Examining Gender Differences in System
Switch Behavior
Emergent Research Forum Papers
Yan (Mandy) Dang
Northern Arizona University
[email protected]
Yulei (Gavin) Zhang
Northern Arizona University
[email protected]
James N. Morgan
Northern Arizona University
[email protected]
Abstract
When new technology is introduced through an upgrade from or a replacement of an existing system,
switching costs can be an important factor influencing the adoption of the new system. Recent research
has identified several categories of switching costs including: uncertainty costs, emotional costs, learning
costs, reduced performance costs, and sunk costs. A substantial amount of literature suggests that there
are gender differences which may impact the degree to which these switching costs are felt. In this study
we look at gender differences in switching costs associated with the transition between two learning
management systems. We find that females generally experience higher switching costs than males and
this difference is statistically significant for emotional and sunk costs.
Keywords
IS adoption, switching costs, prior system impact, e-learning system transition
Introduction
Gender has always been an important factor to consider when designing, adopting, and evaluating an
information system (Venkatesh et al. 2003; Igbaria and Chakrabarti 1990; Gilroy and Desai 1986; Ahuja
and Thatcher 2005).
Modern companies and institutions tend to leverage new technology and systems to help improve task
effectiveness and efficiency. Although new technology and systems can often provide better services and
functions and potentially lead to better task performance, users may not always be willing to switch to
them. To understand such phenomenon, researchers have investigated system transitions based on
different types of switching costs (Kim 2011; Kim and Perera 2008). However, these studies did not
examine gender differences in the extent to which these switching costs were experienced or look at how
gender might moderate the effects of switching costs on system switch intentions.
In this study, we will examine the impact of gender on different types of switching costs. Specifically, we
studied a system transition between two learning management systems, and compared user perceptions
of uncertainty costs, emotional costs, learning costs, reduced performance costs and sunk costs between
the two gender groups.
Switching Costs
In assessing users’ perceptions of the transition from a prior system to a new one, previous research has
focused on switching costs (Kim 2011). Different types of switching costs have been identified, including
uncertainty costs, emotional costs, learning costs, reduced performance costs, and sunk costs (Kim 2011;
Kim and Perera 2008).
1
Track Title
During system transition, uncertainty costs are expected when the performance level of the new system is
unknown (Lam et al. 2004). Emotional costs refer to the psychological discomfort associated with
replacing the prior system with the new one (Guiltinan 1989). Learning costs include any costs that are
needed for users to learn how to use the new system (Burnham et al. 2003). Reduced performance costs
refer to the loss of benefits when switching to a new system (Jones et al. 2002). Sunk costs are about the
time, effort, and money that had already been invested in creating and maintaining the prior system
(Jones et al. 2002).
When studying system transition, prior research has investigated the impact of different switching costs
on various types of systems. For example, Kim and Perera (2008) found significant impacts of reduced
performance costs and emotional costs on user resistance to change to a new Web browser. Zhang et al.
(2009) found that sunk costs could significantly influence bloggers’ intention to switch their blog services.
Keil et al. (1995) found that sunk costs had a significant impact on the escalation of information systems
projects. However, little research has been seen to examine gender differences on different types of
switching costs during the system transition process.
Gender Differences
General studies of computer use by gender have shown mixed results. Early studies found women to have
significantly higher computer anxiety than men with respect to computer use (Igbaria and Chakrabarti
1990; Gilroy and Desai 1986). However a study of internet use found no gender difference in overall
internet use and found that women were more likely than men to buy on-line (McElroy, eta al. 2007).
Awad and Ragowsky (2008) found trust to be a significantly more important factor in women’s intentions
to shop online and found that women tended to place more value on the responses of others than did
men.
Studies specifically looking at gender differences in adoption of new technology have often focused on
factors identified in the UTAUT2 model (Venkatesh et el. 2012) and its predecessors. Vankatesh and
Morris (2000) found women’s intentions to use a new technology were more influenced (than men’s) by
ease of use and by social norms (expectations of bosses and other respected authorities). A study of
intentions to use chat rooms similarly found women’s intentions to be more influenced than men’s by
normative pressure (Nysveen et al. 2005) and that study also found women to be more influenced than
men by perceived enjoyment in using the product. Nel and Raleting (2012) found that ease of use was
more important in affecting women’s intentions and perceived usefulness was more important in
explaining men’s intentions to use cell phone banking services. Ahuja and Thatcher (2005) found that
women were more negatively impacted than men by quantitative overload (work-life balance issues) when
it came to willingness to innovate in the use of technology, and Ahuja (2002) noted that women are
perceived by themselves and by other as having primary responsibility of child-rearing and housework,
and thus may be more sensitive to overload.
A study of customer loyalty in the face of a service failure by a bank (DeMatos et al. 2007) did include a
single instrument designed to capture switching cost, as well as, including gender as a moderating factor.
This study found that higher perceived switching costs did positively influence loyalty to a customer’s
current bank and found that women were statistically more likely than men to remain loyal to their
current bank. However, no analysis of the relationship between switching cost and gender was presented.
A study of bloggers’ switching behavior (Zhang et al. 2009) also included a single switching cost
instrument as a moderating factor, but it was not found to influence intentions to switch and its
relationship to gender was not examined. That study did find that women were more influenced by
satisfaction with current service and less influenced by the attractiveness of alternative services than
males. In addition, a recent study (Dolan and Stevens 2013) found female college freshmen to be more
likely than their male counterparts to consider sunk costs (one of the factors in switching costs) in
decision making. They found that both men and women became less influenced by sunk cost as they
matured and had more exposure to economics and business instruction.
Impact of Gender on Switching Costs
While the gender differences observed above were not directly linked to switching costs, they do appear to
generally support the hypothesis that women will experience greater switching costs than men. The
2
Twenty First Americas Conference on Information Systems, Puerto Rico, 2015
Short Title up to 8 words
factors of greater computer anxiety and greater focus on perceived ease of use by women is likely to affect
their perceptions of uncertainty costs, learning costs and reduced performance costs. The greater
quantitative overload experienced by women is likely to lead to greater uncertainty, learning and reduced
performance cost. The factors of greater loyalty to the existing system, greater influence of satisfaction
with the current system, and greater importance placed on trust in the current system would be expected
to lead to women having greater emotional costs than men. Finally, the Dolan and Stevens (2013) study
would suggest that women may be more influenced than men by sunk costs. Thus, we hypothesize that
women will experience more of each type of switching cost than their male counterparts.
Research Method
Systems
The systems we used in this study are the learning management systems adopted by a major public
university in the western part of the United States. At the time of the study, the university was in the
middle of adopting a new learning management system - Blackboard Learn, to replace its previous version
- Blackboard Vista. The two systems provide similar functions to assist students’ learning activities with
certain improvements claimed in the new system. The overall design and user interfaces of the two
systems are different. The move to the new system is mandatory, however, individual students do have
considerable choice with regard to the extent to which they utilize the new system.
Survey
We used the survey method in this study. We invited students who had the experience of using both
systems to participate in the study. Their participation was voluntary. Extra credit was given as the
incentive. Participants were asked to fill in a questionnaire with measurement items about different types
of switching costs. The measurement items were adopted from Kim and Perera (2008) with wording
changes to fit the context of this study. The 7-Likert scale was used for all items. In total, there are 138
participants in the study, 93 males and 45 females. The average age of participants was 22.7. Variations in
age and ethnicity among the participants was not sufficient to allow assessment of the impact of these
demographic factors on perceptions of switching costs.
Analyses and Results
Independent group t-tests were conducted to compare between male and female participants. Table 1
shows the descriptive statistics. Table 2 lists the comparison results. For each type of switching costs, the
mean value for male was lower than that for women. The T-test results indicate that female participants
perceived significantly higher levels of emotional costs and sunk costs than male participants. The
differences on uncertainty costs, learning costs, and reduced performance costs were not statistically
significant.
Uncertainty Costs
Male
Mean/Std Dev.
3.89/1.12
Female
Mean/Std Dev.
4.03/0.88
Emotional Costs
3.38/1.79
4.04/1.96
Learning Costs
3.73/1.80
4.13/1.85
Reduced Performance Costs
3.55/1.78
3.86/1.73
Sunk Costs
3.67/1.72
4.23/1.64
Switching Costs
Table 1. Descriptive Statistics
Twentieth Americas Conference on Information Systems, Savannah, 2015
3
Track Title
Switching Costs
Uncertainty Costs
Comparison
Male<Female
p-value
0.225
Emotional Costs
Male<Female
0.026*
Learning Costs
Male<Female
0.112
Reduced Performance Costs
Male<Female
0.174
Sunk Costs
Male<Female
0.037*
* significant at the level of 0.05
Table 2. T-Test Results
Conclusion and Future Research Plan
Conclusion
Understanding factors affecting system switch decisions is important and crucial for success system
adoption and replacement. However, little research has been done to specifically examine gender
differences in system switch behavior and intentions. To address the gap, this study compared user
perceptions on five dimensions of switching costs between the two genders, including uncertainty costs,
emotional costs, learning costs, reduced performance costs, and sunk costs. Overall, we found a general
pattern that women tended to perceive a higher level of switching costs than men in all five dimensions.
This pattern was statistically significant on emotional costs and sunk costs. Based on the results of this
study, we would recommend that, in order to make a smooth and successful transition to a new system,
system, developers and managers need to take switching costs and gender differences into consideration.
For example, different strategies and proper training, especially focusing on female users, could be
developed and leveraged to help ease the transition process. It should be expected that women could be
emotionally more loyal to the prior system, and less willing to switch to the new one because of all
investments already made to be proficient in using the prior system. More effort may need to be made to
help female users to deal with emotional costs and sunk costs during system transition.
Future Research Plan
In this study, we presented our preliminary results examining gender differences in switching costs
associated with a system switch. Our statistical results suggests that there are gender differences in
perceived switching costs which may impact system switch decisions. To gain a deeper understanding of
these factors, we plan to conduct a qualitative study using the interview method in the future as an
extension of the current study. In that study, we aim at finding out the reasons why women experience
higher levels of switching costs in general and why such differences are more significant on emotional
costs and sunk costs. The following is a preliminary set of our interview questions:
• Have you experienced a high level of switching costs during the system transition process? If so, what
leads to such significant amount of switching costs?
• Among the following types of switching costs, which one(s) have you experienced during the system
transition process? Uncertainty costs, emotional costs, learning costs, reduced performance costs, and
sunk costs (definitions of these costs will be provided)
• Can you rank the following types of switching costs according to the amount you’ve experienced (if any)
from the largest amount to the smallest amount?
• Do you have any suggestions to system developers, managers, or any other related personnel about
what they could do to help reduce the switching costs you’ve experienced during the system transition
process?
4
Twenty First Americas Conference on Information Systems, Puerto Rico, 2015
Short Title up to 8 words
REFERENCES
Ahuja, M. 2002. “Information technology and the gender factor,” European Journal of Information
Systems (11), pp. 20-34.
Ahuja, M. K. and Thatcher, J.B. 2005, ‘Moving Beyond intentions and toward the theory of trying: effects
of work environment and gender on post-adoption information technology use,” MIS Quarterly
(29,3), pp.427-459wad, N. and Ragowsky, A. (2008) “Establishing trust in electronic commerce
through online word of mouth: an examination across genders” Journal of Management
Information Systems” 24(4) 101-121.
Burnham, T.A., Frels, J.K., and Mahajan, V. 2003. "Consumer Switching Costs: A Typology, Antecedents,
and Consequences," Journal of the Academy of Marketing Science (31:2), pp 109-203.
DeMatos, C. A., Henrique, J. L. and deRosa, F. 2013. “Customer reactions to service failure and recovery
in the banking industry: the influence of switching costs,” Journal of Services Marketing (27,7),
pp.526-538.
Dolan, R. C. and Stevens, J. L. (2013) “Rationality, irrationality, and ‘Predictable irrationality’: does
education, curriculum or gender matter?” Financial Services Review (22), pp. 115-132.
Gilroy, D. F. and Desai, H. B. (1986) “Computer anxiety: sex, race, and age,” International Journal of
Man-Machine Studies (25,6) pp. 711-719.
Guiltinan, J. 1989. "A Classification of Switching Costs with Implications for Relationship Marketing,"
Proceedings of the AMA Winter Educator's Conference: Marketing Theory and Practice, T.
Childers, R. Bagozzi and J. Peter (eds.), Chicago, IL, pp. 216-220.
Igbaria, M. and Chakrabarti, A. 1990. “Computer anxiety and attitudes towards microcomputer use,”
Behavior and Information Technology (9,3) pp. 229-241.
Jones, M.A., Mothersbaugh, D.L., and Beatty, S.E. 2002. "Why Customers Stay: Measuring the
Underlying Dimensions of Services Switching Costs and Managing Their Differential Strategic
Outcomes," Journal of Business Research (55), pp 441-450.
Keil, M., Truex, D.P., and Mixon, R. 1995. "The Effects of Sunk Cost and Project Completion on
Information Technology Project Escalation," IEEE Transactions on Engineering Management
(42:4), pp 372-381.
Kim, H.-W. 2011. "The Effects of Switching Costs on User Resistance to Enterprise Systems
Implementation," IEEE Transactions on Engineering Management (58:3), pp 471 - 482
Kim, H.-W., and Perera, N. 2008. "Software Continuance from the Switching Costs Perspective: A Case of
Web Browser," Proceedings of Pacific Asia Conference on Information Systems (PACIS), p.
Paper 13.
Lam, S.Y., Shankar, V., and Murthy, M. 2004. "Customer Value, Satisfaction, Loyalty, and Switching
Costs: An Illustration from a Business-to-Business Service Context," Journal of the Academy of
Marketing Science (32:3), pp 293-311.
Nel, J. and T. Raleting (2012) “Gender differences in low-income non-users’ attitude towards wireless
internet gateway cellphone banking” South African Journal of Business Management 43(3) 51-63.
Nysveen H., Pedersen, P. E. and Thorbjornsen, H. 2005. “Explaining intention to use mobile chat
services: moderating effects of gender,” Journal of Consumer Marketing (22,5) pp. 247-256.
Venkatesh, V. and Morris, M. G. 2000. “Why don’t men ever stop to ask for directions? Gender, social
influence and their role in technology acceptance and usage behavior,” MIS Quarterly (24,1), pp.
115-139.
Venkatesh, V., Morris, M.G., Davis, G.B., and Davis, F.D. 2003. "User Acceptance of Information
Technology: Toward a Unified View," MIS Quarterly (27:3), pp 425-478.
Venkatesh, V., Thong, J. and Xu, X. (2012) “Consumer Acceptance and Use of Information Technology:
Extending the Unified Theory of Acceptance and Use of Technology,” MIS Quarterly (36,1), pp.
157-178.
Zhang, K.Z.K., Lee, M.K.O., Cheung, C.M.K., and Chen, H. 2009. "Understanding the Role of Gender in
Bloggers' Switching Behavior," Decision Support Systems (47:4), pp 540-546.
Twentieth Americas Conference on Information Systems, Savannah, 2015
5