Antecedents and consequences of CRM technology acceptance in the sales force

Industrial Marketing Management 34 (2005) 355 368 Antecedents and consequences of CRM technology acceptance in the sales force George J. Avlonitis*, Nikolaos G. Panagopoulos 1 Department of Marketing and Communication, Athens University of Economics and Business, Evelpidon 47 and Leykados 33, Athens 113 62, Greece Received 19 April 2004; received in revised form 10 August 2004; accepted 21 September 2004 Available online 2 March 2005 Abstract Two conceptual approaches [Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13 (3), 319 340; DeLone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Information Systems Research, 3 (1), 60 95] are unified into a conceptual model that offers a comprehensive explanation of CRM acceptance antecedents and consequences in a sales force setting. Based upon responses from 240 salespersons that utilize a CRM system, the model is tested and explanations are offered for the system s acceptance. Specifically, the most prevailing influence on CRM acceptance comes from CRM perceived usefulness, followed by the setting of accurate expectations regarding system usage, the salesperson innovativeness towards new technological tools, the CRM perceived ease-of-use, and the supervisor encouragement and support. Surprisingly, the model does not adequately explicate salesperson performance. Sales managers are presented with a discussion and implications of the findings. D 2004 Elsevier Inc. All rights reserved. Keywords: Salesperson performance; CRM; Information technology acceptance 1. Introduction Undoubtedly, the last 10 years have been characterized by an unprecedented shift in the sales function of the business-to-business organization (Ingram, LaForge, & Leigh, 2002), largely due to the introduction of information systems [IS] into the industrial sales organization (Honeycutt, 2002; Marshall, Moncrief, & Lassk, 1999). These systems have been positioned in the sales force literature as a powerful tool for increasing sales productivity (Hise & Reid, 1994). Thus, it is not surprising that some researchers have underlined the importance of conducting research into the Sales Force Automation (SFA) domain (e.g., Marshall, Michaels, Stone, & Jawahar, 2001). Yet, research on SFA/ * Corresponding author. Tel.: +30 210 823 1931; fax: +30 210 820 3607. E-mail addresses: avlonitis@aueb.gr (G.J. Avlonitis)8 npanag@aueb.gr (N.G. Panagopoulos). 1 Tel.: +30 210 820 3650; fax: +30 210 820 3851. CRM 2 applications is very limited (Ingram et al., 2002), in spite of the critical role of CRM systems on building and sustaining effective customer relationships and the fact that while most CRM implementations constitute a great investment for the sales organization, they fail to be accepted by the sales force (Speier & Venkatesh, 2002). Research efforts in the area of industrial SFA systems, are focusing either on the antecedents of SFA adoption and acceptance (e.g., Morgan & Inks, 2001) or on the consequences of the SFA implementation (e.g., Keillor, Bashaw, & Pettijohn, 1997). By reviewing the sales/ marketing literature it becomes apparent that no published study has simultaneously examined the antecedents and the consequences of the SFA/CRM technology acceptance for 2 The terms SFA and CRM are used interchangeably in the sales literature. Basically, CRM is a business strategy that consists of processes and technologies that enhance customer relationships, while SFA deals only with technological tools. In this paper, we focus on the technological elements of CRM (Ingram et al., 2002), recognizing that CRM is a much broader [although related] concept to SFA. 0019-8501/$ - see front matter D 2004 Elsevier Inc. All rights reserved. doi:10.1016/j.indmarman.2004.09.021

356 G.J. Avlonitis, N.G. Panagopoulos / Industrial Marketing Management 34 (2005) 355 368 the industrial salespersons. Moreover, most of the published studies are largely descriptive in nature and have concentrated on technology adoption at the organization level (e.g., Pullig, Maxham, & Hair, 2002; Rivers & Dart, 1999), thus paying less attention to the factors leading individuals to adopt new technological tools. Two notable exceptions are the recent works of Jones, Sundaram, and Chin (2002) and Speier and Venkatesh (2002). Finally, a closer examination of both the information systems and the sales management literature reveals that there is a dearth of knowledge regarding the impact of information technology on individual performance. Therefore, the purpose of this paper is twofold: firstly, to examine the factors that lead to the effective acceptance of CRM technology and, secondly and simultaneously, to investigate the impact of its implementation on the individual sales representative performance. The structure of the paper is organized as follows: first, we review the relevant literature, in order to develop the conceptual framework and the research hypotheses of our study. Next, we present the methodology and the results of the empirical study. Finally, the study findings are discussed both from a theoretical and a managerial standpoint, and several suggestions for future research in this important area, are presented. 2. Theory development and model description Researchers have studied technology acceptance using a variety of theoretical frameworks. However, the most widely used theoretical framework is the Technology Acceptance Model (Davis, 1989), which presents a list of factors that lead to technology acceptance and use. On the other hand, DeLone and McLean (1992) have offered a parsimonious model for IS success, which demonstrates the effect of use and user satisfaction with information systems, on user-performance. Apparently, system use, which is a key variable in most of the theoretical frameworks in the IS literature, is common in the two models and therefore it could serve as a basis for their integration. Fig. 1 presents the conceptual model of our study, which is based on the work of Davis (1989) and DeLone and McLean (1992), and which will be analyzed further in developing our research hypotheses. 2.1. CRM acceptance antecedents During the last 20 years researchers from a diverse set of disciplines, such as information systems research, innovation research, and social/organizational psychology, have devoted a great deal of effort in order to uncover the determinants of individual technology acceptance. This vast amount of research has produced a large number of theoretical frameworks; however, the most widely applied theoretical framework is the Technology Acceptance Model [TAM] (Davis, 1989). Davis, based upon the Theory of Reasoned Action (Ajzen, 1985), developed the TAM, which is specifically designed for explaining individual technology acceptance decisions across a wide range of technologies, user populations, and contexts. The TAM has been applied to a vast number of work settings with great success, during the last 15 years (Hu, Clark, & Ma, 2003). 2.1.1. Salesperson beliefs According to TAM, usage behavior is determined by intentions towards using the system, while intention is jointly determined by two related beliefs: perceived ease-ofuse [PEOU] and perceived usefulness [PU]. PEOU is bthe degree to which an individual believes that using a particular system would be free of physical and mental effort,q while PU is defined as bthe degree to which an individual believes that using a particular system will enhance his/her job performanceq (Davis, 1989). PU is influenced by PEOU because, other things being equal, the easier the system is to use the more useful it can be (Davis, Bagozzi, & Warshaw, 1989; Igbaria & Guimaraes, 1995). Therefore, we hypothesize that: Hypothesis 1. CRM perceived ease-of-use will positively influence CRM perceived usefulness. Social Factors Supervisor Influence Peers Influence Competition Influence H3a H3b CRM Perceived Ease-of-use H2a Organizational Factors Training User Participation Accurate Expectations H4a H4b H1 H6a H6b CRM User Satisfaction H7 CRM Acceptance H9 Salesperson Performance Individual Factors Computer Experience Computer Self -Efficacy Innovativeness H5a H5b CRM Perceived Usefulness H2b H8 Fig. 1. Conceptual model.

G.J. Avlonitis, N.G. Panagopoulos / Industrial Marketing Management 34 (2005) 355 368 357 PEOU and PU can have a direct effect on usage, as the salesperson that perceives a system to be easy-to-use and anticipates some performance benefits from using this system, will tend to use it more (Davis, 1989; Speier & Venkatesh, 2002). The rationale for the link between PEOU and usage is grounded on innovation theory, which suggests that the degree that an innovation is perceived as relatively difficult to understand and use would affect the rate of its adoption (Rogers, 1995). Similarly, the theoretical justification for the PU usage relationship is based on expectancy theory (Porter & Lawler, 1968), which states that within organizational settings, people evaluate the consequences of their behavior in terms of potential rewards, and they base their choice of behavior on the desirability of the rewards. Thus, a salesperson will tend to use a system if he/she believes that it will enhance his/her job performance. Drawing on the above, we hypothesize that: Hypothesis 2a. CRM perceived ease-of-use will positively influence CRM acceptance. Hypothesis 2b. CRM perceived usefulness will positively influence CRM acceptance. 2.1.2. External factors A key point in TAM theorization is that the effect of external variables on intention to use is mediated by PU and PEOU (Venkatesh & Davis, 2000). This argument is grounded on the conceptualizations of facilitating conditions (Triandis, 1980) and perceived behavior control (Ajzen s, 1985). According to these two conceptualizations, external factors [i.e. individual, organizational, and social characteristics] will affect usage behavior through their effects on the person s belief structure [i.e. PU and PEOU]. More specifically, the presence or the absence of requisite resources [e.g., training], skills [e.g., previous experience], and opportunities [e.g., company support], will constrain the person s intention to perform a specific behavior; that is to accept and use an information system. Many researchers have suggested that extending the original TAM model with variables from the external environment can provide a more complete picture of the technology acceptance process (Hu et al., 2003). Although these suggestions differ in specific content, several variables are repeatedly mentioned as central to the successful acceptance of CRM systems, and which can be effectively grouped into the following three categories: a] Social factors (e.g., Jones et al., 2002; Schillewaert, Ahearne, Frambach, & Moenaert, 2000), b] Organizational factors (e.g., Hartwick & Barki, 1994; Venkatesh & Davis, 2000) and c] Individual factors (e.g., Igbaria & Guimaraes, 1995; Rogers, 1995). 2.1.2.1. Social factors. Triandis (1980) stated that behavior is influenced by social norms [factors] that is, bthe individual s internalization of the reference groups subjective culture, and specific interpersonal agreements that the individual has made with others, in specific social situationsq [p. 210]. In a sales force context the potential reference groups are the sales supervisor, the competitive salespersons, and peers. Pullig et al. (2002), for instance, found that encouragement to use the SFA system was the second most important factor in creating the required enabling conditions for system acceptance by the sales force. Moreover, in situations where salespersons perceive a highly competitive situation, as indicated by the competitive salespersons use of CRM systems, adoption of the CRM technology may become an imperative. Finally, working in a sales department where most of a salesperson s peers are using the system, will impact on his/her perceptions concerning both the usefulness and the ease-of-use of the system (Jones et al., 2002). Indeed, when the majority of the salesperson s peers have adopted the CRM system, it is easier for him/her to ask others about how to use the system and therefore reduce learning time. Based on the above discussion we formulated the following hypotheses: Hypothesis 3a. Social factors [supervisor, competition, and peers] will positively influence CRM perceived ease-of-use. Hypothesis 3b. Social factors [supervisor, competition, and peers] will positively influence CRM perceived usefulness. 2.1.2.2. Organizational factors. There is a myriad of organizational factors, which can impact on both the salesperson belief structure and his/her acceptance decision. In the present study, we suggest that organizational training on CRM, user participation in the implementation process, and accurate expectations setting, are pivotal factors in the acceptance of the CRM system. Empirical evidence that supports the effect of these factors on the salesperson s belief structure and acceptance decision can be found in the literature. Specifically, training employees on system usage has consistently been related with user beliefs towards the system (e.g., Davis et al., 1989; Igbaria, 1990; Igbaria & Guimaraes, 1995) and acceptance (Igbaria, Pavri, & Huff, 1989; Morgan & Inks, 2001; Pullig et al., 2002). It seems that training helps salespeople to better understand system operations, as well as the benefits that they will obtain from using the system. Considering that salespeople are among the less computer-literate professionals, the impact of training on their beliefs may well be amplified. User participation, which has been defined as the various design related behaviors and activities that target users or their representatives perform during the system development process (Hartwick & Barki, 1994), is considered as being a critical factor in [a] successful technological adoption, [b] increased levels of satisfaction (e.g., Doll & Torkzadeh, 1989), and [c] enhanced perceptions of the system usefulness (Speier & Venkatesh, 2002). This is

358 G.J. Avlonitis, N.G. Panagopoulos / Industrial Marketing Management 34 (2005) 355 368 accomplished by understanding the system, developing realistic expectations about system capabilities, decreasing user resistance to change, and committing users to the system (McKeen, Guimaraes, & Wetherbe, 1994). Indeed, salespersons that perceive that they participate in the system implementation process will feel, at least to some extent, owners of this change. Those feelings of ownership will increase the likelihood that the salespersons will use the system. Research evidence from the broader sales force literature supports the notion that role accuracy/clarity impacts on salesperson attitudes and behaviors (Behrman & Perreault, 1984). Based on these findings we can speculate that setting accurate expectations concerning the CRM system, will contribute to greater system usage. Indeed, in situations where salespersons know exactly what to expect from system implementation, the process of system acceptance will be smoother and more effective (Morgan & Inks, 2001). Thus, drawing on the above discussion we hypothesize that: Hypothesis 4a. Organizational factors [training, user participation, and accurate expectations setting] will positively influence CRM perceived ease-of-use. Hypothesis 4b. Organizational factors [training, user participation, and accurate expectations setting] will positively influence CRM perceived usefulness. 2.1.2.3. Individual factors. Obviously, salespeople are not all the same; rather, they differ both in personal dispositions and in acquired knowledge. Therefore, even if two members of the same sales organization confront a given organizational and social environment, in terms of the implementation of a new system, individual characteristics will have a significant effect on the way they perceive this new information system and, subsequently, on their desire to accept it. Computer self-efficacy refers to an individual s perception regarding his/her ability to use a computer (Compeau & Higgins, 1995). Research findings from previous studies suggest a positive relationship between self-efficacy and user beliefs (e.g., Hu et al., 2003; Speier & Venkatesh, 2002). The theoretical justification for this linkage is grounded on self-efficacy theory (Bandura, 1977), which states that in the absence of system experience, the confidence in one s computer related abilities and knowledge can be expected to serve as a basis for an individual s judgment about how easy or difficult a new system will be to use. Another individual disposition is personal innovativeness, which assesses the degree to which a person believes that he/she is positively predisposed toward the use of new technologies. Actually, personal innovativeness is an indicator of risk seeking behavior (Agarwal & Prasad, 1998). Given that technological innovations are inherently risky, only those persons that are more risk seekers will demonstrate positive beliefs towards using the CRM technology (Jones et al., 2002; Rogers, 1995). This happens because innovative persons can more easily assess the usefulness of a system and usually have more experience in using information systems. Individual characteristics, such as computer experience, will play an important role in the acceptance of an information system. Computer experience refers to an individual s perception of his/her experience with computers. It is well documented that computer experience will positively influence a person s belief structure regarding an information system (e.g., Igbaria, 1990; Igbaria & Guimaraes, 1995). Thus, based on the reviewed literature, the following hypotheses are proposed: Hypothesis 5a. Individual factors [i.e. computer selfefficacy, innovativeness, and computer experience] will positively influence CRM perceived ease-of-use. Hypothesis 5b. Individual factors [i.e. computer selfefficacy, innovativeness, and computer experience] will positively influence CRM perceived usefulness. 2.1.3. CRM user satisfaction CRM user satisfaction is frequently employed as a surrogate of IS success and refers to the extent to which the system meets the needs of the users (Doll & Torkzadeh, 1988). There are many variables that have been identified in previous studies as direct antecedents of end-user satisfaction. User participation (Baroudi & Orlikowski, 1988; Doll & Torkzadeh, 1989; McKeen et al., 1994), perceived usefulness (Rai, Lang, & Welker, 2002), and management/organizational support (Etezadi- Amoli & Farhoomand, 1996) are hypothesized to positively influence user satisfaction with the system. Obviously, salespersons who believe that the system is easy-to-use and useful will tend to hold a more positive attitude [i.e. satisfaction] towards the system. Therefore, we state that: Hypothesis 6a. CRM perceived ease-of-use will positively influence user satisfaction. Hypothesis 6b. CRM perceived usefulness will positively influence user satisfaction. Conversely, user satisfaction has been found to determine system usage in a vast number of studies (e.g., Etezadi- Amoli & Farhoomand, 1996; Igbaria & Tan, 1997; Rai et al., 2002). It is reasonable to expect that a salesperson that feels satisfied with the information that the system provides, regarding his/her customer profiles, will use and finally accept the system, since he/she expects that using the system will help him/her to better perform his/her tasks. Drawing on the above we state: Hypothesis 7. User satisfaction will positively influence CRM acceptance.

G.J. Avlonitis, N.G. Panagopoulos / Industrial Marketing Management 34 (2005) 355 368 359 2.2. User satisfaction and CRM acceptance impact on salesperson performance Empirical support for the nomological relationship of both user satisfaction and system acceptance with individual performance, can be found in a large number of studies (DeLone & McLean, 2003). Specifically, the results of these studies demonstrate that usage is positively related to individual performance (Goodhue & Thompson, 1995; Igbaria & Tan, 1997; Rai et al., 2002) although in at least one study the two constructs were not shown to be related (e.g., Gelderman, 1998). Moreover, user satisfaction has been found to influence user performance (Etezadi-Amoli & Farhoomand, 1996; Gelderman, 1998; Igbaria & Tan, 1997). Drawing on the above discussion we state: Hypothesis 8. User satisfaction will positively influence salesperson performance and Hypothesis 9. CRM acceptance will positively influence salesperson performance. 3. Methodology 3.1. Research design In the present study, we concentrated on a specific CRM application, developed by one of the largest worldwide vendors of CRM systems for the pharmaceutical industry. This vendor supplies pharmaceutical companies with notebook-based CRM systems, which are being used by pharma sales forces on a non-mandatory basis. By focusing on a specific CRM application, we excluded the possibility that the study will be affected by different CRM characteristics (i.e. design characteristics, system quality]. Moreover, we decided to collect data from one selling context [i.e. prescribed drugs selling], to exclude possible contaminations resulting from mixing heterogeneous selling situations into one sample (Moncrief, 1986). 3.2. Data collection We collected data from five pharmaceutical companies, which used this system. All the selected companies had implemented the particular CRM system at least 1 year ago and had a structured and organized information technology [IT] department (Brynjolfsson & Yang, 1996). Specifically, we asked the sales managers of the respective companies to distribute a research packet, which contained an anonymous [self-administering] questionnaire and a pre-paid envelope stamped with the University address, to all members of their sales force that were users of the CRM system [379 in total]. This procedure resulted in 240 useful responses or a 63.3% overall response rate. The sample can be described as follows: a majority of the salespeople were male [80.7%], most were younger than 39 years old [75%], and a few salespeople [approximately 4%] were more than 50 years old. With respect to job tenure, approximately 41% of the sample had been with the organization for less than 2 years, and 26% had been with the company between 7 and 20 years. Finally, more than half the salespeople [57%] had a University or College degree. To evaluate nonresponse bias, we assumed that response nonresponse differences might be manifested to some degree between early and late responses (Armstrong & Overton, 1977). Late responses were those which resulted from our follow-up efforts. Specifically, 150 responses were early and 90 were late. By comparing the demographic profile [using chi-square tests] and the mean values of all the remaining study variables [using t-tests], between early and late respondents, we found no significant differences [at the 0.05 level]. Thus, nonresponse bias is an unlikely problem. 3.3. Research instrument development measures The development of the research instrument was based mainly on new scales, because we could not identify any past sales force studies directly addressing all of the issues in this research. However, and where possible, we used validated measures that have been previously applied. In the Appendix we present the constructs, scale items, factor loadings obtained from exploratory factor analysis, and sources used. Two consecutive rounds of pre-testing were conducted in order to insure that members of the sales forces could understand the measurement scales used in the study: First, we arranged a meeting with three of the vendor s executives [CEO, sales manager, and customer service manager] and next, we arranged a meeting with the sales managers from all five participating companies. The outcome of the pretesting process was a slight modification and alteration of the existing scales, in light of the sales context under investigation. 3.4. Data analysis results First, the psychometric properties of the constructs were assessed by calculating the Cronbach s alpha [a] reliability coefficient and the items-to-total correlation (Gerbing & Anderson, 1988; Nunnally & Bernstein, 1994). This procedure resulted in the elimination of two items from the PU scale and of one item from the user satisfaction scale, the inclusion of which decreased the reliability coefficients. As can be seen in Table 1, all scales have reliability coefficients ranging from 0.67 to 0.94, which exceed the cut-off level of 0.60 set for basic research (Bagozzi, 1994, p. 96). Next, we performed an exploratory factor analysis (see Appendix) on all of the items [with oblimin rotation] to

360 G.J. Avlonitis, N.G. Panagopoulos / Industrial Marketing Management 34 (2005) 355 368 Table 1 Means, standard deviations, Pearson intercorrelations a, and coefficient alphas b of study measures Measures 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Means S.D. 1. Supervisor influence 0.90 4.22 0.73 2. Peer influence 0.21 4.36 0.72 3. Competition influence 0.26 0.21 0.72 2.93 0.69 4. CRM training 0.36 0.29 0.26 0.67 4.22 0.57 5. User participation 0.14 0.10 0.23 0.17 0.79 2.01 0.86 6. Accurate expectations 0.36 0.23 0.32 0.65 0.37 0.89 3.79 0.76 7. Computer experience 0.02 0.05 0.07 0.20 0.06 0.14 3.38 1.11 8. Computer self-efficacy 0.03 0.11 0.03 0.02 0.00 0.08 0.41 0.78 2.94 0.68 9. Innovativeness 0.12 0.05 0.18 0.19 0.17 0.26 0.45 0.46 0.84 3.26 0.87 10. Perceived ease-of-use 0.36 0.15 0.26 0.56 0.21 0.62 0.14 0.14 0.30 0.88 3.80 0.70 11. Perceived usefulness 0.27 0.03 0.25 0.40 0.34 0.52 0.17 0.09 0.31 0.62 0.94 2.72 0.87 12. CRM acceptance 0.34 0.04 0.32 0.36 0.20 0.43 0.15 0.21 0.43 0.49 0.51 0.75 3.67 0.70 13. Satisfaction with timeliness of information 0.29 0.11 0.29 0.44 0.34 0.54 0.09 0.00 0.26 0.72 0.56 0.45 0.88 3.58 0.77 14. Satisfaction with system capabilities 0.38 0.15 0.35 0.55 0.28 0.64 0.11 0.03 0.24 0.75 0.69 0.53 0.69 0.83 3.58 0.66 15. Salesperson performance 0.08 0.13 0.14 0.23 0.11 0.28 0.18 0.09 0.18 0.33 0.45 0.24 0.33 0.37 0.76 3.76 0.54 Correlations shown in bold are significant at least at the 0.05 level. Coefficient alphas are shown in the diagonal. a b examine if the items for a construct share a single underlying factor [i.e. are unidimensional]. Items, which did not satisfy the following two criteria, were deleted: [1] dominant loadings greater than 0.5, and [2] cross-loadings less than 0.35 (cf. Hair, Anderson, Tatham, & Black, 1998, p. 112). Indeed, bpeer influence on CRM useq and bcomputer experienceq did not load on their underlying factor, and were excluded from further analysis. Thirteen factors were extracted [explaining more than 74% of the extracted variance] by using an eigenvalue of 1.0 as the cut-off point, and by a careful inspection of the scree plot. All factor loadings are generally high, and the lowest loading is equal to 0.53, while the Kaiser Meyer Olkin test of the factor analysis is substantial [0.902]. It should also be noted that the user satisfaction scale was separated into two separate factors. Specifically, the analysis demonstrated that there are two distinct dimensions: satisfaction with timeliness of the data and satisfaction with the system capabilities/operations. Finally, a confirmatory factor analysis [CFA] with reflective indicators (Bollen & Lennox, 1991) was performed (Anderson & Gerbing, 1988). We used covariances as input and the maximum likelihood method in LISREL 8.54 to estimate coefficients. The results of the confirmatory procedure (Table 2), revealed the following fit-statistics: RMSEA=0.062, CFI=0.96, TLI=0.94, RNI=0.93, and NNCP=0.94. Taken together, and given the complexity of the model, these statistics indicate an acceptable fit of the model (Sharma, Mukherjee, Kumar, & Dillon, in press). Without exception, measurement parameters are substantively large and statistically significant [t-values from 2.95 to 12.08], which provide evidence of convergent validity (Table 2). Discriminant validity is obtained because all pair-wise latent-indicator correlations (see Table 1) are Table 2 Measurement model results: confirmatory factor analysis Measurement model Composite reliability Lowest t-value a Lowest squared multiple correlation Supervisor influence 0.91 3.76 0.63 Competition influence 0.74 11.40 0.59 CRM training 0.69 11.40 0.28 User participation 0.79 12.08 0.62 Accurate expectations 0.88 3.72 0.53 Computer self-efficacy 0.79 3.72 0.54 Innovativeness 0.84 11.40 0.52 Perceived ease-of-use 0.89 3.72 0.54 Perceived usefulness 0.96 3.90 0.48 CRM acceptance 0.75 3.76 0.62 Satisfaction with timeliness 0.90 3.76 0.61 of information Satisfaction with system 0.83 2.95 0.26 capabilities Salesperson performance 0.77 3.76 0.67 Fit-statistics: X 2 [df]=2191.16 [1144], Pb.00; RMSEA=0.062; CFI=0.96; TLI=0.94; NNCP=0.94; RNI=0.93. a All t-values are significant at least at the 0.05 level.

G.J. Avlonitis, N.G. Panagopoulos / Industrial Marketing Management 34 (2005) 355 368 361 significantly different from one (Singh & Rhoads, 1991). Additional evidence for the discriminant validity of the measures was provided by a series of pair-wise confirmatory factor analyses. Specifically, for each pair of measures, the chi-square difference test (Anderson & Gerbing, 1988) produced a significant result when items of different constructs were forced into a single-factor model. Taken together with the results of the reliability and exploratory factor analysis, the psychometric evaluation of the measurement scales is indicative of a well-specified measurement model with adequate measurement properties [reliability, unidimensionality, convergent, and discriminant validity]. Given that the purpose of the study is to test the hypothesized causal relationships among the constructs of the model, an appropriate statistical analysis is structural equation modeling with latent variables. We initially estimated the hypothesized model shown in Fig. 1. The results of this analysis are displayed on Table 3. The fitstatistics of the model are indicative of a good fit. However, an inspection of the modification indices and the standardized residuals demonstrated that significant paths exist [a] between perceived usefulness and performance, [b] between user participation and user satisfaction with the timeliness of information, and [c] between several variables [i.e. supervisor influence, competition influence, CRM training, innovativeness, and accurate expectations] and CRM acceptance. Therefore, we added these direct paths in the hypothesized model and deleted all trivial paths [i.e. those that were not significant at 0.05] and re-estimated our model. The results of the btrimmedq model are presented on Table 4 and are schematically depicted in Fig. 2. Moreover, we include a summary of the indirect and total effects of the hypothesized independent variables, which is displayed in Table 5. Hypothesis 1 suggests that CRM PEOU has a positive effect on CRM PU. Indeed, this relationship was found to be quite strong [b=0.62, Pb0.001]. That is, the more easy-to-use a system is the more useful it is perceived by salespersons; thereby, providing support for Hypothesis 1. However, regarding the effect of salespersons beliefs on CRM acceptance (Hypotheses 2a and 2b), we observe that only PU has a significant impact on acceptance (Hypothesis 2b), thus providing only partial support for Hypotheses 2a and 2b. Indeed, PU has the most prevailing total effect on CRM Table 3 Standardized structural coefficients of hypothesized model Criterion variable Predictor variables Hypothesized relationship Standardized coefficients a t-value a [standard error] Perceived ease-of-use Supervisor influence [+] 0.11 1.96 [0.058] 0.64 Competition influence 0.077 1.25 [0.061] CRM training 0.14 0.75 [0.19] User participation 0.035 0.59 [0.059] Accurate expectations 0.57 2.98 [0.19] Computer self-efficacy 0.090 1.30 [0.069] Innovativeness 0.065 1.02 [0.064] Perceived usefulness Perceived ease-of-use [+] 0.56 5.20 [0.11] 0.54 Supervisor influence 0.034 0.54 [0.063] Competition influence 0.089 1.35 [0.066] CRM training 0.0013 0.0069 [0.20] User participation 0.18 2.83 [0.65] Accurate expectations 0.10 0.48 [0.21] Computer self-efficacy 0.013 0.17 [0.074] Innovativeness 0.11 1.52 [0.069] Satisfaction with timeliness Perceived usefulness [+] 0.044 0.65 [0.068] 0.73 of information Perceived ease-of-use 0.82 9.43 [0.087] Satisfaction with system Perceived usefulness [+] 0.22 3.60 [0.061] 0.91 operations/capabilities Perceived ease-of-use 0.79 9.40 [0.084] CRM acceptance Perceived usefulness [+] 0.30 2.77 [0.11] 0.51 Perceived ease-of-use 0.55 1.60 [0.34] Satisfaction with timeliness 0.12 0.91 [0.14] of information Satisfaction with system 0.029 0.083 [0.35] operations/capabilities Salesperson performance CRM acceptance [+] 0.092 0.84 [0.11] 0.046 Satisfaction with timeliness 0.080 0.56 [0.14] of information Satisfaction with system 0.072 0.43 [0.17] operations/capabilities Fit-statistics: X 2 [df]=2318.45 [1175], Pb0.00; RMSEA=0.064; CFI=0.96; TLI=0.93; NNCP=0.96; RNI=0.93. a Coefficients in bold are statistically significant. t-values greater than 1.645 indicate significant effects at the 0.05 level, for a one-tailed test. b This is the total variance explained in the referent dependent variable based on the hypothesized model. R 2b

362 G.J. Avlonitis, N.G. Panagopoulos / Industrial Marketing Management 34 (2005) 355 368 Table 4 Standardized structural coefficients of trimmed model Criterion variable Predictor variables Hypothesized relationship Standardized coefficients a t-value a [standard error] Perceived ease-of-use Supervisor influence [+] 0.13 2.33 [0.056] 0.61 Accurate expectations 0.72 10.28 [0.070] Perceived usefulness Perceived ease-of-use [+] 0.62 8.84 [0.070] 0.47 User participation 0.24 3.98 [0.059] Satisfaction with timeliness Perceived ease-of-use [+] 0.78 11.36 [0.069] 0.77 of information User participation 0.23 4.63 [0.050] Satisfaction with system Perceived usefulness [+] 0.22 3.77 [0.059] 0.91 operations/capabilities Perceived ease-of-use 0.78 9.49 [0.083] CRM acceptance Perceived usefulness [+] 0.31 4.57 [0.067] 0.61 CRM training 0.20 1.20 [0.16] Supervisor influence 0.16 2.59 [0.062] Competition influence 0.15 2.42 [0.061] Accurate expectations 0.14 0.82 [0.17] Innovativeness 0.19 2.88 [0.064] Salesperson performance Perceived usefulness [+] 0.30 3.89 [0.076] 0.087 Fit-statistics: X 2 [df]=2255.40 [1186], Pb0.00; RMSEA=0.061; CFI=0.96; TLI=0.93; NNCP=0.96; RNI=0.93. a Coefficients in bold are statistically significant. T values greater than 1.645 indicate significant effects at the 0.05 level., for a one tailed test. b This is the total variance explained in the referent dependent variable based on the hypothesized model. R 2b acceptance (Table 5). However, PEOU has a significant indirect effect on acceptance, mainly through PU. Hypotheses 3a and 3b propose that social factors have a positive effect on both PEOU and PU. Although the effect of supervisor influence on PEOU is significant [c=0.13, Pb0.05], the rest of the hypothesized relationships were not supported. However, bsupervisor influenceq and bcompetition influenceq have a significant, although not hypothesized, direct effect on CRM acceptance. Hypotheses 4a and 4b suggest that organizational factors influence salesperson beliefs. As the results of the structural model analysis suggest, organizational factors have a partial influence on PEOU and PU. More analytically, accurate expectations influence perceived ease-ofuse, while user participation in the CRM implementation process influences perceived usefulness, thus providing partial support of Hypotheses 4a and 4b. However, although not initially hypothesized, accurate expectations Supervisor Influence 0.13 0.15 0.16 Competition Influence 0.72 CRM Perceived Ease-of-use 0.78 0.23 CRM User Satisfaction With timeliness of data User Participation 0.24 0.62 0.78 0.31 CRM Acceptance Salesperson Performance Accurate Expectations CRM Perceived Usefulness 0.22 CRM User Satisfaction With systemís operations/capabilities 0.19 0.30 Personal Innovativeness Notes: path initially not hypothesized Only significant paths are shown Fig. 2. Final trimmed model.

G.J. Avlonitis, N.G. Panagopoulos / Industrial Marketing Management 34 (2005) 355 368 363 Table 5 Indirect and total effects of hypothesized independent variables on CRM acceptance and salesperson performance [trimmed model] Criterion variable Predictor variables Indirect effects a Total effects a CRM acceptance Supervisor influence 0.02 0.19 Competition influence 0.15 CRM training 0.20 User participation 0.07 0.07 Accurate expectations 0.14 0.28 Innovativeness 0.19 Perceived ease-of-use 0.19 0.19 Perceived usefulness 0.31 Satisfaction with system operations/capabilities Satisfaction with timeliness of information Salesperson Supervisor influence 0.02 0.02 performance Competition influence CRM training User participation 0.07 0.07 Accurate expectations 0.13 0.14 Innovativeness Perceived ease-of-use 0.18 0.18 Perceived usefulness 0.30 Satisfaction with system operations/capabilities Satisfaction with timeliness of information CRM acceptance a Effects in bold are statistically significant at the 0.05 level. The numbers in the table are standardized coefficients. setting was found to have an important total effect on CRM acceptance (Table 5). With regard to the impact of personal factors on user beliefs (Hypotheses 5a and 5b) we observe that there is no significant effect of personal innovativeness and computer self-efficacy on PEOU and PU. Thereby, Hypotheses 5a and 5b are not supported. However, it was found that personal innovativeness has a significant direct effect on CRM acceptance. Hypotheses 6a and 6b suggests that PEOU (Hypothesis 6a) and PU (Hypothesis 6b) would be positively associated with user satisfaction. Specifically, Hypothesis 6a is strongly supported by the significant correlation between PEOU and both dimensions of satisfaction. However, Hypothesis 6b is only partially supported, as PU is related only to satisfaction with the system capabilities/operations. Moreover, the residual analysis revealed another interesting positive relationship between salesperson participation in the CRM implementation process and satisfaction with the timeliness of the data. Hypothesis 7 considers the antecedent effect of user satisfaction on CRM acceptance. Obviously, none of the examined dimensions of user satisfaction with the system is significantly associated with CRM acceptance, thus providing no support for Hypothesis 7. On the other hand, the residual analysis revealed that PU, bsupervisor influence,q bcompetition influence,q and binnovativenessq are positively associated with CRM acceptance. User satisfaction does not have any direct impact on salesperson performance, offering no support for Hypothesis 8. Similarly, CRM acceptance is not significantly associated with performance, thus providing no support for Hypothesis 9. However, and as suggested by the residual analysis, PU is strongly associated with salesperson performance [b=0.30; Pb0.001] and, in fact, this is the only variable that has a direct impact on performance. 4. Discussion and implications The purpose of this article is [a] to enhance our understanding of the antecedents of CRM acceptance and its impact on salesperson performance, and thus [b] offer some useful and practical guidelines for the sales organizations wishing to successfully implement CRM systems and realize performance gains. 4.1. CRM technology acceptance Overall, the study variables explain 61% of the CRM acceptance variance, which is in accordance with the results of previous studies [e.g., Davis et al., 1989). Among the antecedent variables of acceptance, the most prevailing effect comes from salesperson s beliefs towards the system (Jones et al., 2002). That is, salespeople that perceive a CRM system to be easy-to-use and useful in conducting their activities are more likely to adopt it and use it in their day-to-day activities. This finding has major implications for sales management, as it suggests that managers should strive to determine the informational needs of their salespeople and develop positive user beliefs about the system. This can be accomplished in several ways. First, and most importantly, sales organizations must focus on the development of accurate expectations regarding system usage and benefits, so that CRM users have an unambiguous picture of what management expects from system implementation. Second, salespeople should take part in the system design and implementation phase, so as to commit themselves and develop realistic expectations about the system. Finally, sales supervisors have a major role to accomplish in the system acceptance process, by supporting and encouraging salespeople to use the system. Moreover, the study findings clearly demonstrate that more innovative salespeople, who are early adopters of new technological innovations, are more likely to adopt a CRM system. Apparently, managers should pay attention to the innovative characteristics of salespeople in the recruitment and hiring process. Moreover, management must pay attention to the demographic composition of the sales force [e.g., age and education], as younger and more educated salespersons are expected to be more familiar with new technological tools.

364 G.J. Avlonitis, N.G. Panagopoulos / Industrial Marketing Management 34 (2005) 355 368 Surprisingly and in sharp contrast with previous findings (e.g., Gelderman, 1998; Igbaria et al., 1989), user satisfaction with the CRM system and CRM training, are not significantly associated with CRM acceptance. It is quite likely that satisfaction with the system and training are necessary but not sufficient conditions for CRM acceptance by industrial salespersons. Apparently, merely training salespersons on CRM use or providing them with a superior CRM system, will not lead to system acceptance, if salespeople do not perceive the system to be useful and easy-to-use. 4.2. CRM technology impact on performance The results clearly demonstrate that a salesperson s beliefs regarding CRM ease-of-use and CRM usefulness have a catalytic influence on sales performance. More analytically, the most important influence on performance comes from PU, followed by PEOU. In particular, the degree to which users believe that the CRM system is useful and easy-to-use leads to performance improvements. Probably, the low complexity of a CRM system [i.e. ease-of-use] saves time from non-selling activities [e.g., administrative tasks] and, subsequently, improves productivity. In addition, a CRM system that is perceived as performance enhancing, and therefore has more functional capabilities, can help the salesperson to improve his/her work outcomes. Moreover, accurate expectations setting indirectly influences sales performance. Apparently, clarification of the way the system can be used for performing sales tasks and of what the management expects from its use, can boost performance. However, the non-significant linkage between CRM acceptance and salesperson performance was unexpected. Contrary to the findings of past studies in IS research (Igbaria & Tan, 1997), acceptance of a CRM technology does not lead to performance improvements. It seems that merely infusing a CRM system into the sales force is just not enough to boost sales performance. A possible explanation for the absence of association between CRM acceptance and salesperson performance may be attributed to the complex process through which information technology improves performance, and which has been termed as the binformation technology productivity paradoxq (Brynjolfsson & Yang, 1996). More analytically, a CRM system can create intangible benefits, such as employee motivation, smoother interaction between departments, and enhancement of the company image (Rivers & Dart, 1999), which are very difficult to measure or capture by the bnaiveq measures of performance used in most of the studies. Another paradoxical finding, which parallels the insignificant relationship of job satisfaction with performance (Brown & Peterson, 1993), is the absence of association between CRM user satisfaction and salesperson performance. This result, combined with the non-significant effect of acceptance on performance, creates doubts concerning the nomological validity of the DeLone and McLean (1992) model and its applicability in industrial sales contexts. Overall, the insignificant effect of most of the variables on salesperson performance, which is reflected in the relatively low explained variance [R 2 =8.7%], provides evidence of the need for further theoretical development regarding the consequences of CRM acceptance on sales force performance. Although it is too early to conclude that CRM systems do not have an impact on the performance of business-to-business salespersons, sales organizations must critically examine their current needs in information technology and the process through which technology can be delineated with sales strategy. Finally, sales organizations must recognize the fact that the salesperson is the binternal customerq of any CRM system, whose needs and beliefs must be understood, managed, and eventually satisfied, if performance improvements are to be realized. 5. Limitations and suggestions for further research As with any study, there are certain limitations that should be recognized. First, we assessed sales performance by using a four-item measure, while there is evidence that performance is a much broader construct that includes behavior (Behrman & Perreault, 1982) and extra-role dimensions (MacKenzie, Podsakoff, & Ahearne, 1998). Second, the present study relied on a sample of medical salespersons and, consequently, we cannot afford to generalize the findings in other types of selling situations. Third, the data are cross-sectional in nature and hence it is not possible to determine causal relationships. The direction for future research, which emerged from our findings, is concerned with the development of a comprehensive model that explains the impact of CRM systems on salesperson performance. Obviously, the current theoretical model explains more of the process that leads to CRM acceptance than of what drives performance. Given the high costs associated with the implementation of CRM systems on industrial sales organizations, there is an urgent need to uncover the process through which technology influences sales force performance. It is our contention that the theory construction must build upon previous accumulated knowledge regarding the determinants of salesperson performance and rely less on IS success models. To this end, a very promising research approach is the development of a model that explains how CRM technology, through the provision of information, influences variables such as role ambiguity (e.g., Behrman & Perreault, 1984), job satisfaction (Brown & Peterson, 1993), procedural/declarative knowledge (Szymanski, 1988), and knowledge structures (Sujan, Sujan & Bettman, 1988). Acknowledgements The authors express their appreciation to Earl D. Honeycutt [Associate- and Special Issue Editor] and the

G.J. Avlonitis, N.G. Panagopoulos / Industrial Marketing Management 34 (2005) 355 368 365 two anonymous IMM reviewers for their insightful and constructive comments on an earlier version of this manuscript. The authors would also like to thank the participating companies for their cooperation. Appendix. Scale items, factor loadings, and sources Construct/Items Factor Loadings Source Supervisor influence [variance extracted: 33.85%] My supervisor continuously encourages me to use the system 0.90 New scale based on Schillewaert et al. (2000) My supervisor clearly 0.92 advocates over system usage My supervisor continuously 0.81 refers to the importance of using the system to conduct my work activities My supervisor believes that there are true merits from using the system 0.89 Peer influence The majority of my peers are using the system a 0.34 New scale based on Schillewaert et al. (2000) Competition influence [variance extracted: 6.81%] Competitive sales reps are extensively using CRM systems 0.89 New scale based on Schillewaert et al. (2000) Competitive sales reps are 0.90 equipped with state-of-theart CRM systems Competitive sales reps are using CRM systems during the contact with the physicians 0.84 Organizational CRM training [variance extracted: 5%] My company has extensively trained me in using the CRM system 0.69 New scale based on Goodhue and Thompson (1995), I am not adequately trained to 0.83 Igbaria et al. (1989) understand and effectively use the system b The training that was provided by my company has helped me to understand management s expectations regarding system use 0.78 User participation [variance extracted: 4.4%] Company s management has not took notice of my opinion and my needs before 0.81 New scale based on Hartwick and Barki (1994) deciding to buy and implement the system b I have actively participated in the system s buying decision process 0.81 Appendix (continued) Construct/Items Factor Loadings Source Accurate expectations [variance extracted: 3.8%] I know exactly what management expects from me, regarding system usage 0.79 New scale based on Rizzo, House, and Lirtzman (1970) I know exactly what are the 0.79 merits and benefits that will result from system usage During system implementation, 0.86 company s management clarified what are its expectations from me I have received clear goals, 0.71 regarding system usage I feel certain regarding of what I should expect from system usage 0.67 Computer experience I consider myself an 0.30 New scale based on experienced computer user a Igbaria et al. (1989) Self-efficacy [variance extracted: 3.6%] I could complete the job using the system... Adapted from Compeau and If there was no one around to tell me what to do 0.91 Higgins (1995) and Hu et al. (2003) If I had someone else using it 0.92 before trying it myself b If someone else had helped 0.85 me started b If I had just the built-in help 0.84 facility for assistance b If someone showed me how to do it first b 0.72 Personal innovativeness [variance extracted: 3.4%] If I heard about a new 0.83 Jones et al. (2002) information technology, I would look for ways to experiment with it Among my peers, I am usually 0.91 the first to try out new information technology In general, I consider myself quite innovative when it comes to information technology 0.84 Perceived ease-of-use [variance extracted: 3.3%] My interaction with the system is clear and understandable 0.65 Venkatesh and Davis (2000), Rai Interacting with the system 0.60 et al. (2002) does not require a lot of my mental effort I find the system to be easy to 0.78 use I find it easy to get the system 0.66 to do what I want to do I find the system user friendly 0.82 (continued on next page)

366 G.J. Avlonitis, N.G. Panagopoulos / Industrial Marketing Management 34 (2005) 355 368 Appendix (continued) Construct/Items Factor Loadings Source Perceived usefulness [variance extracted: 3.2%] Please, indicate the extent to which the CRM system has helped you to: New scale based on Rai et al. (2002), Torkzadeh and Doll Decrease the time needed to conduct your activities 0.76 (1999), Venkatesh and Davis (2000) Increase your productivity 0.85 Work more, something that 0.82 could not be accomplished without the system Create new ideas 0.82 Find solutions in problems 0.85 that you face Better satisfy your customer s 0.79 needs Improve the quality of your 0.86 daily work Improve sales presentations 0.68 Perform better, in terms of 0.81 sales outcomes Analyze your customers in 0.73 your assigned territory Prepare sales call reports a 0.46 Analyze competition in your assigned territory a 0.42 CRM acceptance [variance extracted: 2.1%] I have fully accepted the system in my daily work 0.55 New scale based on Goodhue and I feel that the system 0.57 Thompson (1995) constitutes an integral part of my work Compared to my peers, I 0.66 consider myself a frequent user of my company s CRM system Compared to my peers, I fully use the capabilities of my company s CRM system 0.71 CRM user satisfaction with timeliness of data [variance extracted: 1.9%] I get the information that I need on time 0.65 New scale based on Doll and Torkzadeh The system provides update information 0.62 (1988), Gelderman (1998), Rai et al. Data provided by the system 0.64 (2002) are very often updated Data provided by the system 0.72 are quickly updated CRM user satisfaction with system operations/capabilities [variance extracted: 1.88%] The information that is 0.74 provided by the system is clear I can easily find the 0.53 information I need so as to effectively adapt my sales approach to each customer I can easily exchange 0.53 information with my peers I often encounter problems 0.21 during connection with the central data base a,b Appendix (continued) Construct/Items Factor Loadings Source The system offers a very 0.58 satisfactory help module The system allows me to 0.56 record all information I get from sales calls I can easily track the progress 0.53 of my sales calls for each customer separately The system allows me to make my own presentation format 0.54 Salesperson performance [variance extracted: 1.80%] How well have you performed Adapted from Sohi during the last 12 months, in (1996) terms of achieving your...objectives Sales volume 0.91 Market share 0.90 New account development 0.59 Servicing existing customers 0.68 All items were scored on a 5-point scale ranging from 1 [bstrongly disagreeq] to 5 [bstrongly agreeq], with the exception of the items for performance and perceived usefulness, which were scored on a 5-point scale ranging from 1 [bmuch worseq] to5[bmuch betterq] and from 1 [bnot at allq] to5[bto a large extentq], respectively. a Item discarded after scale purification. b This item was reversed scored. References Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204 215. Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl, & J. Beckmann (Eds.), Action control: From cognition to behavior (pp. 11 39). New York7 Springer Verlag. Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Journal of Applied Psychology, 103(3), 411 423. Armstrong, S. J., & Overton, T. S. (1977). Estimating nonresponse bias in mail surveys. Journal of Marketing Research, 14(3), 396 402. Bagozzi, R. P. (1994). Measurement in marketing research: Basic principles of questionnaire design. In R. Bagozzi (Ed.), Principles of marketing research. Oxford7 Blackwell. Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84, 191 215. Baroudi, J. J., & Orlikowski, W. J. (1988). A short form measure of user information satisfaction: A psychometric evaluation and notes on use. Journal of Management Information Systems, 4(4), 44 59. Behrman, D. N., & Perreault, Jr., W. D. (1982). Measuring the performance of industrial salespersons. Journal of Business Research, 10, 355 370. Behrman, D. N., & Perreault, Jr., W. D. (1984). A role stress model of the performance and satisfaction of industrial salespersons. Journal of Marketing, 48(4), 9 21. Bollen, K., & Lennox, R. (1991). Conventional wisdom on measurement: A structural equation perspective. Psychological Bulletin, 110(2), 305 314. Brown, S. P., & Peterson, R. A. (1993). Antecedents and consequences of salesperson job satisfaction: A meta-analysis and assessment of causal effects. Journal of Marketing Research, 30(1), 63 77.

G.J. Avlonitis, N.G. Panagopoulos / Industrial Marketing Management 34 (2005) 355 368 367 Brynjolfsson, E., & Yang, S. (1996). Information technology and productivity: A review of the literature. Advances in Computers, 43, 179 241. Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189 211. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319 340. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982 1003. DeLone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Information Systems Research, 3(1), 60 95. DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of MIS Research, 19(4), 9 30. Doll, W. J., & Torkzadeh, G. (1988). The measurement of end-user computing satisfaction. MIS Quarterly, 12(2), 259 274. Doll, W. J., & Torkzadeh, G. (1989). A discrepancy model of end-user computing involvement. Management Science, 35(10), 1151 1171. Etezadi-Amoli, J., & Farhoomand, A. F. (1996). A structural model of end user computing satisfaction and user performance. Information and Management, 30, 65 73. Gelderman, M. (1998). The relation between user satisfaction, usage of information systems and performance. Information and Management, 34, 11 18. Gerbing, D. W., & Anderson, J. C. (1988). An updated paradigm for scale development incorporating unidimensionality and its assessment. Journal of Marketing Research, 25(2), 186 192. Goodhue, D. L., & Thompson, R. L. (1995). Task technology fit and individual performance. MIS Quarterly, 19(2), 213 236. Hair, Jr., J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis (5th ed.). Upper Saddle River, NJ7 Prentice-Hall. Hartwick, J., & Barki, H. (1994). Explaining the role of user participation in information system use. Management Science, 40(4), 440 465. Hise, R. T., & Reid, E. L. (1994). Improving the performance of the industrial sales force in the 1990s. Industrial Marketing Management, 23(4), 273 279. Honeycutt, E. D. (2002). Editorial. Industrial Marketing Management, 31(7), 555 559. Hu, P. J. -H., Clark, T. H. K., & Ma, W. W. (2003). Examining technology acceptance by school teachers: A longitudinal study. Information and Management, 41(2), 227 241. Igbaria, M. (1990). End-user computing effectiveness: A structural equation model. Omega, 18(6), 637 652. Igbaria, M., & Guimaraes, T. (1995). Testing the determinants of microcomputer usage via a structural equation model. Journal of MIS Research, 11(4), 87 114. Igbaria, M., Pavri, F. N., & Huff, S. L. (1989). Microcomputer applications: An empirical look at usage. Information and Management, 16(4), 187 196. Igbaria, M., & Tan, M. (1997). The consequences of information technology acceptance on subsequent individual performance. Information and Management, 32(3), 113 121. Ingram, T. N., LaForge, R. W., & Leigh, T. W. (2002). Selling in the new millennium: A joint agenda. Industrial Marketing Management, 31(7), 1 9. Jones, E., Sundaram, S., & Chin, W. (2002). Factors leading to sales force automation use: A longitudinal analysis. Journal of Personal Selling and Sales Management, 22(3), 145 156. Keillor, B. D., Bashaw, E. R., & Pettijohn, C. E. (1997). Salesforce automation issues prior to implementation: The relationship between attitudes toward technology, experience and productivity. Journal of Business and Industrial Marketing, 12(3), 209 219. MacKenzie, S. B., Podsakoff, P. M., & Ahearne, M. (1998). Some possible antecedents and consequences of in-role and extra-role salesperson performance. Journal of Marketing, 62(3), 87 98. Marshall, G. W., Michaels, R. E., Stone, T. H., & Jawahar, I. M. (2001). Research in selling and sales management in the next millennium: An agenda from the AMA faculty consortium. Journal of Personal Selling and Sales Management, 21(1), 15 17. Marshall, G. W., Moncrief, W. C., & Lassk, F. G. (1999). The current state of sales force activities. Industrial Marketing Management, 28(1), 87 89. McKeen, J. D., Guimaraes, T., & Wetherbe, J. C. (1994). The relationship between user participation and user satisfaction: An investigation of four contingency factors. MIS Quarterly, 18(4), 427 452. Moncrief, W. C. (1986). Selling activity and sales position taxonomies for industrial salesforces. Journal of Marketing Research, 23(3), 261 270. Morgan, A. J., & Inks, S. A. (2001). Technology and the sales force. Industrial Marketing Management, 30(5), 463 472. Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). New York7 Mac Graw Hill. Porter, L. W., & Lawler, E. E. (1968). Managerial attitudes and performance. Homewood, IL7 Irwin-Dorsey. Pullig, C., Maxham, III, J. G., & Hair, J. F., Jr. (2002). Salesforce automation systems: An exploratory examination of organizational factors associated with effective implementation and salesforce productivity. Journal of Business Research, 55(5), 401 415. Rai, A., Lang, S. S., & Welker, R. B. (2002). Assessing the validity of IS success models: An empirical test and theoretical analysis. Information Systems Research, 31(1), 50 69. Rivers, M. L., & Dart, J. (1999). The acquisition and use of sales force automation by mid-sized manufacturers. Journal of Personal Selling and Sales Management, 19(2), 59 73. Rizzo, J. R., House, R. J., & Lirtzman, S. I. (1970). Role conflict and ambiguity in complex organizations. Administrative Science Quarterly, 15(2), 150 163. Rogers, E. M. (1995). Diffusion of innovations. New York7 Free Press. Schillewaert, N., Ahearne, M. J., Frambach, R. T., & Moenaert, R. K. (2000). The acceptance of information technology in the sales force (No. ISBM Report 15-2000): Institute for the Study of Business Markets, The Pennsylvania State University. Sharma, S., Mukherjee, S., Kumar, A., & Dillon, W. R. (2004). A simulation study to investigate the use of cutoff values for assessing model fit in covariance structure models. Journal of Business Research, (in press). Singh, J., & Rhoads, G. K. (1991). Boundary role ambiguity in marketingoriented positions: A multidimensional, multifaceted operationalization. Journal of Marketing Research, 28(3), 328 338. Sohi, R. S. (1996). The effects of environmental dynamism and heterogeneity on salespeople s role perceptions, performance and job satisfaction. European Journal of Marketing, 30(7), 49 67. Speier, C., & Venkatesh, V. (2002). The hidden minefields in the adoption of sales force automation technologies. Journal of Marketing, 66(3), 98 111. Sujan, H., Sujan, M., & Bettman, J. R. (1988). Knowledge structure differences between more effective and less effective salespeople. Journal of Marketing Research, 25(1), 81 86. Szymanski, D. M. (1988). Determinants of selling effectiveness: The importance of declarative knowledge to the personal selling concept. Journal of Marketing, 52(1), 64 77. Torkzadeh, G., & Doll, W. J. (1999). The development of a tool for measuring the perceived impact of information technology on work. Omega, 27(3), 327 339. Triandis, H. C. (1980). Values, attitudes, and interpersonal behavior. In: H. E. Howe (Ed.), Nebraska symposium on motivation, 1979: Beliefs, attitudes, and values, vol. 27 (pp. 195 259). Lincoln, Nebraska7 University of Nebraska Press.

368 G.J. Avlonitis, N.G. Panagopoulos / Industrial Marketing Management 34 (2005) 355 368 Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal studies. Management Science, 46(2), 186 204. George J. Avlonitis is a Professor of Marketing at the Department of Marketing and Communication in the Athens University of Economics and Business and Director of both the Athens Laboratory of Research Marketing A.L.A.R.M. and the Executive Program bmarketing and Communication with New TechnologiesQ of the same Department. His primary research, teaching and consulting activities are in the areas of Product Policy, Sales Management, Industrial Marketing, Technological Innovation and Strategic Marketing. He has presented various papers in U.S.A., Canada and Europe and has published more than 90 articles in the proceedings of International Conferences and the most prestigious international scientific journals of Marketing, including Journal of Marketing, Journal of the Academy of Marketing Science, Industrial Marketing Management, International Journal of Research in Marketing, European Journal of Marketing, Journal of Product Innovation Management, Journal of Services Marketing, Journal of Marketing Management, etc. Nikolaos G. Panagopoulos received his Ph.D. in Marketing from the Athens University of Economics and Business, Department of Marketing and Communication, Greece, where he is currently a research associate. His primary research activities are in the areas of sales force management, research methodology and the applications of new technologies in marketing. His works have appeared in the Conference Proceedings and the Doctoral Colloquium Proceedings of the European Marketing Academy.