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\title{Adoption of Electronic CRM in Service Sector: Using an Integrated Model}
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\begin{document}

             \author[1]{Sameh Tebourbi  Abbes}

             \author[2]{Romdhane  Khemakhem}

             \affil[1]{  Faculty of Economics and Management of Sfax. Sfax University}

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\date{\small \em Received: 10 June 2021 Accepted: 30 June 2021 Published: 15 July 2021}

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\begin{abstract}
        


The main objective of this research is to develop an integrated model of significant success factors of the adoption e-CRM system by determining the intention behavior the user in the service sector. The proposed research model was constructed based on the innovation diffusion theory (Rogers 2003), the Unified Theory of Acceptance and Use of Technology (UTAUT), and the Task Technology Fit model (TTF). A quantitative study carried out with 340 respondents belonging to Tunisian companies with customer databases ?A priori judgment? sample method was chosen. We used structural equation modeling that is an adequate solution to the design of theoretical models and to test the research hypothesis Keywords: integrated model, adoption of e-CRM, the innovation diffusion theory (Rogers 2003), the Unified theory of acceptance and use of technology (UTAUT), and the task technology fit model (TTF).

\end{abstract}


\keywords{integrated model, adoption of e-CRM, the innovation diffusion theory (Rogers 2003)}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
urrently, the solid metamorphosis of the exchange universe and the spectacular evolution of the market, lead the company to initiate systemic transformations under strong time constraints to prevent customer needs and thus develop personalized offers and superior quality \hyperref[b3]{(Cadiat and de Moerloose, 2002)} through the electronic customer's relationship management "e-CRM system".\par
Many of the benefits of this system are that businesses can provide their customers with personalized service, acquire new customers, retain existing customers, and maximize their lifetime value. As a result, acquiring new customers remains a hard task due to the intense competitive environment and complex consumer behavior  {\ref (Tocquer and Langlois 1992)}.\par
Despite the advantages of information technology (IT) and its ability to perform relationship marketing tasks, there is still a lack of empirical data in the field of relationship marketing (RM), customer relationship management. (CRM) and its e-CRM application.\par
In the literature, studies indicate several failures in the use and adoption of the e-CRM system / The failures are mainly due to employee resistance to change, insufficient training on the e-CRM system, lack of management support, and lack of appropriate IT infrastructure.\par
Indeed, most of the previous research had not focused on studying user acceptance behaviors or their attitude towards e-CRM adoption. The e-CRM projects have focused more on the application of technology and not on human issues. However, and according to \hyperref[b33]{Volle (2003)}, the gains to be made from the information system no longer come from massive investments in IT infrastructure but from the strengthening of links between people, technology, and the organization.\par
Big data pose new challenges to traditional data processing methods, because they are not designed for the scale of the web \hyperref[b5]{(Chéour, 2019)}. Given the astronomical amount of potentially exploitable data, it is first necessary to put in place approaches to manage this wealth of information, to store and prioritize this data in an adhesive manner, with a view to their subsequent exploitation \hyperref[b13]{(Ghazaleh, 2020)}.\par
Many theories try to take advantage of this question by studying the conditions under which the interactions between people, technologies and, customers increase the profitability of the company's merchant portfolio. Specially, the diffusion theory of innovation.\par
The diffusion theory of innovations has shown relatively dominance over other models that have tried to investigate the field of perceptual characteristics of innovation  {\ref (Meuter, 1999)}. It was criticized for its lack of specificity \hyperref[b4]{(Chau and Tam, 1997)}.\par
Another criticism of the diffusion of innovations theory is its omission of the vital role intention plays in the process of use  {\ref (Ozdemir, Trott \& Hoecht, 2008)}.\par
However, adopting an innovation involves first its evaluation by the consumer, then forming the intention demonstrating the close link between the use of technology and human performance. Yet, it does not specify how this intention is formed or how it results was used or rejected. Several studies have emphasized the role of intentions in user behavior \hyperref[b2]{(Anderson \& Schwager, 2004;}\hyperref[b19]{Lin, Chan \& Jin, 2004)}.\par
Facing his critics, two main theories have recently emerged proposing elements of ramifications. These are the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Task Technology Fit model (TTF), which serves to better explain the adoption of IT.\par
UTAUT is certainly the most successful explanatory model of the individual adoption of technologies. Explainnearly 70\% of the variance in intent and 50\% of the variance in usage \hyperref[b32]{(Venkatesh et al. 2003}).\par
In addition, there are other significant factors like technological characteristics and task characteristics that are unnoticed in the model \hyperref[b18]{(Khoshtinat, Bojei, Ahmadin, 2014)}.\par
To assess the successful match between the task and the information technology and character of users, many authors like (Goodhue and  {\ref Thompson, 1995)} suggest a relevant research model that covers most of the factors that explain the adoption decision of electronic CRM. The Task Technology Fit model (TTF), which is an organizational assessment tool for Information Systems (IS) and services  {\ref (Goodhue,1995)}.\par
Despite the importance of the task-technology adjustment model  {\ref (Goodhue, 1995)} in explaining the adoption of IT technological innovation, it does not consider an aspect of great importance; the interaction between the user and the task \hyperref[b18]{(Khoshtinat, Bojei, Ahmadin, 2014)}. Given the contributions but above all to overcome the limits of the theory of diffusion of innovations \hyperref[b27]{(Rogers, 2003)}, the unified theory of acceptance and use of technology  and the task-technology adjustment model (Goodhue, 1995), we propose an integration of these three theoretical frameworks to judiciously explain the adoption of e-CRM in service companies. 
\section[{In light of these findings, we can ask the following question:}]{In light of these findings, we can ask the following question:}\par
What are the determinants of the use and adoption of the e-CRM system in service companies?\par
Based on the diffusion of innovation theories \hyperref[b27]{(Rogers, 2003)}, the Unified Theory of Acceptance and Use of Technology (UTAUT), and the Task Technology Fit model (TTF), our main objective is to develop an integrated model of the main factors of CRM adoption by determining the behavioral intention of marketing managers in the service sector. 
\section[{II. Literature Review and Hypothesis}]{II. Literature Review and Hypothesis}\par
The unified theory of acceptance and use of "UTAUT" technology justifies using ICT essentially from four fundamental determinants of behavioral intention: expected performance, expected effort, social influence and, the facilitating conditions. These four elements make it possible to prove using ICT \hyperref[b32]{(Venkatesh et al., 2003)}. The UTAUT model \hyperref[b32]{(Venkatesh, 2003)} will be enriched by another variable, innovativeness. This extension of the UTAUT model is recommended by . 
\section[{a) Effort expectancy and behavioral intention}]{a) Effort expectancy and behavioral intention}\par
Effort Expectancy is defined as "the degree of ease associated with the use of the system" \hyperref[b32]{(Venkatesh, 2003)}. When companies adopt the e-CRM system, they traditionally compare it with existing systems. If they feel that the system is easily understood and used, their willingness to adopt it will be strengthened  {\ref (Pai, Tu, 2011)}.\par
This finding is confirmed by \hyperref[b23]{Park et al. (2007)}, \hyperref[b29]{Tan et al. (2010)} and \hyperref[b35]{Yu (2012)} by applying the UTAUT model. Accordingly, we assume a relationship between effort expectancy and intention to use the e-CRM system in service companies.\par
H1: Effort expectancy affects significantly and positively behavioral intention. 
\section[{b) Social influence and behavioral intention}]{b) Social influence and behavioral intention}\par
This construct is defined as "the degree to which an individual perceives that important others believe that he or she should use the new system"  {\ref (Venkatesh,2003)} Pai, Tu, 2011 take into consideration that behavioral intention will be enhanced when the company adopted the e-CRM system, will create a higher social limpact. H2: Social influence affects significantly and positively behavioral intention. 
\section[{c) The facilitating condition and intention of adopting the e-CRM system in service companies}]{c) The facilitating condition and intention of adopting the e-CRM system in service companies}\par
Facilitating conditions are defined as "the degree to which an individual believes that an organizational and technical infrastructure exists to support the use of the system" Venkatesh et al.  {\ref (2003)}. They are often theorized to have a direct effect on the intention and use of the information system \hyperref[b32]{(Venkatesh et al. 2003}). The study by \hyperref[b32]{Venkatesh et al. (2003)}, and \hyperref[b6]{Chiu and Wang (2008)} found that when users find that they have a fairly high capacity to use and support resources, this results in a more positive acceptance of information technology.\par
Facilitation conditions in both TAM and UTAUT are a direct determinant of adoption intention. Studies have found that the enabling conditions directly affect behavioral intention \hyperref[b31]{(Thompson et al. 1991}\hyperref[b30]{, Taylor \& Todd, 1995)}. 
\section[{H3:}]{H3:}\par
The facilitating condition positively and significantly affects the intention to adopt the e CRM system. 
\section[{d) Performance expectancy and behavioral intention}]{d) Performance expectancy and behavioral intention}\par
Performance expectancy is defined as "the degree to which the user expects that using the system will help him or her attain gains in job performance" \hyperref[b32]{(Venkatesh et al. 2003)}. It has been shown by several studies to be a very significant determinant in the explanation of the intention to adopt a technology  {\ref (Ouedraogo, 2011;}\hyperref[b32]{Venkatesh et al. 2003)}. In general, when companies need to invest in technological innovation, the main consideration in system implementation is whether the technology or system can improve overall performance or can effectively perform the defined task (J.-C. Pai, F.-M. Tu, 2011). Indeed, the technological innovation of information systems can only be adopted within service companies if the staff perceive the gains in terms of efficiency, speed and, performance in the execution of tasks, also, if he sees these new tools as real opportunities to ensure not only professional care, but also to promote business activities. H4: Performance expectancy positively and significantly affects behavioral intention. 
\section[{e) Innovativeness and behavioral intention}]{e) Innovativeness and behavioral intention}\par
Innovativeness is defined as "the degree of adoption of innovations and highlights the extent to which the organization is receptive" \hyperref[b32]{(Venkatesh 2003}). Authors such as \hyperref[b26]{Roehrich (1994)} and \hyperref[b12]{Frini and Limayem (2001)} have found a prominent positive correlation between innovativeness and the propensity of behavioral intention towards the use of innovations. H5: Innovativeness has a positive effect on behavioral intention. 
\section[{f) Behavioral intention and intention to adopt the e-CRM system in service companies}]{f) Behavioral intention and intention to adopt the e-CRM system in service companies}\par
Behavioral intention is defined as "the subjective probability that a person will engage in the behavior in question" \hyperref[b11]{(Fishbein \&Ajzen, 1975)}. Indeed, previous research indicates that the user shows a greater willingness to adopt the new system when supported by the organizational or technological structure \hyperref[b6]{(Chiu \& Wang, 2008;}\hyperref[b32]{Venkatesh et al. 2003)}. 
\section[{H6:}]{H6:}\par
The behavioral intention of the adoption the e-CRM system has a positive effect on the intention to adopt it.\par
The testability of the e-CRM system in service companies and the intention to adopt it Testability is defined as "the degree to which an innovation can be tested over a limited field before its use" \hyperref[b27]{(Rogers, 2003)}. A new idea is generally faster to adopt than other ideas \hyperref[b27]{(Rogers, 2003)}. This possibility would allow the individual or any other adoption unit to better understand how an innovation works and its importance. Thus, an innovation that we can pre-test present less risk to the individual or organization that intends to adopt it \hyperref[b27]{(Rogers, 2003)}. Potential adopters who are allowed to experiment with the innovation will feel more comfortable and be more likely to adopt it \hyperref[b0]{(Agarwal \& Prasad 1998)} and \hyperref[b27]{Rogers (2003)}. 
\section[{H7:}]{H7:}\par
The testability of the e-CRM system has a positive effect on the intention to adopt it.\par
g) The observability of the e-CRM system in service companies and the intention to adopt it Observability is defined as "the degree to which the results of new information technologies are visible and accessible". The clearer the results of adopting these systems, the more easily individuals will adopt them \hyperref[b27]{(Rogers, 2003)}. \hyperref[b25]{Robertson (1971)} points out that observability correlates positively with the adoption of a new system. 
\section[{H8:}]{H8:}\par
The observability of an e-CRM system has a positive effect on the intention to adopt it. 
\section[{h) The perceived risk and intention of adopting the e-CRM system in service companies}]{h) The perceived risk and intention of adopting the e-CRM system in service companies}\par
Perceived risk is defined in the literature as "the perceived uncertainty in a purchasing situation that affects people's confidence in their decisions" \hyperref[b16]{(Im et al. 2008)}.\par
Toufaily, Daghfous and, Toffoli, 2009 confirmed that perceived risk is a determining factor adopting of a new system. The more the manager perceives a high risk in the face of innovation, the lower the adoption rate will be and vice versa.\par
In this same framework, Robertson (1971) approves the negative correlation between the adoption of innovations and perceived risk. \hyperref[b21]{Ostlund (1969)} explained, for his part, that the lower the level of perceived risk, the more we see a high adoption rate. The perceived risk is thus a determining factor in the adoption of a new product. 
\section[{H9:}]{H9:}\par
The perceived risk of the e-CRM system has a negative effect on the intention to adopt it. 
\section[{i) The characteristics of the tasks and the tasktechnology adjustment}]{i) The characteristics of the tasks and the tasktechnology adjustment}\par
The characteristics of the tasks are those that a user must mobilize. They are measured by the following criteria: the complexity of the task (the routine versus non-routine aspect) and the interdependence between the tasks (transversality) (Goodhue and  {\ref Thompson, 1995)}.\par
The task-technology fit "is the degree of correspondence between the functional need for the task, the individual capacities and the technical functionalities offered by the system" (Goodhue and  {\ref Thompson, 1995)}. In this regard Goodhue and  {\ref Thompson (1995)}, assert that the characteristics of the task are directly affected by the characteristics of a specific system, or the efficiency of the use of information technologies and the technical functionalities of the system. The study by \hyperref[b10]{Dishaw and Strong (1999)} shows that the characteristics of the tasks will directly influence the task-technology fit. H10: The characteristics of the tasks positively affect the task-technology fit.\par
j) The characteristics of the technology and the tasktechnology fit \hyperref[b10]{Dishaw and Strong (1999)}, have shown that workgroups and characteristics of technology directly influence task-technology adjustment. Likewise, the task-technology fit will be improved when technological performance increase (Goodhue and  {\ref Thompson, 1995)}. The fit between the tasks to be performed and the technology used in organizations has a direct influence on the use \hyperref[b10]{(Dishaw and Strong, 1999)}, \hyperref[b32]{Venkatesh (2003)}.\par
H11: The characteristics of the technology positively affect the task-technology fit. 
\section[{k) The characteristics of the technologies and the intention of adopting an e-CRM system in service companies}]{k) The characteristics of the technologies and the intention of adopting an e-CRM system in service companies}\par
Research by Goodhue and  {\ref Thompson (1995)} found that the user's cognition and belief determine the strength of behavioral intention, subsequently affecting the final performance of the current behavior. 
\section[{H12:}]{H12:}\par
The task-technology fit significantly and positively affects behavioral intention. 
\section[{l) The influence of moderating variables}]{l) The influence of moderating variables}\par
We retained the two demographic variables: sex and age, as two moderating variables in our study.\par
m) The influence of age  {\ref Rogers (1995)} has shown that "adopters are usually younger". Igbaria and Parasuraman (1989) note that "older people tend to be less exposed to ICTs, therefore less flexible and more resistant to change. As a result, their anxiety about computers increases and thus reduces their likelihood of using these systems. " \hyperref[b36]{Zoltan and Chapanis (1981)} show that "the attitudes of older people towards computers are more negative than those of younger people". Moreover, "the oldest have a weaker perception of the usefulness of personal computers"  {\ref (Igbaria, 1993)}. 
\section[{H13:}]{H13:}\par
The age of the user of an e-CRM system has a moderating effect on the relationship between the determinants of e-CRM system adoption and the intention to use this system. 
\section[{n) The influence of sex}]{n) The influence of sex}\par
The influence of sex on the use of technologies is tested by several studies in the field of the acceptance of various technologies: the microcomputer (Igbaria and Parasuraman, 1989), e-mail, m-commerce , online banking services \hyperref[b20]{(Martins et al. 2013}) and mobile banking services \hyperref[b35]{(Yu, 2012)}. 
\section[{H 14:}]{H 14:}\par
The sex of the user of the e-CRM system has a moderating effect on the relationship between the determinants of adoption of the e-CRM system and the intention to use these systems.\par
The conceptual model which summarizes all the assumptions made is as follows: Figure1: Conceptual model of research 
\section[{Research Methodology}]{Research Methodology}\par
This research was conducted in the context of the Tunisian market. A quantitative questionnaire study was used in this research. 
\section[{a) Data collection}]{a) Data collection}\par
In CRM research in general, the sampling populations under study are companies with large customer databases operating in sectors with great competitive pressure that pushes them to differentiate themselves, in particular companies operating in the B to C sector  {\ref (Coltman et al., 2011)}.\par
Our choice focused on Tunisian companies with a customer database (Coovi 2010), particularly the service sector. "A priori judgment" sample method was chosen since the companies selected to be part of the sample are those which think, before questioning them, that they may have crucial information for the study  {\ref (Giannelloni et al. Vernette, 2012, p.284)}. We use the "prior judgment" sampling method when we take a sample based on certain judgments about the whole population. The assumption underlying its use is that the enumerator will select units that will be characteristic of the population. The measurement scales used have been adapted from previous research where they have shown good validity. To ensure content validity, the vocabulary of these scales has been adjusted to the context using the CRM system.  
\section[{c) Method of data analysis}]{c) Method of data analysis}\par
We used the method of structural equations, which are confirmatory techniques that aim to validate hypotheses using SPSS 20 and AMOS20 software.\par
IV. 
\section[{Search Results}]{Search Results} 
\section[{a) Purification and reliability of measurement scales}]{a) Purification and reliability of measurement scales}\par
A confirmatory factor analysis was performed to assess the psychometric qualities of the scales for measuring latent variables and to test research hypotheses. We made the modifications by adding covariance links between the errors in order to improve the goodness of fit of our measurement model. The reliability of the scales is satisfied (? of Jöreskog and ? of Cronbach> 0.7).\par
The convergent validity of the scales is satisfactory (?VC> 0.5) according to the criterion of Fornell and Larcker. The results also show that the discriminant validity is satisfied. The discriminant validity of all the constructs is verified. 
\section[{b) Testing hypotheses}]{b) Testing hypotheses}\par
To judge whether the hypothesis is supported or not, we need to move on to studying regression links.\par
To judge a relationship like significant, it should be checked that the CR is greater than 1.96 with a significant p (p must be less than 5\%). The results show that expected effort and social influence have a positive effect on behavioral intention are rejected. These results are surprising contradict with the results of previous research, whether in the context of UTAUT \hyperref[b32]{(Venkatesh et al., 2003)}.\par
Also, the results of the current study show that there is a significant positive relationship between the facilitate conditions and the intention e-CRM system.\par
This result is consistent with the work of \hyperref[b32]{Venkatesh et al. 2003}.\par
Facilitating conditions are often theorized to have a direct effect on the intention and use of SI \hyperref[b32]{(Venkatesh et al. 2003)}. \hyperref[b32]{Venkatesh et al. (2003)} and \hyperref[b6]{Chiu and Wang (2008)} found that when users have high utilization capacity and support resources, they show more positive acceptance of information technology.\par
It is recommended that marketing managers integrate the e-CRM system. Since it is easy to use, it does not require strong computer skills.\par
Then, there is a significant and positive relationship between expected performance and behavioral intention. This result is consistent with the work of Wang2008. Indeed, performance expectancy is one of the most powerful predictors of intention built into the UTAUT model as this system is seen as useful for marketing functions.\par
In addition, it is supposed to provide assistance to different managers (regardless of their level) for the analysis of daily activity and decision-making. The software is supplied monthly for weekly management of the activity (schedules and promotion).\par
The effect of innovativeness on behavioral intention is confirmed.\par
This result is consistent with the results of previous research, \hyperref[b32]{Venkatesh 2003}\hyperref[b26]{, Roehrich 1994}\hyperref[b12]{and, Frini and, Limayem 2001.} It is recommended to use the e-CRM system since it is seen as useful for marketing functions; It can manage business problems and help improve performance, it is assumed assist to various managers (regardless of their level) for the analysis of daily activity and decision-making.\par
The behavioral intention of implementing an e-CRM system to have a positive effect on the intention to adopt it is validated.\par
This result is consistent with the work \hyperref[b6]{(Chiu \& Wang, 2008;} {\ref Venkatesh and Davis, 2000;}\hyperref[b32]{Venkatesh et al. 2003)}, which shows that from a certain level of experience, the intention was a better explanatory variable of the intention of use.\par
About this outcome, service companies must support leaders with a technology or a solid organizational structure that meets their expectations.\par
If the leader is well trained the e-CRM system, it will have a positive effect to adopt this system.\par
If the leader is well trained while the company implements the CRM system, it will produce a positive effect on the use of CRM.\par
The testability of the e-CRM system effects the intention to adopt the e-CRM system; it is best for marketers to test the system before adopting it.\par
Exploratory and confirmatory factor analysis decomposed this concept into two dimensions: The rejection of the first dimension can be explained by the fact that observability of the system by other users and observability outside the company do not affect the intention to adopt the system.\par
The acceptance of the second dimension is explained by the fact responds to the real needs of customers and allows the rapid flow of data.\par
The perceived risk has a negative effect on behavioral intention is validated. This result confirmed by previous studies \hyperref[b16]{(Im et al., 2008;} {\ref Pavlou, 2003;} {\ref Toufaily, Daghfous and Toffoli, 2009;}\hyperref[b25]{Robertson, 1971;}\hyperref[b21]{Ostlund, 1969)}.\par
As a result, service companies must ensure that customer personal information is not disclosed while using this system.\par
According to Goodhue and  {\ref Thompson (1995)}, the characteristics of the technology are measured by the functionality of e-CRM system:\par
The exploratory and confirmatory factor analysis allowed us to identify two dimensions, analytical capacity and customer relationship.\par
The results showed that only the customer relationship determines the task-technology fit. This result is consistent with the work of \hyperref[b24]{(Payne 2006)}.\par
The e-CRM system integration helps marketers maintain relationships with customers and provide a single view of the customer through the match between task and technology.\par
The results of the current study show that there is a significant and positive relationship between tasktechnology intention. This result is consistent with the work of Goodhue and,  {\ref Thompson (1995)}.\par
The age of the marketers has a moderating effect on the relationship between the expected effort and the intention to use an e-CRM system. Our results show that the youngest respondents think that using the e-CRM systems will be easier. We can therefore conclude that older leaders are ready to use these systems, but they are more sensitive to the difficulty of learning to use these systems. This confirms the results of \hyperref[b32]{Venkatesh and al. (2003)} Age also has a moderating effect on the relationship between social influence and intention to use an e-CRM system. Indeed, in our study, social influence was crucial for older respondents. This result is consistent with the results of \hyperref[b32]{Venkatesh et al. (2003)}, this could be ainteresting, especially in the initial phase of the experience when individuals is more likely to rely on the opinions of others  {\ref (Agarwal \& Prasad, 1999)}. Moreover, they tend to be less flexible and more resistant to change \hyperref[b15]{(Igbaria and Parasuraman, 1989)}.\par
Age also has a moderating effect on the relationship between the use of e-CRM system and the conditions of facilitation. Our results confirm the results of \hyperref[b32]{Venkatesh et al. (2003)} and, Venkatesh and Zhang (2010), the literature shows that the youngest are always the most interested in technologies  {\ref (Rogers, 1995)}; on the other hand, older people tend to be less exposed to ICT \hyperref[b36]{(Zoltan et Chapanis, 1981;}\hyperref[b15]{Igbaria and Parasuraman, 1989}) therefore, they believe that the use of the e-CRM system requires technological resources and knowledge.\par
In contrast, the gender of the respondents does not affect] the factors influencing intention to use. These are contradictory with the literature \hyperref[b32]{(Venkatesh et al., 2003;} {\ref Garbarino and Strahilevitz, 2004;} {\ref Nysveen et al., 2005;}\hyperref[b23]{Park, 2007;} {\ref Cruz et al. 2010;} {\ref Riffai et al., 2012)}. \hyperref[b32]{(Venkatesh, 2003)} which assumes that men are more likely to rely on the performance expectancy of technology. 
\section[{d) Conclusion}]{d) Conclusion}\par
Based on the diffusion theory of innovation \hyperref[b27]{(Rogers, 2003)}, the innovation diffusion theory \hyperref[b27]{(Rogers 2003)}, the Unified Theory of Acceptance and Use of Technology (UTAUT), and the Task Technology Fit model (TTF), our main objective is to develop an integrated model of key success factors of adoption e-CRM system by determining the intention behavioral in the service sector in Tunisia.\par
The results of our research agree with those of previous research  {\ref (Venkatsh, 2003;}\hyperref[b27]{Rogers, 2003;} {\ref Goodhue and Thompson,1995)}, which allows us to underline the importance of the variables of the three theories in the formation of intention towards the adoption of CRM system in service sector.\par
This work is based only on reviewing the literature relating to the main models developed in psychology and sociology (TDI, UTAUT, TTF).\par
As a future way of research could attempt to invite subsequent studies to integrate other model to explain better the determinants of the adoption of the e-CRM system in service companies.\par
Such as the Information Systems Success Model (ISSM)  {\ref (Delone, 2003;} {\ref DeLone and McLean, 1992)}.\par
A second limitation, is the non-integration of certain relevant variables in our adoption model. As a future line of research, we propose to integrate the satisfaction as a variable influencing the intention of adopting the e-CRM system. The results of the work of (Bhattacherjee, 2001) show that satisfaction with the use of an IS is a powerful determinant of the intention to use the system.\par
We also propose to integrating the culture variable as a moderating variable that can enrich our model since it plays a primordial role in the adoption of technology that makes it possible to change individual belief systems (Venkataesh and Zhang (2010).\par
A final limitation, given the complexity of collecting data from service companies, we used a single survey to conduct the AFE and the AFC.\par
As a future avenue of research, it is preferable to use two different samples to increase the generalization of the results  \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.16085069444444444\textwidth}P{0.4751736111111111\textwidth}P{0.09149305555555556\textwidth}P{0.12248263888888887\textwidth}}
\tabcellsep Sectors\tabcellsep Respondents\tabcellsep \%\\
\tabcellsep Financial services sector\tabcellsep 160\tabcellsep 47,1\\
\tabcellsep Telecommunication sector\tabcellsep 117\tabcellsep 34,4\\
\tabcellsep Sector NTIC\tabcellsep 63\tabcellsep 18,5\\
\tabcellsep Total\tabcellsep 340\tabcellsep 100,0\\
\tabcellsep Member of leader\tabcellsep 16\tabcellsep 4,7\\
Post\tabcellsep Marketing Manager Sales manager\tabcellsep 160 134\tabcellsep 47,1 39,4\\
\tabcellsep Other\tabcellsep 30\tabcellsep 8,8\\
\tabcellsep Total\tabcellsep 340\tabcellsep 100,0\\
\multicolumn{2}{l}{Number of permanent employees}\tabcellsep \tabcellsep \\
\tabcellsep Less of 20\tabcellsep 150\tabcellsep 44,1\\
\tabcellsep Between 20 et 49\tabcellsep 8\tabcellsep 2,4\\
\tabcellsep Between50 et 99\tabcellsep 44\tabcellsep 12,9\\
\tabcellsep 100 and more\tabcellsep 138\tabcellsep 40,6\\
\tabcellsep Total\tabcellsep 340\tabcellsep 100,0\\
\tabcellsep Experience\tabcellsep \tabcellsep \\
\tabcellsep Aucune\tabcellsep 18\tabcellsep 34,7\\
\tabcellsep For less than 1 year\tabcellsep 66\tabcellsep 19,4\\
\tabcellsep For a period of between 1 and 5 years\tabcellsep 145\tabcellsep 13,2\\
\tabcellsep For a period between 5 and 10 years\tabcellsep 107\tabcellsep 31,5\\
\tabcellsep For more than 10 years\tabcellsep 4\tabcellsep 1,2\\
\tabcellsep Total\tabcellsep 340\tabcellsep 100,0\\
b) Measurement of variables\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{2}{l}{All items are measured on a 5-point Likert scale.}\tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_0}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.3\textwidth}P{0.16470588235294117\textwidth}P{0.38529411764705884\textwidth}}
Concepts\tabcellsep Number of items\tabcellsep Authors\\
Expected effort\tabcellsep 4items\tabcellsep Venkatesh et al. (2003)\\
Social influence\tabcellsep 4 items\tabcellsep Venkatesh et al. (2003)\\
Facilitate condition\tabcellsep 4 items\tabcellsep Venkatesh et al. (2003)\\
Expected performance\tabcellsep 4 items\tabcellsep Venkatesh et al. (2003)\\
Innovatively\tabcellsep 3 items\tabcellsep Jones et al. (2002)\\
Testability\tabcellsep 6 items\tabcellsep Rogers (2003)\end{longtable} \par
 
\caption{\label{tab_1}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.41\textwidth}P{0.046\textwidth}P{0.078\textwidth}P{0.06999999999999999\textwidth}P{0.174\textwidth}P{0.072\textwidth}}
\multicolumn{2}{l}{Concepts/ Numbers of}\tabcellsep Items eliminated\tabcellsep Factors\tabcellsep Items after ACP\tabcellsep Reliability\\
\multicolumn{2}{l}{Expected effort (3items)}\tabcellsep EE1\tabcellsep F1\tabcellsep EE2 EE3\tabcellsep 0.645\\
\tabcellsep \tabcellsep EE4\tabcellsep \tabcellsep \\
\multicolumn{2}{l}{Social influence(4items)}\tabcellsep SI1 SI4\tabcellsep F1\tabcellsep SI2 SI3\\
\multicolumn{2}{l}{Facilitate condition(4items)}\tabcellsep FC3\tabcellsep FC1 FC2 FC4\tabcellsep F1\tabcellsep 0.633\\
\multicolumn{2}{l}{Expected performance (4 items)}\tabcellsep \tabcellsep F1 F2\tabcellsep EP1 EP4 EP2 EP3\tabcellsep 0.835\\
\multicolumn{2}{l}{Innovativity (3items)}\tabcellsep I3\tabcellsep F1\tabcellsep I1 I2\tabcellsep 0.658\\
\multicolumn{2}{l}{Percived risk (3items)}\tabcellsep \tabcellsep F1\tabcellsep PR PR2 PR3\tabcellsep 0.645\\
Technological\tabcellsep \tabcellsep \tabcellsep F1\tabcellsep INT1 INT2\\
characteristics\tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
(2items)\tabcellsep practical CRM (4 items)\tabcellsep PCRM4\tabcellsep F1\tabcellsep PCRM1 PCRM2 PCRM3\end{longtable} \par
 
\caption{\label{tab_2}Table 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.09697986577181208\textwidth}P{0.12550335570469798\textwidth}P{0.05704697986577181\textwidth}P{0.10268456375838926\textwidth}P{0.06275167785234899\textwidth}P{0.11409395973154363\textwidth}P{0.03993288590604027\textwidth}P{0.05704697986577181\textwidth}P{0.06275167785234899\textwidth}P{0.04563758389261745\textwidth}P{0.0855704697986577\textwidth}}
Indice\tabcellsep Chi-deux\tabcellsep DL\tabcellsep Chi-deux/ DL\tabcellsep P\tabcellsep \multicolumn{3}{l}{GFI AGFI NFI}\tabcellsep TLI\tabcellsep RMR\tabcellsep RMSEA\\
Valeur\tabcellsep 681.1 4 9\tabcellsep 47 9\tabcellsep 1,42\tabcellsep ,075\tabcellsep 0,9\tabcellsep 0,9\tabcellsep ,78 3\tabcellsep ,90\tabcellsep ,064\tabcellsep , 044\\
Seuil\tabcellsep -----\tabcellsep ----\tabcellsep <2\tabcellsep >0.0 5\tabcellsep ?0. 9\tabcellsep >0.9\tabcellsep >0. 9\tabcellsep >0. 9\tabcellsep 0\tabcellsep <0.08\end{longtable} \par
 
\caption{\label{tab_3}Table 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5} \par 
\begin{longtable}{P{0.13502604166666665\textwidth}P{0.08854166666666667\textwidth}P{0.21471354166666667\textwidth}P{0.14166666666666666\textwidth}P{0.06640625\textwidth}P{0.20364583333333333\textwidth}}
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Year 2021\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep 7\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Volume XXI Issue I Version I\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep ( ) C\\
Items EA2 EA3 Somme IS2 IS3 Somme CF1 CF2 CF4\tabcellsep CF EST 1 1 2 1 1 2 1 0.475 0.55\tabcellsep Variance des erreurs 0.325 0.264 0.589 0.738 1.011 1.749 0.026 0.14 0.21\tabcellsep \multicolumn{2}{l}{Rho de Jöreskog VME 0.871 0.772 0.695 0.533 0.915 0.801}\tabcellsep Global Journal of Computer Science and Technology\\
Somme\tabcellsep 2.025\tabcellsep 0.379\tabcellsep \tabcellsep \\
I1\tabcellsep 1\tabcellsep 0.236\tabcellsep 0.881\tabcellsep 0.787\\
I2\tabcellsep 1\tabcellsep 0.303\tabcellsep \tabcellsep \\
Somme\tabcellsep 2\tabcellsep 0.539\tabcellsep \tabcellsep \\
O9\tabcellsep 1\tabcellsep 0.986\tabcellsep 0.66\tabcellsep 0.5\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep © 2021 Global Journals\end{longtable} \par
 
\caption{\label{tab_4}Table 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{6} \par 
\begin{longtable}{P{0.5919642857142857\textwidth}P{0.25803571428571426\textwidth}}
Concepts\tabcellsep Rho de Joreskog\\
Compatibility\tabcellsep 0.73\\
Perceived utility\tabcellsep 0.9\\
Perceived ease of use\tabcellsep 0.63\\
Trust\tabcellsep 0.79\\
Perceived risk\tabcellsep 0.98\end{longtable} \par
 
\caption{\label{tab_5}Table 6 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{7} \par 
\begin{longtable}{P{0.6696969696969697\textwidth}P{0.1803030303030303\textwidth}}
Concepts\tabcellsep VME\\
Compatibility\tabcellsep 0.7\\
Perceived ease of use\tabcellsep 0.9\\
Perceived utility\tabcellsep 0.64\\
Trust\tabcellsep 0.89\\
Perceived risk\tabcellsep 0.72\end{longtable} \par
 
\caption{\label{tab_6}Table 7 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{8} \par 
\begin{longtable}{P{0.04018181818181818\textwidth}P{0.1638181818181818\textwidth}P{0.1390909090909091\textwidth}P{0.12054545454545454\textwidth}P{0.09581818181818182\textwidth}P{0.08036363636363636\textwidth}P{0.06490909090909092\textwidth}P{0.043272727272727275\textwidth}P{0.024727272727272726\textwidth}P{0.009272727272727273\textwidth}P{0.0061818181818181816\textwidth}P{0.0061818181818181816\textwidth}P{0.0061818181818181816\textwidth}P{0.015454545454545453\textwidth}P{0.0061818181818181816\textwidth}P{0.009272727272727273\textwidth}P{0.0061818181818181816\textwidth}P{0.0061818181818181816\textwidth}P{0.0061818181818181816\textwidth}}
\tabcellsep EA\tabcellsep IS\tabcellsep CF\tabcellsep I\tabcellsep O\tabcellsep OO\tabcellsep T\tabcellsep RP\tabcellsep ORI\tabcellsep CP\tabcellsep CA\tabcellsep RC\tabcellsep NRI T\tabcellsep IT\tabcellsep TTF\tabcellsep PA\tabcellsep IC\tabcellsep IU\\
EA\tabcellsep 0.87 85\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
IS\tabcellsep -0.08 4\tabcellsep 0.73 0\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
CF\tabcellsep 0.46 8\tabcellsep -0.21\tabcellsep 0.89 4\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
I\tabcellsep 0.38 8\tabcellsep -0.20 2\tabcellsep 0.30 2\tabcellsep 0.88 7\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
O\tabcellsep 0.11 9\tabcellsep -0.10 9\tabcellsep 0.13 1\tabcellsep 0.05 9\tabcellsep 0.70 7\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
OO\tabcellsep -0.01 8\tabcellsep 0.13 5\tabcellsep 0.20 5\tabcellsep -0.01\tabcellsep 0.48 5\tabcellsep 0.77 5\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
T\tabcellsep -0.67\tabcellsep 0.00 5\tabcellsep -0.09 2\tabcellsep -0.04 1\tabcellsep -0.21 6\tabcellsep -0.17 1\tabcellsep 0.96 4\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
RP\tabcellsep -0.03 3\tabcellsep 0.11 9\tabcellsep 0.05 5\tabcellsep 0.01 9\tabcellsep 0.15 1\tabcellsep 0.11 2\tabcellsep -0.15 9\tabcellsep 0.77 4\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_7}Table 8 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{9} \par 
\begin{longtable}{P{0.04375\textwidth}P{0.08437499999999999\textwidth}P{0.040624999999999994\textwidth}P{0.12187499999999998\textwidth}P{0.09999999999999999\textwidth}P{0.12187499999999998\textwidth}P{0.08437499999999999\textwidth}P{0.253125\textwidth}}
\tabcellsep \tabcellsep \tabcellsep Estimate\tabcellsep S.E.\tabcellsep C.R.\tabcellsep P\tabcellsep H\\
IC\tabcellsep <---\tabcellsep EA\tabcellsep ,019\tabcellsep ,381\tabcellsep ,049\tabcellsep ,961\tabcellsep Not confirmed\\
IC\tabcellsep <---\tabcellsep IS\tabcellsep ,067\tabcellsep ,195\tabcellsep ,344\tabcellsep ,731\tabcellsep Not confirmed\\
IU\tabcellsep <---\tabcellsep CF\tabcellsep 2,069\tabcellsep ,544\tabcellsep 3,802\tabcellsep ***\tabcellsep Confirmed*\\
IC\tabcellsep <---\tabcellsep PAa\tabcellsep ,579\tabcellsep ,245\tabcellsep 2,361\tabcellsep ,018\tabcellsep Confirmed *\\
IC\tabcellsep <---\tabcellsep I\tabcellsep ,797\tabcellsep ,181\tabcellsep 2,124\tabcellsep ,001\tabcellsep Confirmed *\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep Confirmed *\\
IU\tabcellsep <---\tabcellsep T\tabcellsep -,076\tabcellsep ,056\tabcellsep -1,971\tabcellsep ***\tabcellsep \\
IU\tabcellsep <--\tabcellsep Oo\tabcellsep -,165\tabcellsep ,083\tabcellsep -1,992\tabcellsep ,046\tabcellsep Confirmed *\end{longtable} \par
 
\caption{\label{tab_8}Table 9 :}\end{figure}
 			\footnote{© 2021 Global Journals} 		 		\backmatter  			  				\begin{bibitemlist}{1}
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\end{document}
