Abstract
This paper presents a comprehensive integrative framework for measuring the performance of customer relationship management (CRM) system based on a detailed empirical study of 32 Iranian internet service provider (ISP) firms. At first, by an extensive literature review and experts who have real practical experiences in the field of CRM, appropriate key performance indicators (KPIs) based on four perspectives of balanced scorecard have been extracted. Then, due to the interdependency and feedback among these KPIs, multiple criteria decision making techniques are used to evaluate CRM performance. Specifically, this study first applies the decision making trial and evaluation laboratory approach to determine the interrelated relationships among criteria, and to find the crucial central and influential factors. Then, the analytical network process method is used to obtain the criterion weights. Finally, according to these previous results, the technique for order of preference by similarity to ideal solution method is adopted to analyze the CRM performance of 32 Iranian ISP firms. The results of this study illustrated that learning and growth is the most crucial influential perspective and it would influence on the other perspectives much more. Furthermore, indicators including organization capital, human capital, customer retention process, customer perceived value, and customer expansion process play an essential role in succeeding of CRM. The results of this study can provide a comprehensive insight for managers into discerning how and through which mechanisms CRM can create merits for the organizations.
Similar content being viewed by others
Notes
In this section, a questionnaire including an integer score ranging from 1 to 5 (1-lowest level, 5-highest level) was designed for each perspective and indictor in order to rank 32 ISP firms. Then, twelve experts that have been working in ICT sector for many years filled these questioners for each 32 company. After, gathering and averaging all experts’ opinions, the results were considered as TOPSIS input.
References
Arab F, Selamat H, Zamani M (2010) An overview of success factors for CRM. In: 2010 2nd IEEE international conference on information and financial engineering (ICIFE), IEEE, pp 702–705
Becker JU, Greve G, Albers S (2009) The impact of technological and organizational implementation of CRM on customer acquisition, maintenance, and retention. Int J Res Mark 26(3):207–215
Bentes AV, Carneiro J, da Silva JF, Kimura H (2012) Multidimensional assessment of organizational performance: integrating BSC and AHP. J Bus Res 65(12):1790–1799
Boulding W, Staelin R, Ehret M, Johnston WJ (2005) A customer relationship management roadmap: what is known, potential pitfalls, and where to go. J Mark 69(4):155–166
Brewton J, Schiemann WA (2003) Measurement: the missing ingredient in today’s CRM strategies. J Cost Manag 17(1):5–14
Büyüközkan G, Çifçi G (2012) A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers. Expert Syst Appl 39(3):3000–3011
Camarero Izquierdo C, Gutiérrez Cillán J, San Martin Gutierrez S (2005) The impact of customer relationship marketing on the firm performance: a Spanish case. J Serv Mark 19(4):234–244
Chang W, Park JE, Chaiy S (2010) How does CRM technology transform into organizational performance? A mediating role of marketing capability. J Bus Res 63(8):849–855
Chen IJ, Popovich K (2003) Understanding customer relationship management (CRM): people, process and technology. Bus Process Manag J 9(5):672–688
Chen FH, Hsu TS, Tzeng GH (2011) A balanced scorecard approach to establish a performance evaluation and relationship model for hot spring hotels based on a hybrid MCDM model combining DEMATEL and ANP. Int J Hosp Manag 30(4):908–932
Chiu WY, Tzeng GH, Li HL (2013) A new hybrid MCDM model combining DANP with VIKOR to improve e-store business. Knowl Based Syst 37:48–61
Coltman T (2007) Why build a customer relationship management capability? J Strateg Inf Syst 16(3):301–320
Daniel DR (1961) Management information crisis. Harv Bus Rev 39(5):111–121
Dawkins P, Reichheld F (1990) Customer retention as a competitive weapon. Dir Boards 14(4):42–47
Ergu D, Kou G, Shi Y, Shi Y (2011) Analytic network process in risk assessment and decision analysis. Comput Oper Res 42:58–74
Garrido-Moreno A, Padilla-Meléndez A (2011) Analyzing the impact of knowledge management on CRM success: the mediating effects of organizational factors. Int J Inf Manag 31(5):437–444
Greve G, Albers S (2006) Determinants of performance in customer relationship management—assessing the technology usage-performance link. In: Proceedings of the 39th annual Hawaii international conference on system sciences, 2006 (HICSS’06), vol. 6, IEEE, pp 111b–111b
Hassan A (2012) The value proposition concept in marketing: how customers perceive the value delivered by firms–a study of customer perspectives on supermarkets in Southampton in the United Kingdom. Int J Mark Stud 4(3):p68
Hsu HC (2007) Using strategy maps to assess CRM implementation: a case study of an international IT company in Taiwan (Doctoral dissertation, University of Nottingham)
Hwang CL, Yoon K (1981) Multiple attribute decision making: methods and applications, a state of the art survey. Springer, New York
Hwang CL, Lai YJ, Liu TY (1993) A new approach for multiple objective decision making. Comput Oper Res 20(8):889–899
Jahanshahloo GR, Lotfi FH, Izadikhah M (2006) Extension of the TOPSIS method for decision-making problems with fuzzy data. Appl Math Comput 181(2):1544–1551
Jain R, Jain S, Dhar U (2002) Measuring customer relationship management. J Serv Res 2(2):97–109
Kaplan RS, Norton D (1992) The balanced scorecard measures that drive performance. Harv Bus Rev 70(1):71–79
Kaplan RS, Norton DP (1996) Linking the balanced scorecard to strategy. Calif Manag Rev 39(1):53–79
Keramati A, Salehi M (2013) Website success comparison in the context of e-recruitment: an analytic network process (ANP) approach. Appl Soft Comput 13(1):173–180
Keramati A, Sangari MS (2011) A success framework to investigate critical factors associated with implementation of customer relationship management: a fuzzy ANP approach. Int J Cust Relatsh Mark Manag (IJCRMM) 2(2):43–62
Keramati A, Mehrabi H, Mojir N (2010) A process-oriented perspective on customer relationship management and organizational performance: an empirical investigation. Ind Mark Manag 39(7):1170–1185
Kim HS, Kim YG (2009) A CRM performance measurement framework: its development process and application. Ind Mark Manag 38(4):477–489
Kim J, Suh E, Hwang H (2003) A model for evaluating the effectiveness of CRM using the balanced scorecard. J Interact Mark 17(2):5–19
McFarlane DA (2013) The strategic importance of customer value. Atl Mark J 2(1):5
Mendoza LE, Marius A, Pérez M, Grimán AC (2007) Critical success factors for a customer relationship management strategy. Inf Softw Technol 49(8):913–945
Mithas S, Krishnan MS, Fornell C (2005) Why do customer relationship management applications affect customer satisfaction? J Mark 69(4):201–209
Ngai EW, Xiu L, Chau DC (2009) Application of data mining techniques in customer relationship management: a literature review and classification. Expert Syst Appl 36(2):2592–2602
Niven PR (2002) Balanced scorecard step-by-step: maximizing performance and maintaining results. Wiley, New York
Opricovic S, Tzeng GH (2004) Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS. Eur J Oper Res 156(2):445–455
Ou Yang YP, Shieh HM, Leu JD, Tzeng GH (2009) A VIKOR-based multiple criteria decision method for improving information security risk. Int J Inf Technol Decis Mak 8(02):267–287
Öztayşi B, Kaya T, Kahraman C (2011) Performance comparison based on customer relationship management using analytic network process. Expert Syst Appl 38(8):9788–9798
Payne A, Frow P (2005) A strategic framework for customer relationship management. J Mark 167–176
Reinartz W, Krafft M, Hoyer WD (2004) The customer relationship management process: its measurement and impact on performance. J Mark Res 41(3):293–305
Richards KA, Jones E (2008) Customer relationship management: finding value drivers. Ind Mark Manag 37(2):120–130
Rigby DK, Reichheld FF, Schefter P (2002) Avoid the four perils of CRM. Harv Bus Rev 80(2):101–109
Roh TH, Ahn CK, Han I (2005) The priority factor model for customer relationship management system success. Expert Syst Appl 28(4):641–654
Saaty TL (1996) Decision making with dependence and feedback: the analytic network process
Saaty TL (1999) Decision making for leaders: the analytic hierarchy process for decisions in a complex world, vol 2. RWS Publications
Saaty TL (2001) The analytic network process: decision making with dependence and feedback. RWS Publications
Salojärvi H, Sainio LM, Tarkiainen A (2010) Organizational factors enhancing customer knowledge utilization in the management of key account relationships. Ind Mark Manag 39(8):1395–1402
Sin LY, Alan CB, Yim FH (2005) CRM: conceptualization and scale development. Eur J Mark 39(11/12):1264–1290
Sumrit D, Anuntavoranich P (2013) Using DEMATEL method to analyze the causal relations on technological innovation capability evaluation factors in thai technology-based firms. Int Trans J Eng Manag Appl Sci Technol 4(2):081–103
Tosun OK, Gungor A, Topcu YI (2008) ANP application for evaluating Turkish mobile communication operators. J Glob Optim 42(2):313–324
Tseng ML (2010) Implementation and performance evaluation using the fuzzy network balanced scorecard. Comput Educ 55(1):188–201
Tzeng GH, Chiang CH, Li CW (2007) Evaluating intertwined effects in e-learning programs: a novel hybrid MCDM model based on factor analysis and DEMATEL. Expert Syst Appl 32(4):1028–1044
Wang Y, Feng H (2012) Customer relationship management capabilities: measurement, antecedents and consequences. Manag Decis 50(1):115–129
Wang YL, Tzeng GH (2012) Brand marketing for creating brand value based on a MCDM model combining DEMATEL with ANP and VIKOR methods. Expert Syst Appl 39(5):5600–5615
Wang Y, Lo HP, Chi R, Yang Y (2004) An integrated framework for customer value and customer-relationship-management performance: a customer-based perspective from China. Manag Serv Qual 14(2/3):169–182
Wu WW (2008) Choosing knowledge management strategies by using a combined ANP and DEMATEL approach. Expert Syst Appl 35(3):828–835
Wu HY (2012) Constructing a strategy map for banking institutions with key performance indicators of the balanced scorecard. Eval Progr Plan 35(3):303–320
Wu SI, Lu CL (2012) The relationship between CRM, RM, and business performance: a study of the hotel industry in Taiwan. Int J Hosp Manag 31(1):276–285
Wu HY, Tzeng GH, Chen YH (2009a) A fuzzy MCDM approach for evaluating banking performance based on Balanced Scorecard. Expert Syst Appl 36(6):10135–10147
Wu HY, Tzeng GH, Chen YH (2009b) A fuzzy MCDM approach for evaluating banking performance based on balanced scorecard. Expert Syst Appl 36(6):10135–10147
Wu HY, Lin YK, Chang CH (2011) Performance evaluation of extension education centers in universities based on the balanced scorecard. Eval Progr Plan 34(1):37–50
Yang JL, Tzeng GH (2011) An integrated MCDM technique combined with DEMATEL for a novel cluster-weighted with ANP method. Expert Syst Appl 38(3):1417–1424
Yoon K (1987) A reconciliation among discrete compromise solutions. J Oper Res Soc 38(3):277–286
Yosefi NAM, Bagheri M, Neishabouri JE (2012) A decision making model for outsourcing of manufacturing activities by ANP and DEMATEL under fuzzy environment. Int J Eng Prod Res 23(3):163–174
Yüksel İ, Dagdeviren M (2007) Using the analytic network process (ANP) in a SWOT analysis–a case study for a textile firm. Inf Sci 177(16):3364–3382
Zablah AR, Bellenger DN, Johnston WJ (2004) An evaluation of divergent perspectives on customer relationship management: towards a common understanding of an emerging phenomenon. Ind Mark Manag 33(6):475–489
Author information
Authors and Affiliations
Corresponding author
Appendices: A brief summary of questionnaires
Appendices: A brief summary of questionnaires
1.1 Appendix 1: Instructions for filling out the questionnaire
The present questionnaires are designed to assess the impact of CRM on organizational performance based on the indicators listed in Table 13. In front of each index, elements of each factor are described. Your contributions will help to enrich the results.
1.2 Appendix 2: DEMATEL questionnaires
1.2.1 Comparison of the impact of the indicators
With respect to your experience in implementing CRM, please compare the impact of the 11 criterions using 0–4 integer score ranging from 1, 2, 3, and 4, representing ‘No influence (0),’ ‘Low influence (1),’ ‘Medium influence (2),’ ‘High influence (3),’ and ‘Very high influence (4),’ respectively. [Please fill out the compared level of 11 criterions in the following table (Table 14)].
For example, the impact of customer satisfaction on the customer loyalty is “Very high”, therefore 4 is filled out in the box (Table 15).
1.2.2 Comparison of the impact of the perspectives
With respect to your experience in implementing CRM, please compare the impact of the 4 perspectives using 0-4 integer score ranging from, 1, 2, 3, and 4, representing ‘No influence (0),’ ‘Low influence (1),’ ‘Medium influence (2),’ ‘High influence (3),’ and ‘Very high influence (4),’ respectively. (Please fill out the compared level of 4 perspectives in the following table) (Table 16).
1.3 Appendix 3: ANP questionnaires
1.3.1 Pair wise comparison questionnaire
With respect to your experience in the field of CRM, please fill out the pair wise comparison questionnaires (Tables 17, 18).
1.4 Appendix 4: TOPSIS questionnaire Footnote 1
With respect to your experience in the field of ISP industry, please fill out the questionnaire for each ISP firm (Tables 19, 20).
Rights and permissions
About this article
Cite this article
Keramati, A., Shapouri, F. Multidimensional appraisal of customer relationship management: integrating balanced scorecard and multi criteria decision making approaches. Inf Syst E-Bus Manage 14, 217–251 (2016). https://doi.org/10.1007/s10257-015-0281-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10257-015-0281-8