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African Journal of Agricultural Marketing ISSN: 2375-1061 Vol. 3 (6), pp. 224-228, June, 2015. Available online at 
www.internationalscholarsjournals.org © International Scholars Journals 

 

Author(s) retain the copyright of this article. 

 

 

 

 

Full Length Research Paper 
 

Various factors that can influence the adoption of sales 
force automation and analyze their effect on technology 

adoption 

 

Martha Iman Waiguru 
 

Department of Marketing, Faculty of Management Sciences, University of Nairobi, Nairobi, Kenya. 
E-mail: iman.martha33@uonbi.ac.ke 

 

Accepted 28 May, 2015 
 

Salespersons are adopting and using a variety of technologies to increase their selling productivity and 
efficiency at different rates. This study identifies various factors that can influence the adoption of sales 

force automation and analyzes their effect on technology adoption. An explanatory research design was 

used and data collected by means of self-administered questionnaires to the target population. Reliability 

and correlation analysis were conducted to establish relationships between the research variables. Logit 

regression showed that social factors, system characteristics, organizational factors and salesperson 
characteristics significantly affect technology adoption. The major reason for such failure rates seems to 

be that the experienced salespersons frequently reject the new sales technologies. The study recommends 

that insurance companies should create an enabling environment for sales agents to adopt technology and 

improve their performance and gives further research directions. 
 
Key words: Sales force, self-efficacy, technology, adoption. 

 
 
INTRODUCTION 
 
In the competitive environment, success depends on 
effectiveness of the sales force, developing and 
maintaining customer relationships. Consequently, firms 
are attracted by the customer relationship management 
related technological capabilities including sales force 
automation systems (SFA). SFA refers to the concept, 
tools, system, or the technology; that often describes the 
process of automating sales activities within a firm 
(Lingaiah et al., 2003). Through its boundary spanning 
activity, sales force plays a critical role in building 
mutually beneficial long-term customer relationships with 
clients  (Weitz and Bradford, 1999).  SFA  represents  the  
 

CRM application in support of selling tasks and it is of 
great potential for collection and distribution of market 
information and the creation of valuable customer 
dealings (Day, 1992). SFA encompasses a set of tools 
related to a variety of tasks and functions such as 
communication, presentation generation or customer 
information management. As competition increases and 
technology advances, organization continues to seek 
ways to adjust to changing business environments. This 
is especially true, in the personal selling context where 
salespeople are recognized as the boundary spanners 
and are expected to be relationship managers (Kotler and 

 
 



Martha      224 
 
 
 
Armstrong, 1994). The salesperson is constrained to do 
more in less time and technological advancements have 
become an integral part of the personal selling and sales 
manage-ment process. Sales technology enables sales 
people answering the queries of customers to effectively 
provide competent solutions. This can lead to strong 
relationship between a salesperson and customer. 
However previous studies (Homburg et al., 2010) have 
shown that even superiors who have a less intense 
relationship with salespeople still exert a significant 
influence on their SFA adoption.  

Kenya’s insurance industry consisted of 43 insurance 
companies and 2 reinsurance companies licensed to 
operate in Kenya. In addition, there were 201 licensed 
brokers, 21 medical insurance providers (MIPS) and 
2,665 insurance agents. Insurance policies are sold by 
agents who are recruited by the insurance companies 
and are usually not employees of these institutions. As 
such, the agents earns on commission bases and have to 
work extra hard to have their commissions grow from one 
level to the other. The individual companies invest on 
SFA tools to be used by their sales team.  

Taylor (1993) reports that SFA provides salespeople 
with faster access to information, thus reducing the time 
required to prepare for a client presentation and reducing 
the number of follow-ups when further information is 
requested. Verity (1993) identifies several additional 
benefits from SFA, including the reduction of errors 
common with manual sales processing, reduced support 
costs, improved close rates, and an increase in the 
average selling price through more accurate and timely 
pricing information. Despite the benefits, the adoption of 
SFA technology by the sales force continues to be 
sluggish. 

Previous studies investigating drivers of salespeople’s 
SFA adoption have mainly scrutinized predictors on the 
level of salespeople (within-level analysis). Hence, these 
studies have mostly neglected the social influence of 
coworkers’ and superiors’ on salespeople’s SFA adoption 
(Homburg et al., 2010). The purpose of this study was to 
assess the effect of various factors on sales force adoption 
of technology among insurance sales agents in Kenya. This 
study adopted the TAM model by Venkatesh and Davis 
(2000) which provided more detailed explanations for the 
reasons participants finds a given system useful. This study 

analyzed the effect of system characteristics, ease of 
use, social characteristics and organizational 
environment on salesperson technology adoption. 
 
 

LITERATURE REVIEW 

 
Theory of Reasoned Action (TRA) is a widely validated 

intention model that has proven successful in predicting 

and explaining behavior across a wide variety of domains 

(Fishbein and Ajzen’s, 1975). Ajzen (1985) extended the 

theory by  including  another construct  called   perceived  

 
 
 

 
behavioral control, which predicts behavioral intentions 
and behavior. The extended model is called the Theory of 
Planned Behavior (TPB). Previous studies (Mathieson, 
1991; Taylor and Todd, 1995; Venkatesh and Davies, 
2000) have used the two theories for studying the 
determinants of Technology Adoption usage behavior.  

The Technology Acceptance Model (TAM) was theore-
tically derived from Fishbein and Ajzen’s (1975) Theory of 
Reasoned Action (TRA), and attempts to explain the 
determinants of computer use across a broad range of 
end-user computing technologies and populations (Davis 
et al., 1989). TAM explains an individual’s acceptance of 
computer technology based on two specific beliefs: 
perceived usefulness (that is, the degree to which a 
person thinks that using a system enhances his/her 
performance) and perceived ease of use (that is, the 
extent to which an individual believes that using the 
technology requires little effort). TAM theorizes that both 
beliefs directly determine adoption. The theory also 
suggests that perceived ease of use influences perceived 
usefulness, because, technologies that are easy to use 
can be more useful. In fact, the efforts saved due to easy-
to-use systems may be reused to complete more work for 
the same overall effort (Davis et al., 1989).  

SFA systems consist of centralized database systems 
that can be accessed through a modem by remote laptop 
computers using special SFA software. An SFA system 
also enables a salesperson to file regular reports 
electronically without having to travel to the central office 
in person. The social factors of SFA-applications increase 
with the number of users within a focal salesperson’s 
social environment (Markus, 1990). Secondly, social 
influence may be normative in nature and affects social 
persuasion and interpersonal communications. 
Demographic characteristics comprise one of the factors 
(other than personal and environmental) which play an 
important role in determining the timing of the adoption of 
an SFA system (Chen et al., 2011). This includes age, 
experience and education. 

In sales automation, user training in a system with field 
support have been proposed as critical success factors 
for intra-firm adoption. In addition, user training is used to 
inculcate corporate goals and increase salespeople’s 
motivation to adopt the technology. Personal innovative-
ness has a long standing tradition in the fields of 
marketing and innovation adoption and better realize the 
usefulness of these systems for their sales activities 
(Churchill et al., 1993). The concept of computer self-
efficacy on how well one can execute a course of action 
required to deal with prospective situations is also very 
important in SFA (Bandura, 1986). Several studies have 
found empirical evidence to support the fact that self-
efficacy in the domain of computer technology is signifi-
cantly related to the perceptions users hold about these 
technologies.  

Adoption of innovation is typically considered a discrete 

or   dichotomous   phenomenon  (Westphal  et al.,  1997). 



 
 
 

 
Sales force technology usage has changed the methods 
of selling and requires salespeople to develop a 
technological orientation to access, analyze, and 
communicate information in order to establish a strong 
relationship with customers (Hunter and Perreault, 2006). 
Sales technology enables salesperson’s answering the 
queries of customers to effectively provide competent 
solutions. This can lead to strong relationships between a 
salesperson and a customer. Thus, technology tools are 
used not only for smoothing the work process but they 
also have strategic utilizations.  

The ongoing changes and challenges that characterize 
today’s business environment have made it far more 
difficult for firms to compete effectively based on 
traditional marketing. As a consequence, we have begun 
to witness a transition wherein firms are extending their 
focus from simply selling to business customers to 
serving them more effectively in different ways 
(Parasuraman and Grewal, 2000). This transition includes 
the dramatic growth and use of customer relationship 
management (CRM) technology to building a competitive 
strategy (Musalem and Joshi, 2009). The tremendous 
growth in CRM and sales force automation (SFA) 
systems that integrate tools such as planning and product 
configuration to make salespeople more efficient and 
effective (Moutot and Bascoul, 2008) and the successfully 
adoption help firms exploit their sales force capability and 
enhance selling techniques, thereby increasing 
performance (Hollenbeck et al., 2009; Rapp et al., 2010). 
 
 
 
RESEARCH METHODOLOGY 
 
The study adopted explanatory design in order to establish casual 
relations between the variables. This quantitative study used 
primary data which was obtained from sales agents in the insurance 
industry in Kenya. The target population was 2665 registered 
insurance sales agents as per the Insurance Regulatory Authority 
(2013). A sample of 173 sales agents were randomly selected and 
issued with a questionnaire adopted and modified from previous 
studies. From the questionnaires issued, 163 were received; out of 
which 7 were rejected because they were incomplete leaving 156 
usable questionnaires. Respondents were assured of confidentiality 
of their responses by not sharing any information gathered from 
them and by not writing their names on the questionnaire. 

There were four independent variables in the study. System 
characteristics variable had two sub-constructs; Perceived 
useful-ness and Perceived ease of use and were measured 
using a five point Likert scale. Salesperson characteristics 
variable also has two sub-constructs, innovativeness and 
computer self-efficacy, and was measured using a five point 
likert scale. While organizational facilitators variable has two 
sub-constructs, user training and technical support, measured 
through a likert scale. The fourth variable of social influence was 
measured with a five point likert scale addressing peer usage 
among other sales agents. The dependent variable was a 
binary measure of usage or not using SFA systems. 

The data were entered into an SPSS package and then 
descriptive analysis of the data was conducted in order to 
check the representativeness of the respondents and the 

nature  of  their  responses.  Thereafter,  a  logit model was  

 

225       Afr. J. Agric. Mark. 
 
 
 
 

 used to determine the effects of the independent variables on 

sales force technology adoption among insurance sales agents. 

The major focus of the study was the likelihood or probability of 

the outcome, that is, whether the respondent has adopted 

technology or not, The binary response in this study was whether 
the respondent had adopted technology (“Success”) or had not 

adopted technology (failure) and the analytical model was as 
follows: Logit P(Y) = α + ∑βiXi + μi 
 
Where: 
 

Yi = 1 if success (respondent has adopted technology  
= 0 if failure (respondent has not adopted technology  

α = Constant term  
βi’s = Logistic coefficients for the independent 
variables μi = Error term 

Xi’s = Independent variables such 
that: X1 = social characteristics 

X2 = system 
characteristics X3 = 
organization facilitators 

X4 = Salesperson characteristics. 
 

 
RESULTS 
 
A total of 163 questionnaires were received out of the 
possible 173.Out of the 163, seven questionnaires were 
rejected because they were incomplete. The respondents 

had only answered the personal information section and 
the rest of the questions were left un- answered.The 
majority of the insurance sales agents who responded 
were between the age of 26 - 35 years (65,4%), only 16% 
of the respondents are above age of 36 years. Most 
(57.7%) of the insurance agents are diploma holders with 
7.1% are degree holders and the rest 35.3% only have 
secondary level education. The results indicate that most 
(74.4%) agents have only three years in the profession 
due to the fact, sales agents do not work for long, they 
opt to look for other jobs. 

 
Descriptive statistics 
 
The results indicate that the majority of the respondents 
have not adopted technology (Mean 1.4786, Sd .37898) 
in their selling process. The respondents perceive that it 
is not very difficult to use. The results indicated that a few 
agents would experiment on a new technology while it 
was clear that a few would really innovate on information 
technology. The results indicated that majority of the 
sales agents computer efficacy is high with the mean 
being above 50% on all items that were being measured. 
The results indicated that majority of the sales people in 
the insurance industry actually do receive training on the 
usage of SA-tools of their organizations.  

Sales agents work in teams and so there is a lot of 

influence from the peers. This was confirmed by the 

analysis done in that above 50% of the sales agents do 

make use of the SA tools and influences others to follow 

suit. The support given by the management is quite good; 

with above 50% of those interviewed feeling  that  enough 



 Martha      226 
 
 
 

Table 1. Descriptive Statistics of the study Variables. 
 

 
Variable 

Mean Standard deviation  Skewness  Kurtosis 
 

 
Statistic Statistic Statistic Standard error Statistic Standard error  

  
 

 System characteristics 3.0248 .49588 -.170 .194 -.355 .386 
 

 Salesperson characteristics 3.5874 .24431 1.216 .194 5.682 .386 
 

 Organization facilitators 3.9268 .90138 -.217 .194 -.774 .386 
 

 Social characteristics 3.1453 1.08570 -.689 .194 -.468 .386 
 

 Tech adoption 1.4786 .37898 .147 .194 -1.380 .386 
  

Source: Research data (2013). 
 

 
Table 2. Correlation of independent variables of SFA adoption. 

 
 
Pearson correlation 

Organizational System Social Salesperson 
Adoption 

 

 
factors characteristics characteristics characteristics  

   
 

 Organizational factors 1     
 

 system characteristics .414
**
 1    

 

 social characteristics .785
**
 .304

**
 1   

 

 salesperson characteristics -.325
**

 -.143 -.280
**
 1  

 

 Adoption .409
**
 .086 .304

**
 -.133 1 

  
**Correlation is significant at the 0.01 level (2-tailed). Source: Research data (2013). 

 

 
support is given. Insurance sales agents really do require 

a lot of technology in their selling process; here the 

results indicated that they have adopted technology 

differently. Less than 50% (mean = 1.4786) of all those 

interviewed use the company sales automation tools 

frequently. Some of the sales agents fully use the capa-

bilities of the SA program of their companies (Table 1). 
 
 
Relationships 
 
The correlation results (Table 2) show that the four 

variables identified affecting technology adoptions were 
correlated. Technology adoption has a significant rela-

tionship with organizational (p .409) and social (p .304) 
factors. The relationship with system characteristics was 

not significant. The results indicated that salesperson 
characteristics negatively (p -.133) influence the adoption 
behavior of the insurance salespeople. The results also 

show a negative relationship with social and organization 
characteristics. This implies that sales agents who have 

advanced in age or with many years’ experience are not 
willing to change and start using new technologies. 
 
 
Logit regression 
 
A logit regression was run to test how various variables 

affect technology adoption. It was realized that among the 

four independent variables, the organization variable 

positively affects the technology adoption of sales agents, 

 

 
while social characteristics and system characteristics 
affected technology adoption slightly and salesperson 
characteristics do not have a positive effect on the 
adoption of technology. From the results, it was clear that 
the sales agents have not used their sales automation 
capabilities fully because P(Y) = 0.26004452 which is 
closer to 0. This showed that the four variables identified 
in this study influence technology adoption. The results 
were as shown in Table 3.  

The study results show that organizational factors, 
social and system characteristics do affect technology 
adoption among insurance agents and that the sales-
person characteristics negatively influence technology 
adoption among insurance agents. The salesperson 
characteristics that really affect the technology adoption 
include the age and education levels of the insurance 
agents. The young people seemed to integrate 
technology into their work while the old were glued to the 
old selling methods. It was also noted that the sales 
agents have not adopted technology in their sales 
activities as it is supposed to be. The results of this study 
are in line with previous studies on TAM e.g. Homburg et 
al. (2010) and Chen et al. (2011). 
 
 
DISCUSSION AND CONCLUSIONS 
 
The study findings of this study show the challenges in 

automation of sales force activities. Like in previous 

studies (Chen et al., 2011), conscientiousness is posi-

tively related to the efficient use of sales force automation 



227       Afr. J. Agric. Mark. 
 
 
 
 
Table 3. Marginal effects after logit. 
 

Variable dy/dx Std. Err. z P>|z| [95% C.I.] X Y 

zorgan~s -.2531228 .08667 -2.92 0.003 -.423 -. 083246 -.004102 
zsyste~s .098604 .05148 1.92 0.055 -.002303 .199511 .003576 
zsocia~t -.0100023 .06245 -0.16 0.873 -.132397 .112392 -.00508 
ZSales~r .1126984 .08764 -1.29 0.198 -.284465 .059068 -.121806 
_Iage_2* .0135901 .13833 0.10 0.922 -.257526 .284706 .322581 
_Iage_3* -.0255411 .1455 -0.18 0.861 -.310718 .259636 .335484 
_Iage_4* -.0273162 .11849 -0.23 0.818 -.259555 .204922 .16129 
_Ieduc~2* .1846403 .09342 1.98 0.048 .001535 .367745 .574194 
_Ieduc~3* .5155765 .16904 3.05 0.002 .184264 .84689 .070968 
_Iexpe~2* .1704035 .16131 1.06 0.291 -.14575 .486556 .206452 
_Iexpe~3* -.1212711 .15866 -0.76 0.445 -.432235 .189693 .051613 

 
(*) dy/dx is for discrete change of dummy variable from 0 to 1; y =Pr (techadoptionnew4) 

(predict); Y= 0.26004452. Source: Research data (2013). 
 

 
(SFA) for planning and territory management. Although 
the benefits of SFA are clear, management require to 
provide user support. Users of SFA can produce sales 
forecasts and analyze reasons for won and lost 
opportunities. In addition sales automation software 
enables sales representatives to manage their client’s 
lists contacts, products, price lists, orders, documents 
and electronic mail from remote regions. The SFA also 
lower the cost of leads and sales, enhancing teamwork 
and productivity, improving customer satisfaction and 
retention, facilitating communication with the office and 
instantaneous forecasts.  

There is need to carry out a continuous training needs 
assessment to be able to know the gap between what the 
sales agents already know and what they ought to know 
so that any time there is a gap, a training session should 
be carried out in order for them to appreciate and 
embrace technology. Prior research provides strong 
conceptual as well as empirical evidence for within-level 
relationships at the salespeople’s level (Venkatesh, 
2000). The findings demonstrate that coworkers’ and 
superiors’ SFA adoption has a positive effect on 
subordinates’ SFA adoption which goes beyond the 
commonly tested determinants (Homburg, 2010). 

Sales people work in teams, and such, it is 
recommended that the management should really try to 
ensure that the teams are cohesive and they should also 
come up with team building strategies which will enable 
the members in various teams to share their experiences 
and will develop each other as far as technology usage is 
concerned. Such strategies include holding team building 
meetings away from the normal working environment.  

The management of insurance companies should en-

sure installations of SFA tools in their companies in order 

to reap its benefits which will surpass the initial costs. 

Sales force automation maintains records of customers 

and allows sales managers to track the activities of their 

sales people. It also help sales representatives sell more 

 

 
consultatively by providing survey questions to access 

customer needs, and helps to attract new representatives 

to the firm. Other benefits of SFA are faster feedback to 

the marketing department of product/service, problems 

encountered by customers, more accurate pricing and 

ordering process and the provision of a central database 

of customer profile. 
 
 
Conflict of Interests 
 
The authors have not declared any conflict of interests. 
 
 
 

REFERENCES 
 

Ajzen I (1985). From Intentions to Actions: A Theory of 
Planned Behavior: In action control: From cognition to 
behavior. J. Organizational behavior and human 
decisionprocesses. 4:11-39 Bandura A (1986). Social 
Foundations of Thought and Action. A Social  

cognitive Theory. Englewood Cliffs, NJ7 Prentice Hall. 
Chen C-W, Tseng C-P, Lee K-L, Yang H-C (2011). 

Conceptual framework and research method for 
personality traits and sales force automation usage, 
Sci. Res. Essays 6(17):3784-3793. 

Churchill GA (1973). A paradigm for developing better 
measures of marketing constructs. J. Marketing 
Research 16: 64–73. 

Davis FD, Bagozzi RP, Warshaw PR (1989). User 
Acceptance of Computer Technology: A Comparison of 
Two Theoretical Models. J. Manage. Sci. 35(8): 982–
1003. 

Fishbein M, Ajzen I (1975). Belief, attitude, intention and 
behavior: An introduction to theory and research. 
Reading, MA7 Addison-Wesley. 

Homburg C, Jan W, Christina K (2010). Social influence 
on  salespeople ’s    adoption  of  sales  technology: a  



Martha      228 
 
 
 
 

multilevel analysis, Academy of Marketing Science. 
Hunter GK, Perreault Jr. WD (2006). “Sales Technology 

Orientation Information Effectiveness, and Sales 
Performance,” J. Personal Selling & Sales 
Management. 26(2):95–113.  

Kotler P, Armstrong G (2004). Principles of Marketing, 
Prentice Hall New Jersey, Englewood Cliffs. 

Lingaiah N, Pires G, Stanton J (2003). An Evaluation of 
Marketing Issues in Sales Force Automation. ANZMAC 
Conference Proceedings Adelaide. 

Markus R (1990). Toward a critical mass theory of 
interactive media: Universal access, interdependence 
and diffusion. In Fulk J., & 
Steinfeld C. (Eds.), Organizations and communication 
technology. Pp. 194–218.  
Jean-Michael M, Ganeal B (2008).”Effects of sales force 

Automation Use on sales force activities and customer 
Relationship Management Processes” J Personal 
Selling Sales Manage, .28(2):67-184. 

Mathieson K (1991). Predicting user intentions: 
Comparing the technology acceptance model with the 
theory of planned behavior. Inform. Syst. Res. 2(3): 
173-191. 

 

 
 
 

 

Musalem A,Yogesh VJ (2009). ”How Much should you 
invest in each customer relationship? A competitive 
strategic Approach” Market. Sci. 28(3):555-565. 

Parasuraman A, Dhrum G (2000).”Serving customers 
and consumers effectively In the twenty -first century; 
A conceptual framework and overview”, J. Acad. 
Mrkt. Sci. 28(1):9-12. 

Taylor S, Todd PA (1995). Understanding information 
technology usage:a test of competing models. Inform. 
Syst. Res. 6:144-176.Venkatesh V (2000). 
Determinants of Perceived Ease of Use: Integrating 
Control, Intrinsic Motivation, and Emotion into the 
Technology Acceptance Model. Inform. Syst. Res. 
11(4): 342–365.  

Venkatesh V, Davis FD (2000). A theoretical extension of 
the technology acceptance model: Four longitudinal 
field studies. J. Manage. Sci. 46(2):186– 204. 

Weitz BA, Bradford KD (1999). Personal selling and sales 

Management: A relationship marketing perspective, J. 

Acad. Mrkt. Sci. 27(2):241-254. 

 

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