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Behavioral Intention to Use a CPQ System

CHAPTER 2. LITERATURE REVIEW

Introduction

This chapter provided a scholarly and peer-reviewed literature that addressed the behavioral intention to use a CPQ system using the antecedents of ERP and CRP adoption research. There is a management challenge for IS/IT researchers and technology adoption, and diffusion research is considered as a mature area to explore (Williams, Rana & Dwivedi, 2015). New technology developers, senior management, and implementation managers note that the lack of user acceptance can lead to monetary and resources losses (Hwang, Al-Arabiat, & Shin, 2016). There are also potential effects on an organization’s bottom line.

Understanding user intention factors to use technology has been studied in numerous quantitative and qualitative studies. Researchers studying technology adoption focus on the antecedents to technology adoption and utilization. The extant literature on technology adoption models covers diverse fields, industries, and settings. The studies have applied the constructs of the UTAUT model to various technologies. The majority of studies employed a quantitative methodology covering different countries and cultural contexts. Evaluating the technology models and behavioral intention better explains the determinants of behavioral intention and usage behavior. The organizational drivers for understanding the technology adoption are meant to guarantee user acceptance of the technology.  UTAUT has detailed the critical factors and contingencies related to the prediction of behavioral intention to use a technology and to the technology used primarily in organizational contexts (Oechslein, Fleischmann, & Hess, 2014). The UTAUT model has served as a baseline model and has been applied to the study of a variety of technologies in both organizational and non-organizational settings. However, like TAM and TRA, UTAUT is designed from an internal perspective of the organization developed for the implementation of new technologies within organizations (Rondan-Cataluña, Arenas-Gaitán, & Ramírez-Correa, 2015). The constructs of UTAUT have a utilitarian character.

The literature review presents technology acceptance using the context of behavioral intention to use CPQ by sales representatives. Although CRM and CPQ use is often mandated at the organization level, CPQ system use is often mandatory.

The extant literature supports the robustness of the UTAUT model for predicting technology adoption.  The UTAUT model explains over 70% of all the technology acceptance behavior, unlike other forms of the model that explain as little as 40% of technology acceptance factor (Williams, Rana & Dwivedi, 2015). Venkatesh et al. (2012) state that UTAUT, PE, EE, and SI lead BI to adopt a technology, whereas BI and FC characterize technology usage (Baabdullah, Dwivedi, & Williams, 2014). Research for ERP and CRP has been taken using the constructs of UTUAT but not for a CPQ context.  This chapter examines research studies on technology acceptance unique to ERP, CRM, and similar technologies. As there is no extant literature regarding CPQ adoption, CPQ related technology was applied in the literature review.  The scope of the literature review is the examination of the research framework based on the UTAUT model, additional relevant research studies conducted using the UTAUT model, and the appropriateness of the UTAUT model in examining sales user acceptance of CPQ. While the various studies contribute to realizing the utility of UTAUT in different contexts, in that respect is even the need for a systematic investigation and theorizing of the salient factors that would apply to a consumer technology use context (Rondan-Cataluña, Arenas-Gaitán, & Ramírez-Correa, 2015). The extant literature regarding CRM, ERP, and UTAUT comprised a small subset of research.  While there is a broad application of UTAUT across multiple research fields, a gap exists in the literature for CPQ and manufacturing companies. The journal articles focus on named systems and software and specific conditions.

ERP, CRM, and CPQ Systems

Many enterprises have implemented information technology to develop innovative e-business applications systems such as enterprise resource planning (ERP), customer relationship management (CRM), and supply chain management (SCM) to enhance competitive advantages. CRM is an organizational activity under the umbrella of integrated selling, marketing, and service strategy (Pai & Tu, 2011). CRM systems help organizations to gain the potential new customers by increasing new customer leads, promoting customer purchases, and maintaining healthy customer relationships (Pai & Tu, 2011).  The development and application of CRM systems are considered important issues for researchers and practitioners in recent years (Pai & Tu, 2011).   In 2001, CRM evolved to synthesize with functions of sales, customer service, and marketing activity, all based on customer orientation. The benefits of CRM implementation assist the enterprise to locate profitable markets and improve competitive advantage, through lowering cost and gaining higher customer value (Pai & Tu, 2011).  Successful CRM implementations integrate information technology information resources such as a client database, salesman interviews and customer interaction.

Enterprise resource planning systems are extensive software systems that integrate some business processes, such as manufacturing, supply chain, sales, finance, and customer service activities (Weinrich & Ahmad, 2009). ERP systems are complementary to CRM and result in significant investments in software and package customization (Doom, Milis, Poelmans, & Bloemen, 2010). The other benefits of ERP systems are a complete integration with the business processes, data entry reduction, technology upgrades, portability to other systems, and applying best practices.  An ERP implementation is an arduous process, involving different types of end user impact as well as individual, organizational, and technological factors on the usage of ERP (Rajan & Baral, 2015). Given the complexity of implementation and cross functional nature, implementing ERP in an organization is not always successful (Ling-Keong, Ramayah, Kurnia, & May Chiun, 2012).

The introduction of ERP systems within an organization is considered a strategic initiative and aligned with long-term business objectives (Ram, Corkindale, & Wu, 2014). There is an expectation that organizations will tie ERP investment to the attainment of competitive advantage (Ram, Corkindale, & Wu, 2014). Organizations are making significant investments in complex information systems such as enterprise resource planning (ERP) systems and need to understand system adoption from the user’s perspective to prepare employees to face new challenges and learn how to make good use of the technology to reap tangible benefits (Ram, Corkindale & Wu, 2014). There are sparse empirical studies on the prior factors that facilitate the achievement of such an advantage of adopting ERP (Ram, Corkindale, & Wu, 2014).  While previous research examined aspects of business process change, little research focused on the individual employee or studied the drivers of process adoption by employees on the factors influencing resistance, or the impacts of process change on employees of complex technology solutions like ERP (Ram, Corkindale, & Wu, 2014). Adopting and using specific IT is not solely dependent on the characteristics of the technology, but also dependent on other external factors such as organizational or social context, and individual characteristics and attitudes (Ram, Corkindale, & Wu, 2014).

Several models of technology adoption are used to study the acceptance and use of ERP systems by enterprises and employees. The study of this acceptance includes the Technology Acceptance Model TAM, TAM2, and TAM3, which includes the determinants of perceived ease of use (Venkatesh & Bala, 2008), and the Unified Theory of Acceptance and Use of Technology (UTAUT) (Fillion & Ekionea, 2010). Corporations have spent nearly 50% of revenue on technology, but many of these new systems fail (Fletcher, Sarkani, & Mazzuchi, 2014). The technology acceptance modeling field argues user acceptance is a cause of these failures (Fletcher, Sarkani, & Mazzuchi, 2014). Technology acceptance studies endeavor to limit the elements that direct users to assume or eliminate a technology, but existing models have multiple weaknesses impacting effectiveness and applicability (Fletcher, Sarkani, & Mazzuchi, 2014). UTAUT  is lacking in providing for situations where disconfirmation of expectations about fundamental beliefs may occur and consequently, influence outcomes such as behavioral intention and use (Venkatesh, Thong, Chan, Hu, & Brown, 2011). An analysis of acknowledged limitations across UTAUT studies indicated that focusing on a single subject regarding a community, culture, country, organization, department, person, or age group were widely reported constraints (Williams, Rana, & Dwivedi, 2015). Multiple UTAUT studies acknowledged a singular task at a given point of time, limiting the generalization of findings which weakens many of the findings (Williams, Rana, & Dwivedi, 2015).

CRM integration with marketing systems such as CPQ and other automation tools between two operations (Washington, 2017). CPQ is the system that maintains all of the product information necessary for sales teams to configure, price and sell solutions to customers. CPQ soultions rely on customer and prospect data supplied via effective CRM integrations. There is a reliance on pricing data provided by other back-office financial systems as well as inputs from the sales rep and the prospect to help pull together the assorted parts and assemblies into a configured model and a complete bill of material. CPQ extends into multiple processes in the  enterprise, making CPQ an organization-wide enterprise system. The CPQ market has grown in recent years, and the rationale to invest in CPQ is sensible as is it improves the B2B buying experience by selling the right products, at the right price, to the right customer, at the proper time, and in the buyers’ preferred channel (Bruno, 2017). CPQ benefits extend to operational efficiencies by shortening the time-to-market for new offerings, decreasing seller ramp time, automating complex back-office sales processes in the front-office, and reduces costly pricing errors (Bruno, 2017). CPQ selection and implementation must support the current sales dynamic and allow for flexible growth in the future (Bruno, 2017). The CPQ market has experienced considerable consolidation and investments in the past seven years, and vendors like CallidusCloud, IBM, Infor, Oracle, and Salesforce have all acquired CPQ solutions since 2010. Other vendors have experienced an infusion of venture capital to fuel R&D (Bruno, 2017). The acquisitions round out the offerings of CRM, commerce, or sales technology vendors, while  an infusion of cash into the marketplace has spurred rapid development and expansion of core CPQ functionality (Bruno, 2017). CPQ maturation means solution breadth and depth will make the software a viable solution for more large enterprises to evaluate in 2017.  Figure 2 shows the sampling of CPQ vendors.  Sales reps of manufacturing companies with hundreds of complex products that are struggling with providing proper quotes to customers can overcome this problem by purchasing manufacturing CPQ software. An increase in sales is directly related to increasing revenue for the business. IT research and Gartner predicted that CPQ software could help companies to grow their sales by 10 percent. There are many advantages for those in the manufacturing industry to use CPQ software to increase their productivity and sales. Applying UTAUT in a CPQ context is necessary to understand and avoid potential roadblocks for future use of the tool.

CPQ VENDORS

Figure 2.  Configure Price Quote Solutions. Adapted from The Forrester Wave by Bruno (2017). Copyright © 2017, Forrester. Used with permission.

Relevant research regarding CRM and ERP technology adoption can be classified into two categories: Exploration of acceptance degree when the individual is facing the latest technology and users’ behavioral intention and actual users’ behavior (Pai & Tu, 2011).  The related literatures for CRM and ERP, contain very few studies have been conducted regarding adoption in a manufacturing context. CPQ is the optional technology that lies between CRM and CPQ. The literature review explores and executes analysis regarding the appropriateness of the application of unified theory of acceptance and use of technology (UTAUT) to a CPQ context using the extant literature for CRM and ERP. The study will provide reference data for industries which still have not yet deployed CPQ systems or are in the post implementation phase of adoption.

Technology Acceptance Models

Adoption, or usage of technology, has been an important research topic for the past 20 years (Weinstein & Mullins, 2012). Perception based models are focused on understanding how an individual perceives a technology to explain usage or intention to use (Schwarz & Schwarz, 2014). Technology acceptance models are an effective way to gauge the use of software and a means for software process improvements. The majority of all patterns in technology adoption are based on theories coming from the behavioral science and social psychology fields (Eckhardt, Laumer & Weitzel, 2009). The low-level of acceptance and use of software has been a source of concern of multiple studies (Wallace & Sheetz, 2013) Researchers of information technology have developed sophisticated theoretic frameworks on how and why people are willing to adapt to the latest information technology (Pai & Tu, 2011).

TAM

A commonly known model for technology adoption is the Technology Acceptance Model (TAM) shown in Figure 3. Davis (1989) proposed the Technology Acceptance Model (TAM), which was based on the TRA model. The TAM model has been used as an instrument to facilitate prediction and use of software measures.  The TAM has as a basis that the primary determinants of information technologies adoption in organizations are perceived usefulness and ease of use (Davis, 1989). The model includes multiple determinants such as perceived ease of use (PEOU) and perceived usefulness (PU) to determine technology adoption by users (Davis, 1989). Perceived usefulness is the degree to which an individual believes using a particular information system or information technology enhances job or life performance (Linders, 2008). Perceived ease of use is the degree to which a person believes using a particular information system or information technology is effortless (Linders, 2008). TAM and other technology acceptance models and theories measure technology acceptance as the utilization of the system and as a result, acceptance and usage terms are used interchangeably (Youngberg, Olsen, & Hauser, 2009).

Figure 3. Technology acceptance model (TAM). Adapted from A Model of the Antecedents of Perceived Ease of Use: Development and Test, p 453 by V. Venkatesh and Fred D. Davis, 1996, Decision Sciences, 27(3), pp. 425-478. Copyright © 2007, John Wiley & Sons Inc. Used with permission.

UTAUT

Multiple models have been used to describe how users accept new technology with the primary goal of each to accurately predict technology.  UTAUT combines the elements of the Theory of Reasoned Action, Motivational Model, and the Theory of Planned Behavior (TPB), a combination of the TAM and TPB models, a Model of PC Utilization, Innovation Diffusion Theory, and Social Cognitive Theory (Venkatesh, 2015).   Although there are many models used to determine the effectiveness of technology acceptance, UTAUT has demonstrated 70% accuracy at predicting user acceptance of information technology innovations (). The UTAUT model uses underlying features of the TAM models, but also looks at four basic constructs.

  1. Performance expectancy: The extent to which users believe CPQ will help perform a task better (Venkatesh, 2016).
  2. Effort expectancy: The extent to which people believe using CPQ would be free from effort and not difficult to use (Venkatesh, 2016).
  3. Social influence: The strength with which authoritative figures have influenced a person to adopt or use a CPQ system (Venkatesh, 2016).
  4. Facilitating conditions: The perceived extent to which users believe which the organizational and technical infrastructure required for the support of the technologies exists (Venkatesh, 2016).

Users formulate beliefs about the system on multiple levels such as individual, peer, and managerial (Amoako-Gyampah, Salam, 2007). The goal of the TAM and UTAT models is to predict information system acceptance and address design problems before user interaction with the system (Dillon & Morris, 1996). By examining the presence of each constructs in a “real world” environment, researchers and practitioners are able to assess an individual’s intention to use a particular system (Dillon & Morris, 1996). Understanding use intention allows for the identification of the key influences on acceptance in any given context (Williams, Rana, & Dwivedi, 2015). Regarding interrelationships among the predictors in UTAUT, there is evidence of modest correlations among effort expectancy, social influence and facilitating conditions measured at different points in time.  These correlations are lower than the correlations of perceived usefulness (performance expectancy) over time (Venkatesh, Thong, Chan, Hu, & Brown, 2011).

UTAUT2

Venkatesh et al. (2012) proposed the UTAUT2 by extending the original UTAUT with three additional constructs, price value, hedonic motivation, and habit, to enhance its explanatory power (El-Masri & Tarhini, 2017).  UTAUT2 has been developed to translate UTAUT to the consumer context (Venkatesh et al., 2012). Models such as the TAM or the original Unified UTAUT omitted factors such as hedonic motivation making the models less viable for applications in the consumer context (Oechslein, Fleischmann, & Hess, 2014). With UTAUT2, technology acceptance research has access to a consolidated tool to explore consumers’ adoption behavior (Oechslein, Fleischmann, & Hess, 2014). There are very few empirical applications of UTAUT2 outside of consumer contexts (Oechslein., Fleischmann, & Hess, 2014).  Many ERP, CRM, and subsequently CPQ systems have consumer touchpoints, and the extant literature provides additional context to behavioral intention (Oechslein., Fleischmann, & Hess, 2014). UTAUT2 provides additional knowledge about the underlying technology as well as user acceptance of this technology (Oechslein, Fleischmann, & Hess, 2014). Venkatesh et al. (2012) emphasize the importance of testing UTAUT2 in different cultures and settings to enhance its applicability and robustness.  Factors affecting the adoption of a new information system generally vary in context, target users, and technology (Oechslein, Fleischmann, & Hess, 2014).  The UTAUT adapts the original UTAUT through the addition of a direct relationship between facilitating conditions and behavioral intention and derived from the relationship of perceived behavioral control with intention and behavior in TPB.  The effect of behavioral intention on use is moderated by experience (Venkatesh et al. 2012).

ERP and UTAUT

According to Ekanyake (2014), research on ERP usage demonstrates the actual usage of the system has a high dependency on users’ acceptance of the ERP system.  UTAUT’s six main variables are: performance expectancy (PE), effort expectancy (EE), social influence (SI), behavioral intention (BI), and usage behavior (UB), BI being both an independent and dependent variable (Williams, Rana, & Dwivedi, 2015). Performance expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), and Attitudes Towards Use of System (ATUS) positively correlated with Symbolic Adoption (SA) of ERP systems (Ekanayake, 2014). Regression analysis showed that PE, EE, and ATUS contribute to 77.7% of the total variance of SA. These findings should be of relevance to organizations who have already implemented and who intend to install ERP systems, ERP vendors, and future researchers. Organizations experiencing issues with low utilization of ERP systems can consider certain factors identified in this research to overcome their problems. ERP vendors have used various research findings to determine end customers’ requirements and incorporating perceptions of end-users when developing products (Ekanyake, 2014). Perceived ease of use and usefulness of the ERP system significantly contribute to the satisfaction with it and indirectly affect the intention to use it. PEOU has a direct positive effect on attitudes toward using ERP systems (Alok & Mocherla, 2016).

Organizations need to understand and identify factors regarding the individual, organizational, and technological characteristics for complex ERP system implementations (Rajan & Baral, 2015). Management goals extend beyond the successful use of the business system and ensuring employees are satisfied with using the system as well as empowering employees to make decisions (Rajan & Baral, 2015).  The variables from the UTAUT are relevant in explaining continuance intention to use emergent technology and shows performance expectancy, effort expectancy, social influence, facilitating conditions and intrinsic motivation together accounted for 53% of the variance of continuance intention (Bakar, 2013). Performance expectancy and intrinsic motivation were found not to be significant predictors of continuance intention (Bakar, 2013). Social expectancy and other enabling factors will significantly influence user’s social behavior on internet websites. The focusing of successful adoption of organizational culture dimension, such as offered by the research of information technology adoption of Arabic culture proposed by Al-Gahtani, Hubona, and Wang (2007) found performance expectancy and social expectancy, both positively affecting adoption behavior of using information technology. Also, the more abundant experience of computer usage will bring about higher acceptance to information technology (Pai & Tu, 2011).  The next section discusses the four core constructs of the UTAUT model that used as independent variables in this study.  The constructs are considered to be direct determinants of usage and behavior.

Performance Expectancy

The enterprise bases the decision whether or not to adopt a CRM system on a comparison with the existing system usage or current business operations (Pai & Tu, 2011). If the organization feels the CRM system is quickly apprehended and used, then the willingness to adopt is enhanced (Pai & Tu, 2011). Ling, Ramayah, Kurnia, and May (2012) posit usefulness is a robust and highly significant determinant of technology usage. PU was the more influential driver for predicting the intention to use an ERP system. The greater the PE in using the ERP system, the greater the likelihood the ERP system would be adopted (Ling, Ramayah, Kurnia, & May, 2012). Perceived information transparency of the ERP system has significant direct effects on perceived usefulness, ease of use, and indirect effects on attitude and adoption (Ling, Ramayah, Kurnia, & May, 2012). Perceived usefulness fully mediates the relation- ship between information transparency and the attitude toward using the ERP system (Al-Jabri & Roztocki, 2015). The shared belief among ERP end users becomes necessary in an ERP environment when there are cross functional boundaries, and multiple users are involved in the implementation process (Al-Jabri & Roztocki, 2015). A shared sense of belief about the benefit of the new ERP system allows organizational participants to find common grounds and a shared sense of purpose. Shared beliefs in the benefits of resource planning systems were positively related to PEU and PU (Ling et al., 2012). Enhancing end user usage of the implementation of new ERP system involves shared feelings between the employees, peers and line managers.

The UTAUT framework has also been used to study CRM implementation acceptance (Lawson-Body, Lawson-Body, & Willouby, 2017).  The theory of UTAUT helps to explore new or unexpected contextual, organizational, and individual variables in the investigation of the implementation of CRM. The most commonly used predictors of mobile technology adoption, routinely measured as behavioral intention, have been perceived usefulness and perceived ease of use in the TAM model, and performance expectancy and effort expectancy in the UTAUT framework. In understanding the benefits such as the perceived usefulness of mobile SFA, the real value is if barriers to use are too high.  Researchers studied the use of CRM solutions using the constructs of the UTAUT model. The research found there is a significant correlation between performance expectancy, effort expectancy, social and behavioral intention, facilitating the link between the condition and user behavior and the relationship between behavioral intention and user behavior of CRM (Lawson-Body, Lawson-Body, & Willouby, 2017).

Understanding the factors that lead to positive or negative attitudes towards technology is important to help management implement new technology with less attrition (Al-Jabri & Roztocki, 2015). Resistance towards new information technology (IT) may reduce the overall organizational performance because of the discontented users and users’ acceptance, or rejection of IT is not entirely understood. Although many IT investments, such as ERP systems, are sometimes conducted without the involvement of primary users, acceptance of this technology varies substantially among the users (Al-Jabri & Roztocki, 2015). Perceived usefulness and ease of use have a strong significant correlation with the attitude towards using the ERP system, indicating that they form favorable attitudes towards system use and consequently affect the adoption of ERP system. ERP users value and benefit from sharing the information the ERP system provides (Al-Jabri & Roztocki, 2015).  Positive attitudes have formed that lead to system adoption if ERP users view the system as useful.

Effort Expectancy

Effort Expectancy (EE) is the extent to which people believe using technology would be free from effort and not difficult to use (Venkatesh, 2016). This construct is also known as perceived ease of use by Davis (1989).  Effort expectancy will be most salient for women, specifically those who are older and with relatively little experience with the system (Venkatesh et al., 2003). Oye (2012) conducted a study for the acceptance and use of information and communication technology among university academic staff at a school in Nigeria. The researchers found that effort expectancy had a positive influence on the behavioral intention of educators to recognize and utilize communication technology. Venkatesh et al. (2001) identified age and gender as moderating factors of effort expectancy in the UTAUT model. Youth workers were more interested in performance expectancy, as more young people were more attracted to technology, while older women without experience were more interested in effort expectancy (Magsamen-Conrad, Upadhyaya, Joa, & Dowd, 2015). Effort expectancy and facilitating conditions were the only determinants that positively predicted tablet use intentions after controlling for age, gender, and tablet use (Magsamen-Conrad, Upadhyaya, Joa, & Dowd, 2015). Effort expectancy had a positive and significant effect on users’ behavioral intention in a study conducted by Ghalandari (2012). If users feel comfortable using e-banking services, they will be willing to use these services (Ghalandari, 2012).

Performance Expectancy

Performance Expectancy (PE) is the extent to which users believe certain technologies will help better perform a task (Venkatesh, 2016).  Perceived usefulness is another term used for PE in other technology adoption models. Performance expectancy was considered by Venkatesh et al. (2003) as the strongest predictor of behavioral intention and use of information technology.  The study conducted by Wagaw (2017) demonstrates the impact of performance expectancy on respondents’ behavioral intention was significant. The findings further posit performance expectancy, effort expectancy, social influence, competitive advantage, cost effectiveness, and facilitating conditions are determinants of ERP system acceptance. Experience and voluntariness are found to be significant moderators, and Behavioral intention increased when experience moderated it (Wagaw, 2017). The study provides further empirical evidence that users are interested in using ERP systems when systems are easy to use (Wagaw, 2017).  Social influence was significant to the participants’ behavioral intention and applicable only for users with less experience and voluntariness suggesting social influence becomes more important when people have limited              El-Gayar et al. (2011) found that the main acceptance factor for the Tablet PCs is students’ attitude followed by Performance Expectancy, Facilitating Conditions, Effort Expectancy and Social Influence. Oye et al. (2011) found that among the four UTAUT constructs, Performance Expectancy is the most influential factor in the acceptance and use of ICT among teachers. 78% of the respondents believed that the use of ICT in their workplace can increase their opportunity in job promotion (Oye et al., 2011). The researchers posit there is monetary reward or incentive related to the usage of ICT and also a future prospect to get a better job with better salary.

According to Taiwo and Downe’s (2013) meta-analysis of 37 selected empirical studies, the only significant relationship among the four key determinants and behavioral intention (technology adoption) were between performance expectancy and intention.  Similarly, Kaba and Touré (2014) found that performance expectancy positively influenced 1030 social network website users in Africa’s intentions to adopt social networking, but this relationship did not hold when gender and age moderators entered.

There will be higher user’s behavioral intention, if and when the CRM users expect that they need not spent too much time or attention in learning the system (Pa & Tu, 2011). The influence of performance expectancy on behavioral intention. Performance expectancy has shown positive influence on user’s behavioral intention.  A possible reasoning in the system is by using the CRM, a person can only handle issues concerning sales and operations, but the same system dawns no apparent effect regarding performance merit bonus or promotion opportunities and has no influence on performance expectancy (Pa & Tu, 2011). There is an influence of effort expectancy on behavioral intention (Pa & Tu, 2011). In the CRM system, effort expectancy has shown a positive effect on user behavior. If staff members feel CRM is easily learned and operated, their willingness to employ, it will be enhanced (Pa & Tu, 2011). From the empirical study (Pa & Tu, 2011), the empirical data showed that when staff members feel the CRM system is easily learned and operated, there is a sense of the system being able to assist them regarding better job performances.

Performance expectancy emerges as one of the most influential predictors of students’ behavioral intention to use the e-learning systems (El-Masri, & Tarhini, 2017). The findings are in line with previous research and that of the original UTAUT proposition (Venkatesh et al. 2003). This result means that students will mainly adopt the system if they find it useful in their learning process and will enrich their learning experience.  The researchers posit instructors should cultivate and solidify a positive perception regarding the usefulness of the e-learning system on their students to encourage them to use the system (El-Masri, & Tarhini, 2017). The results also showed that effort expectancy has a relatively significant effect on behavioral intention in Qatar and insignificant in USA (El-Masri, & Tarhini, 2017). The results contradict the findings of Venkatesh et al. (2003) and most of the previous research by Al-Gahtani 2016; Cheung and Vogel 2013; Merhi 2015; Park 2009. The research contradictions could be due to the absence of barriers of students to adopt and use the e-learning services due to their familiarity with using the technology in general (Venkatesh et al. 2003; Venkatesh and Zhang 2010). The effect of effort expectancy becomes insignificant for experienced users and a significant factor when users are not familiar with the system (Venkatesh and Zhang 2010). Users who demonstrate higher self-confidence and experience in using technology are more likely to use the system (El-Masri & Tarhini, 2017).

Social Influence

Social Influence (SI) is the strength by which authoritative figures have influenced a person to adopt or use a technology system (Venkatesh, 2016). Social psychology research defines social influence as a change of mind in behaviors, thoughts or feelings from an individual’s perspective as revealed by interaction with another person or a group. It is also known as peer group pressure that is the pressure on a person to conform to a different group resulting in a particular behavior (Eckhardt, Laumer, & Weitzel, 2009).  As a result, people tend to act in conformity with a distinct group as they continuously compare their acting behavior with the behavior of significant others.  Users feel pressured to act in a way that will not make them stand out as lonely and disliked (Eckhardt, Laumer, & Weitzel, 2009). Burns and Stalker integrated social influence in the organizational context into their work on innovation (Eckhardt, Laumer, & Weitzel, 2009). Technology adoption research has struggled to incorporate normative beliefs from sources in the social environment of adopters into adoption models (Eckhardt, Laumer, & Weitzel, 2009).  Social influence on adoption significantly differs about both source (peer groups) and sink (adopters and non-adopters) of the influence (Eckhardt, Laumer, & Weitzel, 2009).  Observing the impact of attitudinal and normative beliefs on users and potential adopters shows that social norms can induce initial adoption while continuing usage based on attitudinal beliefs (Eckhardt, Laumer, & Weitzel, 2009).  The major limitation is the small adequacy of the UTAUT to explain the intention of non-adopters. Unlike the sample of adopters, the constructs effort expectancy and facilitating conditions are not valuable determinants for an individual’s intention not to adopt a specific IS (Eckhardt, Laumer, & Weitzel, 2009).

Observing the impact of attitudinal and normative beliefs on users and potential adopters shows that social norms can induce initial adoption while continuing usage based on attitudinal beliefs (Eckhardt, Laumer, & Weitzel, 2009).  The major limitation is the small adequacy of the UTAUT to explain the intention of non-adopters. Unlike the sample of adopters, the constructs effort expectancy and facilitating conditions are not valuable determinants for an individual’s intention not to adopt a specific IS (Eckhardt, Laumer, &Weitzel, 2009).

Social influence plays a significant role in influencing the behavior of an individual using the relevant technology.   Shazad, Jen, and Yuen (2016) posit social influence does not play a role in UTAUT constructs in voluntary contexts and Social Influence (SI) did not have a significant effect on Behavioral Intention of using e-government services. Normative pressure or social pressure attenuates over time as increasing experience provides a more practical basis for specific intent to use the system (Ling et al., 2012).  When a user senses the new system is easily learned and operated, they will show a more positive attitude of acceptance (Pa & Tu, 2011). The CRM system will be readily accepted by enterprises when someone with power and authority from within the company, actively advocate conducting use of the CRM system (Pa & Tu, 2011). Social influences play a significant role within the context of adoption within team selling, with hypothesized effects stemming from peer usage of technology, competitor usage of technology, and customer influence on the use of technology (Weinstein & Mullins, 2012). Technology usage by other respected salespeople or reps held in high esteem by their peers, signals usefulness and may become normative (Weinstein & Mullins, 2012).  Coworker influence, sometimes resulting from observation can have substantial effects on an individual’s beliefs and behaviors (Weinstein & Mullins, 2012). According to the correlation analysis, SI does not correlate with BI and correlates only with gender. The regression analysis, however, reveals a relationship between BI and SI (Damaskinov, Ketikidis, & Solomon, 2015).

Facilitating Conditions

Facilitating Conditions (FC) is the perceived extent to which users believe which the organizational and technical infrastructure required for the support of the technologies exists (Venkatesh, 2016). Venkatesh, Thong, and Xu (2012) extended the UTAUT theory of acceptance and use of technology and concluded acceptance level of the end users varies across age, gender, and experience. Al-altobi (2016) utilized the major UTAUT constructs and found user adoption moderates three factors: age, gender, and education. Behavioral intentions towards the use of technology predict performance expectancy, effort expectancy, social influence, and technology characteristics.  Gender, age, and education mediate the UTAUT constructs (Al-altobi, 2016). Age and gender were found to be insignificant regarding moderating the behavioral intention to use a government ERP system (Alshehri, Drew& AlGhamdi,2013

Ahearne, Srinivasan, and Weinstein (2004) examined the effect of operational CRM technology usage on salespersons’ performance after they have been using the technology for at least six months. While research frameworks have used for understanding adoption issues or general technology acceptance issues, there has been no research into the performance (Ahearne, Srinivasan, Weinstein, 2004).  User intention and cognition of favorable environment will influence the CRM system used in the work place (Pa & Tu, 2011). Facilitating conditions has a relatively significant influence on behavioral intention towards using e-learning systems (Pa & Tu, 2011). Management should provide all facilities for users including the necessary hardware and software and provide efficient technical support when there are access difficulties, system crashes, and service delays (Pa & Tu, 2011).  This will improve perceptions about using the technology (El-Masri & Tarhini, 2017). If users have access to more resources and assistance than expected in the usage stage, they will experience positive disconfirmation of facilitating conditions(Venkatesh, Thong, Chan, Hu, & Brown, 2011). This assistance leads to higher satisfaction and post-usage facilitating conditions and post-usage attitude and continuance intention (Venkatesh, Thong, Chan, Hu, & Brown, 2011).

Age

Burton-Jones and Hubona (2006) found age is a significant moderating factor between effort expectancy (EE) and usage of the system. Age was not a major moderating factor between performance expectancy (PU) and system use (Ling et al., 2012; Ekanyake, 2014). Older end-users may find it hard to adapt to new system usage, and effort expectancy (EE) becomes an important factor in the impact of system usage. Thus, older workers have lower performance expectancy as they require more effort to learn the new ERP system and do not perceive that use of the system would increase their work performance (Ling et al., 2012). Users who have high PU will use ERP when they believe that there is a positive user-performance relationship

Magsamen, Upadhyaya, Joa, and Dowd (2015) found consistent differences in UTAUT determinants between the oldest and youngest users. These studies revealed that unlike the younger generation, concerns such as the perceived requirements for adopting and using technologies impacted the older generation’s use of information technology to a much greater degree (Alvseike & Brønnick, 2012; Barnard et al., 2013). Attitudes towards technology and its use are the most commonly studied elements of research regarding the relationship between aging and technology adoption (Alvseike & Brønnick, 2012; Barnard et al., 2013). The relationship between age and attitudes towards technology is predominantly negative, meaning that as the age of individuals’ increases, their negative attitudes towards technology increase (Wagner et al., 2010).  Cost is thought to be a major prohibitive factor in adoption or use of digital technology (Wagner et al., 2010). Researchers found older adults are doubtful about the benefits that they will have from technology use, and that lack of perceived benefit outweighs cost as a key factor for less use of technology by older adults (Melenhorst et al., 2006; Wagner et al., 2010).  The findings suggest that perceived easiness or understandability has emerged as one of the major factors predicting the use of technology for older generations (Chen & Chan, 2011).  Zaremohzzabieh, Samah, Omar, Bolong, and Shaffril (2014) found that age moderated the effect of overall UTAUT determinants of ICT adoption in Malaysia, whereas experience only moderated performance expectancy and effort expectancy determinants bearing on intention. Often ERP can be fully installed and implemented, but end users might refuse or be reluctant to use all of its available features (Damaskinov, Ketikidis, & Solomon, 2015). Age was found to be a significant moderator between symbolic adoption and its antecedents (Damaskinov, Ketikidis, & Solomon,2015).

Behavioral Intention and Attitude

According to Amini and Safa- vi (2013), it is the end users’ attitude towards the system that influences their adoption of the software and their successful use of it. Mitra & Mishra (2016) suggested leadership competencies, learning the attitude of the users, and organizational power dynamics can be potential areas of research in the context of ERP implementation and adoption. To influence an individual to use ERP systems, the behavior of the user is crucial because it involves judgment regarding whether the user attitude is good or bad (Shazad, Jen, & Yuen, 2016).  Amoako-Gyampah and Salam (2007) note that behavioral intention is the intention of end-users to make use of new technology and this assertion is supported by Venkatesh (2012) that there is a strong correlation between behavioral intention and actual behavior. End users who have high PU will use ERP when they believe that there is a positive user-performance relationship (Ling Keong, Ramayah, Kurnia, & May Chiun, 2012).

After the stabilization stage, the ERP implementation might be successful, but overall success depends on ERP end-users’ attitudes toward the system and actual use of the system (Chen, 2012). Only when employees are pleased with their direct system interaction can the full potential of the system be exploited. Organizations gain advantage from ERP system only to the extent that users accept and utilize them regularly and comprehensively. The success or failure of ERP system implementation depends directly on the end-users’ behavioral intention to use a better understanding of these factors would enable more efficient organizational interventions that lead to increased acceptance and use of systems.

The complex nature of ERP systems limits the amount of knowledge that users can absorb before actual usage and the higher complexity results in higher mental workload and stress and could negatively affect user’s attitudes towards using the system (Rajan & Baral, 2015). The analysis shows that PE, FC, EE, and SI influence the BI of the employees to adopt the system and further encourages them to use it (Damaskinov, Ketikidis, & Solomon, 2015). It seeks to explain the behavioral intention (BI) to use, which is a reliable indicator predictor for future usage (Oechslein, Fleischmann, & Hess, 2014). Users’ behavioral intention to use various ERP systems increases when a user believes the system will have a competitive advantage. The behavioral intention to use these systems and voluntariness is found to be a significant moderator (Wagaw, 2017).

Summary

This literature review began by outlining the history of technology models used to gauge user adoption and intention to use various technologies. This section was followed by the relevancy of the UTAUT model to ERP, CRM, and CPQ systems. A discussion of why ERP, CRM and CPQ systems are important to an organization’s competitive goals was also analyzed. There are many technology adoption models, but the robustness of UTAUT lends itself to the study of CPQ technology. The literature review discussed the theoretical model to be used in the study, the UTAUT model. Additionally, the negative views of UTAUT were covered. UTAUT is a synthesized model comprising of eight independent models. The model consists of Theory of Reason Action (TRA), Technology Acceptance Model (TAM, TAM-2), Theory of Planned Behavior (TPB), Motivational Model (MM), Combined Theory of Planned Behavior and Technology Acceptance Model (C-TPB-TAM), Model of PC Utilization (MPCU), Innovation Diffusion Theory (IDT), and Social Cognitive Theory (SCT). This section was followed by discussing the core determinants of UTAUT including behavioral intention, performance expectancy, effort expectancy, social influence and facilitating conditions.  The studies related to the UTAUT model were also discussed, and the studies related constructs of the UTAUT were examined.  The existing literature revealed and supported the UTAUT constructs and age as a moderating factor. The extant literature shows there are correlations among effort expectancy, social influence and facilitating conditions measured at different points in time. The next chapter presents a systematic methodology that was used to conduct this study.



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