Name of the university
City and state where it is located
2Background of the problem
3Objectives of the study
4Significance of the study
5Literature Review and Hypothesis development
7Technology Acceptance Theory (TAM)
9The Analytical Hierarchical Process (AHP)
User Acceptance of Social Media Marketing Technology: A Case of Insurance Sales Executives
Background of the problem
Information communication technology (ICT) has become a useful tool in the marketing industry. Talukder & Yeow (2007) noted that ICT facilitates efficient and effective transfer of institutionalized information from the centre. In response to the increasingly competitive market, companies are constantly changing their business models, and they are upgrading their IT systems to be consistent with emerging market trends. However, the full use of ICT can only be realized if the users accept to adopt the technology. Resistance to new technology has been observed in some organizations (Martinko, Henry & Zmud, 1996). This study will examine predictors of acceptance of social media marketing (SMM) by insurance sales representatives.
Internet has been billed as the most radical development in ICT. The business and corporate world alike have embraced and invested on internet as an integral part of their operations. One major component of internet is social media (SM) which has drastically revolutionized interpersonal communication. Because it is capable of connecting millions if not billions of online users in real time, the social media is fast becoming the marketing tool of choice for businesses (Coccia, 1997). Some of the social media platforms include Twitter, Facebook, Linkedln, Myspace, user blogs, and emails. Estimates by Laudon and Traver (2011) put active and regular users of Facebook at about 1 billion making it an important tool for peaking business performance.
Research on social media marketing (SMM) in the insurance industry has mainly focused on corporate communication and public relations (Bernoff and Li, 2008), the use of SM in work-related activities (Leftheriotis and Giannakos, 2013), and underwriting (Chang and Kao, 2014). It has been established that social networks offer employees effective means for interacting with colleagues and customers (DiMicco et al., 2009). Nevertheless, there is paucity of studies in the domain of SMM of insurance policies. To fill this gap, the proposed research will study factors that predict willingness and adoption of SMM by insurance sales executives.
Objectives of the study
The study intends to examine predictors of acceptance of SM systems by sale representatives in the insurance industry. The specific objectives are stated as follows.
To establish the trust levels for social media among insurance sales executives
To examine the influence of technology-related factors on the acceptance of social media marketing by insurance sales executives
To determine the effect of social (non-technology related) factors on acceptance of social media marketing by insurance sales executives
To compare the predictors of social media marketing acceptance by insurance sales executives
To what extent do insurance sales executives trust social media?
How do technology-related factors influence insurance sales executives’ acceptance of social media marketing?
What is the effect of social factors on acceptance of social media marketing by insurance sales executives?
How do the predictors of social media marketing acceptance by insurance sales executives compare with one another?
Significance of the study
It is an established fact that SMM is one of the most effective ways of reaching customers (DiMicco et al., 2009). Online social networks (OSNs) offer a great potential for businesses and customers (Kevin, 2009). Research has found that OSNs are effective in e-Commerce especially in advertising and transacting business (Zeng, 2008). Using SMM, therefore not only saves costs (for instance of travel and phone calls) drastically, but it also attracts volumes of customers.
Kwon and Zmud (1987) outlines the barriers to optimal use of IT systems in institutions in the developed world: lack of top managerial support, low quality Information Systems design, inadequacy of motivated and capable users. This study will address the last barrier by isolating factors that determine IT acceptance in the marketing and insurance industries. Similar barriers exist in the developing countries, but they are compounded by weak nation IT infrastructure, policies that curtail technology transfer and limited technology diffusion (Goodman and Green, 1992). The proposed study is a step towards technological diffusion among SM users.
By studying sales executive’s attitude towards SMM and their willingness to use the proposed study will provide useful empirical data for optimizing benefits accruing from this form of e-Commerce. Human resource and marketing managers will hence utilize the findings of this study to build capacity for their employees in this domain. User acceptance models are also important to mangers since they assist them to understand their employees’ attitudes, and strategies on staff training and motivation.
Literature Review and Hypothesis development
Since related literature from previous studies forms the basis of the research hypotheses, this section reviews studies in technology acceptance in general and SMM in particular. Literature from relevant empirical studies will precede discussions of theoretical constructs that are applicable to this study.
Literature on technology acceptance (TA) addresses different aspects of the phenomenon as well as its application in a wide range of industries. User trust of the technology is one of the factors that characterize TA (Tang & Liu, 2015). Applied to IT, trust refers to the belief that data content constructed and relayed online should meet both the sender’s and the receiver’s threshold of confidence (Pavlou & Gefen, 2004). Three concepts construct the multidimensional trust model in online contexts: dispositional, interpersonal and institutional trust (Tan & Sutherland, 2004). Dispositional trust applies to personal traits which include expectancy, belief, and feeling accumulated from personal experiences of using a system. Interpersonal trust is defined by the level of confidence between individuals sharing online information while institutional trust is confidence that is built by groups in the course of their interaction. Factors that enhance trust in a user of an IT system include the user’s perception of the system to deliver expected results, accuracy of the content conveyed, and conformance between what is said about the system and what it can actually do (Koufaris & Hampton-Sosa, 2004). The proposed study identifies perception of a system’s trustworthiness as a major predictor of user acceptance of SMM as a system. This leads to the first study hypothesis:
H1: User trust of the social media significantly influences acceptance of the system as a marketing tool. The study will evaluate the level of trust that the sale representatives place on SM and asses how it affects their willingness to adopt it for marketing. In essence, the study seeks to establish the relationship between user trust and TA.
Tswan-Hwan Lin and I-Chong Lin (2014) found that PEU and PU are direct predictors of accepting new technology but their influence of technology adoption is indirect. This research was conducted in logistics and supply chain industry. This study reveals a very essential aspect for the proposed study. It establishes the triad relationship that could benefit from a vibrant technology: the seller, the buyer, and the third party. The authors argue that information technology (IT) plays a key role in linking these parties. They point out that with IT and related technology, service quality, efficiency, and flexibility are optimized. Another advantage of using IT in the industry is to cut costs. Furthermore, IT is useful in synchronizing and coordinating a firm’s operations. Similar findings were found by Phan and Daim (2011) among users of mobile phones. PU and PEU ranked higher than other factors that influence users to accept and adopt mobile phone data services. The proposed study will examine how PEU and PU affect acceptance of IT, but unlike the two previous studies, it will focus on the use of SM technology for marketing insurance policy. The second study hypothesis is derived from these findings:
H2: Acceptance of SMM depends on technology-related perceptions – PU and PEU. The study makes a distinction between user perceptions that are directly related to the technology and user perceptions that are non-technology related as described in the third hypothesis.
In another study, Guo, Shim, and Otondo (2010) considered the influence of three social network determinants on technology acceptance. These were the tie strength, centrality and density. This study examined how technology acceptance by an individual is influenced by others rather than the technology per se. The study hence described social characteristics that predict other people’s influence on a user’s acceptance of technology. Tie strength refers to the closeness between individuals created by time, emotional intensity, intimacy and reciprocal interactions (Granovetter, 1973). As such, the stronger the social ties, the higher the influence –the reverse also applies. The centrality characteristic refers to the number of direct ties or connections an individual has with others; the more the ties, the higher the centrality of the individual within that network, and the greater the influence (Scott, 2000). The third characteristic, density, defines the degree of connectedness of individuals within a network, and the denser the ties, the higher the conformity, solidarity, cooperation and degree of trust (Sparrowe, Liden, Wayne & Kraimer, 2001). The proposed study adopts social network as one of the predictors of acceptability of new ICT by employees in the marketing industry and the findings from this study provide useful insights into this factor. It forms the basis for the third hypothesis:
H3: Acceptance of SMM is dependent upon social (non-technology related) factors – tie strength, tie centrality, and tie density. These factors are called non-technology related since they have more to do with significant others than with the user’s interaction with the technology.
The study selects as the theoretical framework two technology acceptance models: TAM (Davis, 1989) and TAM2 (Venkatesh and Davis, 2000). Both models posit that two factors predict an individual’s intention to use technology namely perceived ease of use (PEU) and perceived usefulness (PU). Furthermore, TAM2 has incorporated social influence process and instrumental processes which are important for this study. This study finds the models appropriate since they were primarily designed for explaining and determining user acceptability of computer and related systems. While TAM fits the first two objectives of the study, TAM2 is appropriate for researching on the third objective. It has been noted that TAM2’s complex theoretical constructs can accommodate varied factors and predictors (Bagozzi, 2007). The two versions of TAM and related theories are discussed in the subsequent sections of this proposal. The fourth objective of the study will be based on the Analytical Hierarchical Process (AHP) developed by Thomas Saaty (2008).
Technology Acceptance Theory (TAM)
TAM, Theory of Reasoned Action (TRA), and, Theory of Planned Behavior (TPB) are closely intertwined. According to TRB, the performance of an individual is dependent upon the individual’s behavioral intentions which are in turn influenced by both the individual’s attitude and subjective norms (Fishbein and Ajzen,1975). TRA is a model extended from TPB which posits that behavior is determined by perceived behavioral control (Shih, 2004). Most researchers use TRA and TRB models to predict behavior based on three main constructs: attitude, belief, and intention. Davis (1989) built upon the TRA model in constructing TAM for predicting users’ acceptance and adoption of a system or technology. He postulates that a user’s acceptance to adopt a new system is significantly influenced by the user’s intention to use the system which is also determined on the user’s attitude towards the system.
As shown in Fig.1, TAM comprises three distinct criteria namely external variables (EV), attitude towards using and actual use of a new system which are intermediated by two moderating criteria: PU and PEU. This means that these two intervening criteria are significant in defining a user’s intention to use new technology and his or her attitude towards using the new system. Davis’ original TAM was later reconstructed by Davis and Venkatesh (1996) in a model (TAM2) that removed attitude and redefined EV as an external stimulus, PU and PEU as cognitive responses, and actual use as the behavior. TAMs hypothesize that PEU is a critical predictor of PU which in turn positively but indirectly affects user’s self-efficacy in computer use.
Fig 1. Technology Acceptance Model (Davis, Bagozzi and Warshaw, 1989)
The relationship between the elements in the model can be explained briefly. External variables are factors that are extraneous to the new technology but they facilitate its acceptability. Perceived usefulness comprises the user’s perception of the technology’s utilitarian value. PU refers to an individual’s belief that by using a particular system, his or her action will be better off (Venkatesh and Davis, 2000). The user would consider aspects of the technology such as cost, time, content, mobility, and enjoyment to form the system’s PU. The perceived ease of use means that a user believes that the technology will be simple and friendly to work with (Venkatesh et al., 2003). PEU is determined by the simplicity, speed and quality service associated with the technology. Attitude towards using a system is the measure of desirability of a user of the system in using it (Malhotra & Galletta, 1999). Behavioral intention is the user’s preparedness to use the system and it is predicted by PU and the attitude towards use.
Researchers have widely used TAMs in a number of IT studies. These include e-shopping (Shih, 2004), internet utilization behavior (Shih, 2001), internet banking (Sah and Han, 2002). The models have played a notable role in improving knowledge on acceptance, resistance or rejection of new technology. They also provide insight into system, adoption and diffusion among users. The proposed study utilizes the TAMs to explain and establish the moderating factors between users’ attitude towards using social media marketing and their actual of the same. Although the study has expanded the model to accommodate other factors absent in the prototype model (see Fig.2), the basic theoretical framework remains intact.
The Analytical Hierarchical Process (AHP)
AHP will be used for comparing the predictors as well as the social media systems with regard to user acceptability of the systems. AHP is a structured logical framework that is applied to establish weight of criteria under study. The framework categorizes multiple criteria and arranges hierarchically so that they can be easily evaluated (Saaty, 2008; Liu , Chuang, Huang and Tsai, 2010). One of the steps in AHP relevant to this study is the pairwise comparison (PC). The method involves ascribing relative importance or priority weights for every criterion in the hierarchy. It does this by comparing two items each time in order to determine the relationship between them and to rank one in relation to another. Comparison of criteria would mean that no single criterion would be dominant in a straightforward manner.
For the purposes of this study, three acceptance criteria emerging from the reviewed literature will be compared using PC: Trust, Technology-related factors (Ease of use, and Usefulness) and Non-technology related factors (Social factors). Similarly, each of these variables will be compared against each Social Media Technology (Twitter, Instagram, Email, and other media), This model informs the construction of the fourth study hypothesis:
The influence of trust, technology-related, and non-technology related factors on SMM acceptance are comparatively different. Through this assumption the study seeks to compare relative weights of each factor with one another and with each SM technology listed in the paragraph above.
The study model expands TAM to include other factors drawn from literature review and insight from the AHP.
Fig2. The conceptual framework for employees’ acceptance of social media technology in marketing
The proposed study is basically an industry analysis focusing on the insurance market industry as well as the social media technology. A quantitative survey design will be utilized to collect data from the participants so as to describe and compare the predictors of acceptance of social media technology. The study population will comprise sales executives in the insurance industry. They will be drawn from local insurance firms and randomly sampled to obtain to be included the sample size appropriate for the study. Towards this end, questionnaires will be issued to the participants. Questionnaires are effective in measuring the participants’ preferences when the variables under comparison have been identified (Phan and Daim, 2011). The study’s independent variable is acceptance of SMM by insurance executives while the three dependent variables are the user perception of social media trustworthiness, technology-related factors and non-technology related factors. All these factors will be items in the questionnaire.
The analysis of data from the questionnaire will be done by comparing each factor as well as each social media platform with others. The method is called Pairwise Comparison (Helper & Mazur, 2007). The study will use the PCM (Pairwise Comparison Matrices) software to calculate means for each of the three variables using data from the questionnaires. From the software, each predictor will be compared with the other two against each SM platform and relative weights computed. Weight means, dispersion, and standard deviation will be generated by the software. The analyses will be presented in screenshots, tables, charts and graphs. The effect of the factors in influencing acceptance of a system or technology is considered significant if value of and inconsistency lies between 0.0 and 0.10 (Kocaoglu, 1983).
Validity of study results will be achieved by triangulation, which involves using multiple data analysis procedures (McMillan & Schumacher, 2006). Reliability will be achieved by logic strategy that involves seeking data from as many participants as possible to the point of data saturation. Other measures to be used for realizing reliability are code-recode strategy and peer review (Merriam, 1998). The measures taken to achieve validity and reliability of the study process, and tools will significantly ensure trustworthiness of its findings.
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