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African Journal of Business Management Vol. 5(17), pp. 7511-7515, 4 September, 2011 Available online at http://www.academicjournals.org/AJBM DOI: 10.5897/AJBM11.439 ISSN 1993-8233 ©2011 <strong>Academic</strong> <strong>Journals</strong> Full Length Research Paper Factors of cross-buying intention - bancassurance evidence Chiang Ku Fan 1 , Li-Tze Lee 2 *, Yu-Chieh Tang 3 and Yu Hsuang Lee 4 1 Department of Risk Management and Insurance, Shih Chien University, China. 2 Department of Accounting and Information, Overseas Chinese University, Taiwan, China. 3 Graduate Institute of Management Sciences, Tamkang University, Taiwan, China. 4 Graduate Institution of Business Administration, Shih Chien University, Taipei, Taiwan. Accepted 15 April, 2011 The question of why bank customers decide to cross-buy and to enhance their relationship with a bank has received scant attention in the literature, and has not been appropriately investigated in prior studies. The first purpose of this study is to address this research gap by developing and assessing an objective research model to weigh the factors that affect intention of cross-buying insurance in banks. The second purpose of this study is to identify the resulting degree of satisfaction of these factors. The gaps between the factors of cross-buying intention and the resulting satisfaction are recognized by using the “Importance Performance Analysis.” On the basis of the findings, bancassurance strategy makers can identify and allocate the marketing or operational resources for an organization accordingly. Key words: Cross-buying, bancassurance, intention, conjoint analysis, importance performance analysis. INTRODUCTION The banking industry in Taiwan has experienced tremendous change and an increased growth in earnings from selling insurance products. Banking networks represent the major distribution channel for life insurance products. Most banks are looking for the same things - better ways to retain customers and to increase income. Similarly, most insurers are looking for the same things - more efficient distribution channels to sell policies and to expand premium incomes. To manage their relationship development efforts better, it is important for practitioners to understand the motivations that lead the customer to reduce their market choices and to engage in a relationship with a financial firm (Sheth and Parvatiyar, 1995). Cross-selling is the practice of promoting additional services or products to a firm’s existing customers, which are in addition to the ones that the customers already have (Butera, 2000). Selling additional products or services to existing customers may reduce the cost of *Corresponding author. E-mail: leelitze@ocu.edu.tw. customer acquisition and yield price advantages over competitors (Nbogo, 2004). In spite of cross-selling being associated with increased lifetime duration and value (Reinartz and Thomas, 2001), prior studies have implied that it is not easy to motivate customers to cross-buy services or products from the same provider. Unfortunately, the question of why customers decide to crossbuy and to enhance their relationships with a bank has received scant attention in the literature and has not been appropriately investigated in prior studies (Ngobo, 2004). Furthermore, no satisfaction evaluations of these factors that affect cross-buying intention have been conducted. There is, therefore, a significant research gap which has emerged in understanding the factors that motivate cross-buying intention (Soureli et al., 2008). Obviously, additional evidence and conceptualizations are necessary in this area. The first purpose of this study is to address this research gap by developing and assessing an objective research model to weigh those factors that affect intention of cross-buying insurance in banks that have been suggested in previous studies. The second purpose is to identify the degree of satisfaction of these factors
7512 Afr. J. Bus. Manage. Table 1. The factors impact cross-buying intention for bancassurance. Factors impact cross-buying intention References Image Ngobo, 2004; Soureli et al., 2008 Service convenience Dimitriadis, 2010; Berry et al., 2002; Ngobo, 2004; Liu and Wu, 2007 Interpersonal relationships Reinartz, 2003; Jeng, 2008 Trust Anderson and Weitz, 1989; Doney and Cannon, 1997; Liu and Wu, 2007, 2008; Soureli et al., 2008 Payment equity Bolton, 1998; Verhoef, 2001 Experience Anand and Cunnane, 2010; Verhoef, 2001; Ngobo, 2004 Pricing Verhoef, 2001; Jeng, 2008 Product variety Jeng, 2008; Kumar et al., 2008 that affect intention of cross-buying insurance in banks. LITERATURE REVIEW Verhoef et al. (2001) were the first to introduce the term “cross-buying” and defined it as the purchase of a number of different services from the same provider. In other words, cross-buying is the behavior expressed in buying various products from the same provider (Soureli et al., 2008; Verhoef et al., 2001). In fact, cross-selling and its benefits can only be achieved if consumers are willing to cross-buy (Polonsky et al., 2000). Therefore, cross-buying is complementary to cross-selling, which pertains to the supplier’s efforts to increase the number of products or services that a customer uses within a firm (Aurier and N' Goala, 2010; Kamakura et al., 2003). A number of factors that may impact bank customers’ cross-buying intentions have been proposed in previous research studies. The findings of these prior studies are reported in Table 1. A summary of the literature review with regard to factors that affect cross-buying intention is listed thus: 1. The tendency of most prior studies has been to examine variables such as image, service convenience, interpersonal relationships, trust, payment equity, experience, pricing, and product variety. 2. It is difficult to determine the relative importance of the weight or ranking of each cross-buying intention, because they were not defined in previous studies. 3. Prior studies have neither focused on evaluating the strategies of stimulating the customer’s cross-buying intentions, nor suggested how bancassurance decisionmakers should allocate marketing resources. METHODOLOGY The estimation model in this study consists of three phrases. In the first phrase, the factors of cross-buying intention for bancassurance are identified using the modified Delphi method. In the second phrase, the factors' weights of cross-buying intention are also used as the evaluation criteria and are calculated effectively by employing the “Conjoint Analysis.” In the third phrase, the gaps between the factors' weights of cross-buying intention and the customers’ satisfaction are recognized by using the “Importance Performance Analysis.” The main theoretical approaches adopted herein are described, as shown in Figure 1. RESULTS To identify the factors that affect intention of cross-buying insurance in banks, this study applied a purposive sampling technique and selected 23 financial advisers who were employed by different model banks and have many years of experience working with bancassurance. The interviews were conducted either through e-mail or faceto-face. Respondents were asked to justify their answers to interview questions and to rate their level of agreement toward the factors of cross-buying intentions, ranging from strongly agree (SA) (5) to strongly disagree (SD) (1). The interview protocol was developed on the basis of the literature review. On the basis of the result of a Wilcoxon Signed-Rank test, no significant attitude difference toward each factor of cross-buying intention was found between round 2 and round 3. This means that the 8 items proposed by this study can be identified as factors that affect intention of cross-buying insurance in banks according to the Delphi study procedures. All factors that affect cross-buying intention were ranked SA by most of the Delphi panelists in the 3 rd round of the interviews. The CA questionnaire was developed on the basis of some of the literature, planned with an orthogonal design, and distributed to 300 customers. Two hundred and sixtynine questionnaires were completed in the survey. According to the CA report, the most important factor was image (relative importance = 36.172%), the second most important factor was service convenience (relative importance = 11.099%) and the third most important factor was interpersonal relationships (relative importance = 10.514 %). The purpose of the attitude survey was to collect data with regard to the performance of each factor that affect intention of cross-buying insurance in the bank. The
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African Journal of Business Managem
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Editors Prof. Wilfred Isioma Ukpere
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Electronic submission of manuscript
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Fees and Charges: Authors are requi
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Table of Contents: Volume 5 Number
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7166 Afr. J. Bus. Manage. the high
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7168 Afr. J. Bus. Manage. Cargo mov
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7170 Afr. J. Bus. Manage. has produ
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7172 Afr. J. Bus. Manage. resources
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7174 Afr. J. Bus. Manage. celebrate
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7176 Afr. J. Bus. Manage. effective
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7178 Afr. J. Bus. Manage. interacti
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7180 Afr. J. Bus. Manage. firstly,
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7182 Afr. J. Bus. Manage. TQM pract
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7184 Afr. J. Bus. Manage. with lowe
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7186 Afr. J. Bus. Manage. in a diff
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7190 Afr. J. Bus. Manage. Decision
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7192 Afr. J. Bus. Manage. restructu
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7194 Afr. J. Bus. Manage. Performan
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7196 Afr. J. Bus. Manage. firm perf
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African Journal Of Business Managem
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7200 Afr. J. Bus. Manage. to establ
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7202 Afr. J. Bus. Manage. marketing
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7204 Afr. J. Bus. Manage. initial a
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7206 Afr. J. Bus. Manage. from the
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7208 Afr. J. Bus. Manage. Phillips
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7210 Afr. J. Bus. Manage. of the sa
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7212 Afr. J. Bus. Manage. ′ w k m
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7214 Afr. J. Bus. Manage. procedure
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7218 Afr. J. Bus. Manage. Figure 2.
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7220 Afr. J. Bus. Manage. stimulate
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7222 Afr. J. Bus. Manage. these com
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7228 Afr. J. Bus. Manage. Table 2.
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7234 Afr. J. Bus. Manage. CRM resea
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7238 Afr. J. Bus. Manage. The one a
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7242 Afr. J. Bus. Manage. and Norto
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7250 Afr. J. Bus. Manage. DEFINING
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7256 Afr. J. Bus. Manage. critical
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7260 Afr. J. Bus. Manage. external
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7262 Afr. J. Bus. Manage. represent
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7270 Afr. J. Bus. Manage. business
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7274 Afr. J. Bus. Manage. Satisfact
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7276 Afr. J. Bus. Manage. Score of
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7282 Afr. J. Bus. Manage. Satisfact
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7292 Afr. J. Bus. Manage. determine
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7298 Afr. J. Bus. Manage. Procedure
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7308 Afr. J. Bus. Manage. required
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7318 Afr. J. Bus. Manage. expectati
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7320 Afr. J. Bus. Manage. Compensat
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7324 Afr. J. Bus. Manage. Psychol.,
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7326 Afr. J. Bus. Manage. the resea
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7328 Afr. J. Bus. Manage. Z( T, whe
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7330 Afr. J. Bus. Manage. (10) and
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7332 Afr. J. Bus. Manage. during wh
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7334 Afr. J. Bus. Manage. optimal v
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7336 Afr. J. Bus. Manage. satisfact
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7340 Afr. J. Bus. Manage. female re
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7352 Afr. J. Bus. Manage. ln S = ln
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7356 Afr. J. Bus. Manage. but some
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7358 Afr. J. Bus. Manage. investiga
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7360 Afr. J. Bus. Manage. when conf
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7362 Afr. J. Bus. Manage. PCs in a
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7364 Afr. J. Bus. Manage. supported
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7366 Afr. J. Bus. Manage. Psychol.,
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7368 Afr. J. Bus. Manage. social pr
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Table 2. Companies and countries co
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to CSR clearly and reliably. The st
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consumers to participate with the t
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A Liao 7379 Figure 1. Example of ev
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Table 3. Summary statistics of the
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Table 5. Utilities for subject 1 Li
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REFERENCES Anderson JC, Narus JA (1
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7386 Afr. J. Bus. Manage. aggregate
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7388 Afr. J. Bus. Manage. Analysis
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(Bass and Avolio, 1994). Leaders fo
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the employees. When employees are e
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Table 1. Means, standard deviation
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negativity in the organization. Tra
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of psychological ownership. Thus te
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Meyer JP, Becker TE, Vandenberghe C
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goods industry in the future. LITER
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F + 2 0. 110 * F + Table 3. Factor
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APPENDIX Data for evaluation on the
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anks. Camanho and Dyson (2005) enha
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Step A-4: Correlation analysis Star
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Table 1. Original raw data of input
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Table 5. Scenario table. Wang and W
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Delmas M, Toka Y (2005). Deregulati
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Technology managers need to attract
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λ is the parameter for risk aversi
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specified ranges of return, for eac
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Carlsson C, Fulle´r R, Majlender P
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Stock Exchange, (Rao, 2010) in Nati
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etween size and unsystematic risk i
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The research schema Figure 1 indica
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Table 3. Result of Freidman rank te
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Table 8. The result of Spearman cor
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significant differences between the
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leadership. The process of manageri
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indices of quality of life work. On
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Figure 2. The values of standardize
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Table 4. The values of coaching ind
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Table 1. Contrast between hedonic a
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in consumers’ shopping behavior.
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et al., 2006; Carpenter, 2008). For
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Table 2. contd Shopping satisfactio
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value in the construct of clothing
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Page 326 and 327: Dean AM (2004). Rethinking customer
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Page 330 and 331: (7) Table 1. Descriptive statistics
Page 332 and 333: APPENDIX A Table 1. Failure rate te
Page 336 and 337: Table 1. Results of the multivarian
Page 338 and 339: Figure 3. Entrepreneurship as a fun
Page 340 and 341: Furkukawa K, Shafer WE, Lee GM (200
Page 342 and 343: happen to firms of any and all size
Page 344 and 345: participants attributed their failu
Page 346 and 347: Table 1. Descriptive statistics of
Page 348 and 349: Table 4 Contd. Inconsideration of l
Page 350 and 351: Table 5 Contd. Manufacturing 2.17 1
Page 354 and 355: The CLRP is an NP-hard problem, so
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Page 358 and 359: Table 1. Computational results of G
Page 362 and 363: Table 1. Correlation of facets of C
Page 366 and 367: Figure 1. Theoretical approach adop
Page 368 and 369: quadrant (III), including product v
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Page 372 and 373: signal. This hypothesis predicts a
Page 374 and 375: Variables are defined as in the ful
Page 376 and 377: Table 3. The results of model (2).
Page 378 and 379: around the world. Q. Rev. Econ. Fin
Page 380 and 381: tourism industry development. In th
Page 382 and 383: Table 2. Canonical correlation. 2 C
Page 384 and 385: Table 5. Bilateral analysis and ran
Page 388 and 389: strategies (Hart, 1995; Tello and Y
Page 390 and 391: Table 1. Reliability and construct
Page 392 and 393: Table 3. Latent variables test. She
Page 394 and 395: Haines R, Street M, Haines D (2008)
Page 396 and 397: owned insurance firm to enter the m
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Page 400 and 401: Figure 1. Structural relationships
Page 402 and 403: Prior to analysis, variables were e
Page 404 and 405: Table 1. Effect decomposition for M
Page 406 and 407: Figure 5. Unstandardized estimates
Page 408 and 409: prospects. In other words, less wor
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Sale Lean Supply Chain (LSC) Growth
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Upper Saddle River: Pearson Prentic
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the green awareness movement by ado
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usiness practices than smaller firm
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Table 3. Descriptive statistics and
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China. Green leadership could be em
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APPENDIX Appendix 1: Measures of ob
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Emotional intelligence Self monitor
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ship between a people’s perceived
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Table 1. Analysis of the reliabilit
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Table 4. Results from hypothesis te
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Ajzen I, Driver BL (1992). Applicat
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mono production economy, and rural
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Table 2. Pearson correlation betwee
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Table 1. Behavioral Phenomenon that
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Table 3. Test period data. Table 4.
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Prospect value Figure 3. Expected v
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Tversky A, Gilovich T (1989). The H
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Table 1. Components of a mentoring
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Table 3. Perceptions on the success
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Table 5. Characteristics of a mento
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terms of sustained development, are
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Table 7. Ideal characteristics of m
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qualitative analysis of fifteen hou
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section. In Section 3, we will disc
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Table 1. Participants’ demographi
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of Am. Acad. of Bus., Cambridge. 4(
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directors and propose the board ind
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composition is the outcome of a neg
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Table 1. Descriptive statistics. Ti
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Table 3. The determinants of board
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Booth JR, Cornett M, Tehranian H (2
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provides a wider response and swift
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Correlation “Customary Approach
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Table 2. Correlation matrix of EAC
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Table 9. Intra-EAC imports as share
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Martin JC (1999). The European Mone
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participate in the highest degree o
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innovation. A secondary innovation
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Figure 2. The switching ecology of
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Table 1. Revenue growth rate and CA
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variables (Beattie and Jones, 1999a
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Table 3. Explanations of dependent
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Courtis JK (1997). Corporate annual
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treated in the literature, its rela
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company. METHODOLOGY Table 1. Quest
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as an important factor in the manag
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satisfaction may also reduce the ov
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Table 1. Customer expectations on s
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Table 2. Contd. Machirori and Fatok
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and travelling may lead to satisfac
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and professionally developmental pr
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Table 1. Organizational elements mo
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Table 2. Studying the demographical
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education might involve plenty of c
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(Deng et al., 2010). To date, there
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Table 2. Results of hierarchical re
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WOM. The comparison can be seen in
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UPCOMING CONFERENCES International
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