 19 (4): 305–335.CrossRefGoogle Scholar
  127. Said, E., E.K. Macdonald, H.N. Wilson, and J. Marcos. 2015. How organizations generate and use customer insight. Journal of Marketing Management 31 (9/10): 1158–1179.CrossRefGoogle Scholar
  128. Satorra, A., and P.M. Bentler. 1994, Corrections to test statistics and standard errors in covariance structure analysis. Proceedings of the Business and Economic Statistics Section of the American Statistical Association, 308–313.Google Scholar
  129. Schlegelmilch, B., K. Boyle, and S. Therevil. 1986. Marketing research in medium sized UK and US firms. Industrial Marketing Management 15 (3): 177–182.CrossRefGoogle Scholar
  130. Schmalensee, D.H., and A.D. Lesh. 1999. How to make research more actionable. Marketing Research 10 (4): 23–38.Google Scholar
  131. Sekaran, U., and R. Bougie. 2016. The role of theory and information in research. In Research methods for business: skill-building approach, ed. U. Sekaran and R. Bougie, 3–5. Chichester: Wiley.Google Scholar
  132. Selart, M., S.T. Johansen, T. Holmesland, and K. Gronhaug. 2008. Can intuitive and analytical decision styles explain managers’ evaluation of information technology? Management Decision 46 (9): 1326–1341.CrossRefGoogle Scholar
  133. Shah, S., A. Horne, and J. Capella. 2012. Good data won’t guarantee good decisions. Harvard Business Review 90 (4): 45.Google Scholar
  134. Sinclair, M., and N.M. Ashkanasy. 2005. Intuition: myth or a decision-making tool? Management Learning 36 (3): 353–370.CrossRefGoogle Scholar
  135. Sinkula, J.M. 1994. Marketing information processing and organizational learning. Journal of Marketing 58 (1): 35–45.CrossRefGoogle Scholar
  136. Smith, B.D., and P.J. Raspin. 2008. Creating market insight: how firms create value from market understanding. Chichester: Wiley.Google Scholar
  137. Smith, D., and N. Culkin. 2001. Making sense of information: a new role for the marketing researcher? Marketing Intelligence and Planning 19 (4): 263–271.CrossRefGoogle Scholar
  138. Smith, G., and M. Callingham. 1994. Quality comes to the market research world: just in time or just too late? International Journal of Market Research 36 (4): 269–294.Google Scholar
  139. Sörbom, D. 1974. A general method for studying differences in factor means and factor structure between groups. British Journal of Mathematical and Statistical Psychology 27 (2): 229–239.CrossRefGoogle Scholar
  140. Souchon, A., and A. Diamantopoulos. 1996. A conceptual framework of export marketing information use: key issues and research propositions. Journal of International Marketing 4 (3): 49–71.CrossRefGoogle Scholar
  141. Souchon, A., and A. Diamantopoulos. 1997. Use and non-use of export information: some preliminary insights into antecedents and impact of export performance. Journal of Marketing Management 13 (1/3): 135–151.CrossRefGoogle Scholar
  142. Steiger, J., and J. Lind. 1980. Statistically based tests for the number of common factors. Paper presented at the Annual Meeting of the Psychometric Society.Google Scholar
  143. Sudman, S., and E. Blair. 1998. Marketing Research. Boston: McGraw Hill.Google Scholar
  144. Sutcliffe, K.M., and G. McNamara. 2001. Controlling decision-making practice in organizations. Organization Science 12 (4): 484–501.CrossRefGoogle Scholar
  145. Tarka, P. 2017. Managers’ beliefs about marketing research and information use in decisions in context of the bounded-rationality theory. Management Decision 55 (5): 987–1005.CrossRefGoogle Scholar
  146. Tarka, P. 2018. The views and perceptions of managers on the role of marketing research in decisions. International Journal of Market Research 60 (1): 774–784.CrossRefGoogle Scholar
  147. Tarka, P. 2019. Managers’ cognitive capabilities and perception of market research usefulness. Information Processing and Management 56 (3): 541–553.CrossRefGoogle Scholar
  148. Treiblmaier, H., and P. Filzmoser. 2010. Exploratory factor analysis revisited: how robust methods support the detection of hidden multivariate data structures in IS research. Information and Management 47 (4): 197–207.CrossRefGoogle Scholar
  149. Tucker, L.R., and C. Lewis. 1973. A reliability coefficient for maximum likelihood factor analysis. Psychometrika 38 (1): 1–10.CrossRefGoogle Scholar
  150. Tull, D., and D. Hawkins. 1985. Marketing research: measurement and method. New York: McMillan.Google Scholar
  151. Usunier, J.-C. 1993. Cross-cultural differences in market information research and use. CERAG Working Paper pp. 93–103.Google Scholar
  152. Van de Schoot, R., P. Lugtig, and J. Hox. 2012. A checklist for testing measurement invariance. European Journal of Developmental Psychology 9 (4): 486–492.CrossRefGoogle Scholar
  153. Vandenberg, R.J., and C.E. Lance. 2000. A review and synthesis of the measurement invariance literature: suggestions, practices, and recommendations for organizational research. Organizational Research Methods 3 (1): 4–69.CrossRefGoogle Scholar
  154. Walle, A.H. 1999. From marketing research to competitive intelligence: useful generalization or loss of focus? Management Decision 37 (6): 519–525.CrossRefGoogle Scholar
  155. Walliman, N. 2011. Research methods: the basics. New York: Routledge.Google Scholar
  156. Walsh, J.P. 1995. Managerial and organizational cognition: notes from a trip down memory lane. Organization Science 6 (3): 280–321.CrossRefGoogle Scholar
  157. Webb, J.R. 1992. Understanding and designing marketing research. UK: Academic Press.Google Scholar
  158. Wedel, M., and W. Kamakura. 2012. Market segmentation: conceptual and methodological foundations. New York: Springer.Google Scholar
  159. Wee, T.T. 2001. The use of marketing research and intelligence in strategic planning: key issues and future trends. Marketing Intelligence and Planning 19 (4): 245–253.CrossRefGoogle Scholar
  160. West, S.G., J.F. Finch, and P.J. Curran. 1995. Structural equation models with nonnormal variables: problems and remedies. In Structural equation modeling: concepts, issues and applications, ed. R. Hoyle, 56–75. Thousand Oaks: Sage Publications.Google Scholar
  161. Wheaton, B., B. Muthén, D.F. Alwin, and G.F. Summers. 1977. Assessing reliability and stability in panel models. Sociological Methodology 8: 84–136.CrossRefGoogle Scholar
  162. Wieland, A., C.F. Durach, J. Kembro, and H. Treiblmaier. 2017. Statistical and judgmental criteria for scale purification. Supply Chain Management: An International Journal 22 (4): 321–328.CrossRefGoogle Scholar
  163. Wierenga, B. 2011. Managerial decision making in marketing: the next research frontier. International Journal of Research in Marketing 28 (2): 89–101.CrossRefGoogle Scholar
  164. Wierenga, B., and G. van Bruggen. 1997. The integration of marketing problem solving modes and marketing management support systems. Journal of Marketing 61 (3): 21–37.CrossRefGoogle Scholar
  165. Wills, S., and P. Williams. 2004. Insight as a strategic asset: the opportunity and the stark reality. International Journal of Market Research 46 (4): 393–410.CrossRefGoogle Scholar
  166. Wilson, A. 2014. Bounded memory and biases in information processing. Econometrica 82 (6): 2257–2294.CrossRefGoogle Scholar
  167. Wilton, P.C., and J.H. Myers. 1986. Task, expectancy and information assessment effects in information utilization processes. Journal of Consumer Research 12 (4): 469–486.CrossRefGoogle Scholar
  168. Yeoh, P.L. 2000. Information acquisition activities: a study of global start-up exporting companies. Journal of International Marketing 8 (3): 36–60.CrossRefGoogle Scholar
  169. Yu, C.-Y., and B. Muthén. 2002. Evaluation of model fit indices for latent variable models with categorical and continuous outcomes. Report,
  170. Zaltman, G., and C. Moorman. 1988. The importance of personal trust in the use of research. Journal of Advertising Research 28 (5): 16–24.Google Scholar
  171. Zaltman, G., and V.P. Barabba. 1991. Hearing the voice of the market: competitive advantage through creative use of market information. Boston: Harvard Business School Press.Google Scholar
  172. Zikmund, W.G., and B.J. Babin. 2006. Exploring marketing research. Mason: Thompson.Google Scholar

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© Springer Nature Limited 2019

Authors and Affiliations

  1. 1.Department of Market ResearchPoznan University of Economics and BusinessPoznanPoland

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