Expectation and disconfirmation approach

Computers in Human Behavior 75 (2017) 450e460

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Computers in Human Behavior journal homepage: www.elsevier.com/locate/comphumbeh

Assessing consumers' satisfaction and expectations through online opinions: Expectation and disconfirmation approach Atika Qazi a, b, c, *, Alireza Tamjidyamcholo d, Ram Gopal Raj a, Prof. Glenn Hardaker e, Prof. Craig Standing f a

Faculty of Computer Science and Information Technology, University of Malaya, Lembah Pantai, 50603, Kuala Lumpur, Malaysia Centre for LifeLong Learning, University Brunei Darussalam, Gadong, Brunei Darussalam Faculty of Computer Science and Information Technology, COMSATS Institute of Information Technology, Islamabad, Pakistan d Faculty of Computer Science and Information Technology, Islamic Azad University, Tehran, Iran e Centre for LifeLong Learning and Sultan Hassanal Bolkiah Institute of Education, University Brunei Darussalam, Brunei Darussalam f Foundation Professor of Strategic Information Management, School of Business and Law, Edith Cowan University, Perth, Australia b c

a r t i c l e i n f o

a b s t r a c t

Article history: Received 1 December 2016 Received in revised form 17 May 2017 Accepted 17 May 2017 Available online 18 May 2017

The opportunity to capture the opinions of the general public about multiple topics such as social events, marketing campaigns, and product preferences has raised more and more interest in the business world. This paper aims to find the users' level of expectation associated with multiple types of opinion. In addition, the study also aims to find the effect of sentiment words on customer satisfaction at the postpurchase stage. In this work, the positive, negative and neutral sentiment words relating to the users' satisfaction were analysed. Firstly, the questionnaire comprising seven sections was proposed to collect data. The study used the expectancy disconfirmation theory (EDT), and a set of seven hypotheses. Confirmatory factor analysis (CFA) and structural equation modelling (SEM) were used to analyse the data and evaluate the research model. The findings of this study imply that regular, comparative and suggestive opinions have a positive effect in raising users’ expectations. This study also predicted a strong relationship between expectation, performance and disconfirmation. In addition, sentiment words proved to be a full mediator in predicting user satisfaction with a purchased item. © 2017 Elsevier Ltd. All rights reserved.

Keywords: Opinion types Electronic commerce User satisfaction User expectation Online reviews

1. Introduction Opinions are the feedback of users and are key influencers of behaviour. The principle of social validation states that “we seek the advice from others on how to think, feel and behave, particularly when we are in a state of uncertainty” (Cialdini & Goldstein, 2002). Customer feedback on an item (product, service, organization, individual, issue, event, topic and their attributes) influences other customers and traders to make decisions (Cao, Thompson, & Yu, 2013; Chiou, Hsiao, & Su, 2014). Such feedback is collected as user-generated content that has become an important source of information. One can read and write the opinions about an item before buying and after purchasing, which leads to their expectation and satisfaction (Qazi, Raj, Tahir, Waheed et al., 2014).

* Corresponding author. Faculty of Computer Science and Information Technology, University of Malaya, Lembah Pantai, 50603, Kuala Lumpur, Malaysia. E-mail addresses: [email protected] (A. Qazi), [email protected] (A. Tamjidyamcholo), [email protected] (G. Hardaker). http://dx.doi.org/10.1016/j.chb.2017.05.025 0747-5632/© 2017 Elsevier Ltd. All rights reserved.

Recent research predicted that user-generated content and Internet users would increase in the coming years. For example, it has been projected that the number of user-generated content creators in the US would increase to 115 million by 2013, up from 83 million in 2008. Similarly, the number of US Internet users that consume some form of user-generated content will reach 155 million by 2013, up from 116 million in 2008 (Hu, Koh, & Reddy, 2013). Clearly, online reviews are increasingly becoming an important source of information that have a strong and significant impact on consumer decisions to adopt information within online communities (Cheung, Lee, & Rabjohn, 2008). Highlighting the importance of consumers’ opinions, recently, the studies coming up with a detailed literature survey not only in different fields of study (Qazi et al., 2015); but also in the field of sentiment analysis and opinions (Qazi, Raj, Hardaker, & Standing, 2017). These opinions have multiple types that lead to customers’ expectations at the pre-purchase stage while reading. These types are classified as regular (A), comparative (B), and suggestive (C) (Jindal & Liu, 2006b; Qazi, Raj, Tahir, Waheed et al., 2014). These all types

A. Qazi et al. / Computers in Human Behavior 75 (2017) 450e460

of opinions, helpfulness rating and quantitative factors make a review helpful for making a purchase decision (Qazi et al., 2016). The customer writes feedback by using sentiment words as a postpurchase behaviour, which shows their level of satisfaction with their purchase. The consumers express sentiment words, which could be good, bad, or neutral depending on their experience with the purchased item (Huang, 2012). Past researchers have acknowledged the importance of capturing the sentiments expressed in product reviews (Pang & Lee, 2008). The online opinions encapsulating sentiments words have usefulness in diverse areas, such as marketing, education, and health (Archak, Ghose, & Ipeirotis, 2011; Das & Chen, 2007; Eliashberg, Hui, & Zhang, 2007; Jeong, Koo, & Jansen, 2015; Netzer, Feldman, Goldenberg, & Fresko, 2012). The study of opinion mining is used to facilitate the customers in decision-making that make him/her satisfied in the case of successful decisions. It is a common practice that users first read the online opinions, and, once an item has been consumed, each consumer will compare outcomes against expectations to make a judgment regarding his/her satisfaction (Flint, Woodruff, & Gardial, 2002). This indicates that, ultimately, expectation is used to gauge the satisfaction that could be filled or unfilled, as described by perceived performance, and can be judged by the sentiment words shared in each type of review. Rust and Oliver proposed the expectancy-disconfirmation model as a means to measure customer satisfaction based on the difference between the perceived customer expectation and experience in products or services (Rust & Oliver, 1994). The expectancy-disconfirmation theory (EDT) explains that there are two main stages in the purchase decision of customers pre-stage that belong to expectations, and post-stage that covers perceived usefulness and disconfirmation (Oliver, 1977). Soon after the expectancy-disconfirmation paradigm (EDP) emerged as the most frequently cited framework, EDT was applied by many researchers in different fields for a better understanding of the customers’ expectations and requirements concerning their satisfaction, such as marketing, tourism, information technology, repurchase behaviour and retention, and computer mediated environments (Saeed and G. Grunert, 2014; HA Bijmolt, KRE Huizingh, & Krawczyk, 2014; Correia, Kozak, & Ferradeira, 2013). This area has made several contributions to the literature, for example over the last few years; several authors have studied the impact of online reviews on different variables. These variables include sales (Ha & Janda, 2014; Wang, Zheng, & Mao, 2011) trust nez & (Lee, Park, & Han, 2011), consumer purchase intention (Jime Mendoza, 2013), helpfulness (Ghose & Ipeirotis, 2011; Krishnamoorthy, 2015; Mudambi & Schuff, 2010) Qazi et al., 2016 and user behaviour for different review types (Qazi, Raj, Tahir, Waheed, et al., 2014). Also exisitng studies have examined whether suggestive reviews play an important role in enhancing business intelligence (Qazi, Raj, Tahir, Cambria, & Syed, 2014). The one area that has not been widely studied is the impact of multiple types of opinion and sentiment words on customer expectations and satisfaction. This could impact on the high or low expectations of buyers that potentially affects future sales. Thus, it is important for online traders to find the impact of multiple types of online opinion on customers’ expectations. Accordingly, the research first objective is to examine the multiple opinion types (regular, comparative and suggestive) and their impact on users' expectations as a pre-stage buying behaviour. The second objective is to identify the relationship of sentiment words as a mediator between disconfirmation and satisfaction at the poststage users’ buying behaviour. Opinion mining is a growing field of research that has been extensively growing since 2001. Fig. 1 depicts the growing trend of OM and sentiment analysis (SA) studies published from 2001 to 2016. Fig. 1 shows that the field of OM and

451

SA is growing rapidly. The rest of this paper is organized as below: Section 2 explains the background of the study and development of hypotheses. Section 3 defines the research model. Section 4 explains the research methodology. Section 5 explains the results, section 6 provides the discussion and implications, and section 7 concludes the article. 2. Theoretical background and development of hypotheses Two areas of the literature serve as groundwork for this study, the expectancy disconfirmation theory, and opinion types. Oliver's expectancy-disconfirmation approach has been the most influential model among theoretical frameworks. EDT has been proposed to explain consumer satisfaction and was largely applied in the business sector (Weber, 1997).According to expectancy disconfirmation model, satisfaction indicates to an affective state representing an emotional reaction to a product or service. After experiencing the product's actual performance, expectations then serve as a comparative standard for the formation of a satisfaction judgment (Oliver, Rust, & Varki, 1997).It is important to note that the incongruence of expectations can be positive as well as negative. So, a consumer, in regard to any service, can frame a service experience as congruent, positive or negative towards his expectations. The majority of studies have assumed that the EDT is a reliable framework that can be used confidently to determine customer satisfaction. Therefore, we have used this EDT theory to study our proposed model. In light of opinion mining (OM) and EDT, it is observed that expectations are associated with users feedback (Haistead, Hartman, & Schmidt, 1994). It has become a common trend to read and write feedback before and after buying an item. As noted from the related work, the EDT has two notable variables expectation or desire, and experience or perceived performance. These variables are defined in two distinct time periods; expectation or desire is related to the pre-purchase time period, and the perceived performance or experience is related to the after purchase time period. A customer gains experience after perceiving a real performance (Oliver, 1980). Therefore, it is derived that in a different time period, customers behave differently, in that, at the initial stage, customers read the feedback and develop an expectation of an item. After their experience, customers write their feedback according to the perceived performance of being either satisfied/ unsatisfied (Vinodhini & Chandrasekaran, 2014) (Pang & Lee, 2008). This shows the strong relationship of customers’ expectation and perceived performance with online opinions. To identify the customers association with different types of opinion in a distinct time period, we intend to use EDT and propose the following hypotheses. 2.1. Effect of multiple opinion types on users’ expectations In the present research, opinions are defined as users’ feedback that have multiple types (Jindal & Liu, 2006a). The opinions are classified not only because of the different semantic meanings but also because of the different syntactic forms. The opinion types, such as regular (A), comparative (B) and suggestive (C), are differentiated based on language constructs, in which each type expresses a different type of information (Ganapathibhotla & Liu, 2008; Jindal & Liu, 2006b; Qazi, Raj, Tahir, Waheed et al., 2014). The regular opinions are those that pertain to only single entities, which have a further two types the direct and indirect opinions. A direct opinion refers to an opinion expressed directly about an entity or an aspect of the entity, e.g., “The product X is great.” The indirect opinion is an opinion that is expressed indirectly about an entity or an aspect of the entity based on its effect

452


Fig. 1. Year-wise distribution of publications for opinion mining and sentiment analysis studies.

on some other entities, e.g., “The X product is good for a short time but later it gives poor performance.” Both types are clearly providing an overview of item “X” from different aspects (Jindal & Liu, 2006b). A considerable number of opinions are provided through the social media that compare more than one item and their features. This type of opinion is known as a comparative opinion. The comparative opinion expresses a relation of similarities or differences between two or more entities (Jindal & Liu, 2006b). Comparative opinions are related to, but are also different from regular opinions. For example, a comparative sentence is “The quality of X is better than that of Y”. This comparative sentence does not indicate that the quality of X is good or bad, but simply compares it with the quality of Y (Jindal & Liu, 2006b). Due to this difference, comparative opinions gain the attention of consumers who are interested in comparisons. Human communication is a broad, complex and multi-dimensional phenomenon. This dynamic phenomenon generates newer forms and transforms old ones. However, most human communication occurs through human utterances. Suggestives are first time defined as utterances that occur in the written and spoken forms (Aldridge, 1969). Suggestives are common in the interpersonal communication of everyday life. According to linguists, suggestives are speech acts. The speech act theory was provided by John Austin, in 1962, in his published lectures ‘How to do things with Words’ (Searle, 1969). Most of the great ideas for enhancing corporate growth and profits are given by the users in the form of suggestions. Therefore, suggestions are abundantly available in the social media. Suggestives have recently been identified as the third most useful type of opinion that subtly guides readers into making a better choice (Qazi, Raj, Tahir, Cambria et al., 2014). All these types are collectively called opinions and the study that explores opinion is called OM and sentiment analysis (Liu, 2012). With respect to the present study, it is observed that opinions are categorised in different classes, which play a vital role in leading the expectations of the users at the pre-purchase stage. Therefore, it is very important to find the effect of the different types of opinion in

leading users’ expectations. Thus, we have formulated the first hypothesis, as follows: H1. Multiple opinion types at the pre-usage stage have an effect on the expectations of online users.

2.2. Effect of expectations on disconfirmation and perceived performance of the users Expectations are the hopes associated with the performance of a purchased item (Churchill Jr and Surprenant, 1982). The prescriptive relationship between expectation and disconfirmation has either a negative or positive influence. The negative influence is because high expectations are more likely to be negatively disconfirmed, while low expectations are more likely to be positively disconfirmed (Yi, 1990). Experimental research in information systems and marketing generally supports this relationship (Bhattacherjee & Premkumar, 2004; Spreng, MacKenzie, & Olshavsky, 1996). Accordingly, the following hypothesis is suggested: H2. Expectations disconfirmation.

at

the

pre-usage

stage

influence

the

The expectations come from previous experience with the purchased item. The previous experience of a new customer is based on the opinions shared by other customers in the social media. In which the existing customer has initial expectations based on his/her own experience with the purchase item (Haistead et al., 1994). Expectations typically have a positive influence on performance, as the perceptions of performance of customers are formulated based on their expectations (Berger and Webster Jr, 2006; Mauri & Minazzi, 2013).Thus, higher expectations tend to produce higher performance ratings and lower expectations tend to produce lower performance ratings (Yi, 1990). Accordingly, the following hypothesis is presented: H3. Expectations are positively related to perceived performance.


2.3. Effect of perceived performance on the disconfirmation and satisfaction of the users Performance can influence satisfaction both directly and indirectly through disconfirmation. The relationship between performance and disconfirmation is assumed to be positive, which is due to keeping the expectations of higher performance, the more likely it will exceed expectations, which results in positive disconfirmation (Spreng & Page, 2003). This relationship has been shown to have a positive effect on the disconfirmation in the study of Internet based service (Khalifa & Liu, 2003) and other studies (Oliver, 2010; Van Ryzin, 2004). Accordingly, we have proposed the following hypothesis: H4. Users' perceived performance is positively related to disconfirmation. It is anticipated that performance not only affects satisfaction indirectly, but that it is also directly related to satisfaction. This direct relation is related to past research on learning from a system at satisfactory level without having initial expectations (Tse & Wilton, 1988). This direct relationship is also supported by various researchers (Spreng & Page, 2003) (Khalifa & Liu, 2003). Therefore, we have proposed the following hypothesis: H5. Users' perceived performance is directly related to the users' satisfaction

2.4. Effect of disconfirmation on writing sentiment words The disconfirmation is the difference between the initial expectation and perceived performance. When performance is greater than expected, it leads to positive disconfirmation and when performance is less than expected, it leads to negative disconfirmation; however, in some cases, the state is neutral and does not show much difference between the expected and perceived performance (Yi, 1990). In the case of positive disconfirmation, customers tend to write feedback with influential positive sentiment words, such as glad, great, excited. In the case of negative disconfirmation, customers are likely to write feedback using negative sentiment words, such as poor, worst and unhappy. In the case of impartial feedback, the expectation is neither filled nor unfilled e customers express sentiment through neutral words (Liu, 2012; Pang & Lee, 2008). This shows that disconfirmation is posited to have a positive effect on expressing sentiments. Thus, we propose the following hypothesis: H6. Disconfirmation at the post-usage stage is positively related to sentiment words

2.5. Effect of sentiment words in measuring users’ satisfaction The disconfirmation of customers' expectations is expressed through written feedback at the post-purchase stage. Such feedback includes a sentiment word that could be negative, positive or neutral depending upon the customer's experience with the purchased item. All these types of sentiment words (positive, negative or neutral) are equally important for traders in terms of meeting customer satisfaction (Yi, Nasukawa, Bunescu, & Niblack, 2003). To predict customer satisfaction, the sentiment words presented in a set of opinions are examined by various researchers in marketing and information systems (Galariotis, Holmes, Kallinterakis, & Ma, 2013; Kang & Park, 2014; Lee, Kim, & Peng, 2013; Singal, 2012; Yaakub, Li, & Zhang, 2013). Thus, it demonstrates that sentiment words play a vital role in determining customer satisfaction, and

453

the impact of the words is mediated between satisfaction and disconfirmation. Accordingly, we propose the following hypothesis. H7. Users' disconfirmation influences their satisfaction and the impact is mediated through the sentiment words. 3. Research model The research model underlying this study is presented in Fig. 2. The model was based on the aforementioned EDT theory and studies related to users’ opinions. The model illustrates that the types of opinion have a direct effect on expectations. Additionally, consistent with EDT, the model explains the relationship between expectations, perceived performance, disconfirmation, sentiment words and satisfaction. This shows that, directly or indirectly, these factors will have an effect on each other that ultimately influences satisfaction. With respect to this model, seven propositions are examined. Each hypothesis is described using H and a number. In this study, the items used to operationalize the constructs included in the model were mainly adapted from previous studies and modified for use in the context of the opinions of the customers. The four items for each type of opinion are taken from the study by (Qazi, Raj, Tahir, Waheed et al., 2014). Each of the three items for expectation are taken from (Fornell & Larcker, 1981) items relating to perceived performance are obtained from (Davis, Bagozzi, & Warshaw, 1989) disconfirmation is based on the study of (Bhattacherjee, 2001); and satisfaction is adopted from the study of (Spreng et al., 1996). The three scale items for sentiment words is based on the study of sentiment words (Liu, 2012) and (Cambria, Schuller, Xia, & Havasi, 2013). Fig. 3 shows the most read reviews by strongly agreeing participants in order to find better (1) decision making, (2) knowledge gain and (3) future trends. The chart explains that suggestive and comparative reviews are categorized most read reviews by strongly agreed participants. Fig. 4 shows that the most reviews are taken on other than listed domains that are defined in the questionnaire. Where, defined domains, the regular affairs and recreation are most frequent domains for taking the reviews from participants. 4. Research methodology 4.1. Measurements In this study, the questionnaire comprised seven sections: introduction, demographics, online opinions, expectations, perceived performance, disconfirmation, and satisfaction. The questionnaire was prepared by considering the literature (Qazi, Raj, Tahir, Waheed et al., 2014) (Fornell & Larcker, 1981) (Davis et al., 1989) (Bhattacherjee, 2001) (Spreng et al., 1996). The scale of items was measured on a five point Likert scale, ranging from strongly disagree (1) through neutral (3) to strongly agree (5). After this, two information system specialists and four PhD students pretested the questionnaire. The respondents were asked to comment on the construct items with respect to understandability, contextual relevance, and sequence of questions. Furthermore, the pilot test was conducted with fifty members who were experts in digital marketing, and expert opinion discussion forums hosted by LinkedIn. Conducting the pre-test and pilot-test led to a few minor changes in the questionnaire. After incorporating the minor changes, the questionnaire was reviewed one last time by two academic experts and finally, 30 items for 6 variables were selected. Thereafter, the survey was sent online to a large sample to collect data for the research model. Review types are measured by three types of reviews (A) Regular, (B) Comparative and (C) suggestive. Each type of review,

454


Regular(A) H(1)

Comparative (B)

Suggestive(C)

Opinions

H(2)

H(6)

Expectations

H(3)

Disconfirmation

H(4)

Sentiment Words

H(7)

Satisfaction

H(5)

Fist order factor

Perceived Performance

Second order factor

Fig. 2. Research model.

which capture users' affect with (feelings about) reviews. Satisfaction level of users' associated with online reviews is calculated by three items (SA1-3). Three items for sentiment words (SW1-3) are used to take the users’ feedback about writing reviews (good/ bad) according to their experience with reviews. 4.2. Data collection

Fig. 3. Use of multiple opinion types by strongly agreed participants.

The survey was distributed among seven active information systems and opinion mining groups in LinkedIn and the university mail servers. Members included experts at various levels of online information systems, such as digital marketing, tourism, business intelligence, and the hotel industry. First, we applied for membership to the groups. After we managed to obtain membership, the active users of the groups were identified and then a personal email was sent to 1500 users of the groups to obtain responses and improve group participation. The email included a hyperlink to a questionnaire developed by Google form technology, and a brief explanation about the purpose of the study. Our emails were sent from 5 December 2013 to 22 February 2014. Overall, 280 responses were received. After eliminating 11, which were invalid, 269 valid responses were considered for further analysis. Table 1 shows the demographic and characteristic profiles of the participants. All the items were measured on a five-point Likert scale anchored between “strongly disagree” (1) and “strongly agree” (5). A pre-test and a pilot test were conducted prior to performing the final and formal survey in order to validate the research model. 4.3. Data analysis

Fig. 4. Usage of online reviews for participants in different domains.

regular, comparative and suggestive is measured by three items i.e. (RE1-3), (CO1-3) and (SU1-3). These items examine the effect of multiple opinion types on users' expectations. Expectations are measured by three items (EXP 1-3). These expectations examine the users' anticipation of the reviews. The perceived performance is measured by (PP1-3) items. Which examines that how much learning and knowledge is gained by users' by reading the reviews. Disconfirmation of reviews is measured by three items (DCF 1e3),

The suggested model of the study was tested using partial least squares (PLS). Partial least squares is a multivariate analytic technique that is mainly used for path analytic modelling with latent variables (Baron & Kenny, 1986). Contrary to the standard linear regression model, multivariate normality is not necessary in PLS when it performs an assessment of the parameters. In addition, PLS is an appropriate technique for assessing theories in their early formation stages; therefore, causal models can easily and properly be tested by PLS (Davenport & Prusak, 2000), which is true about the present study case. With regard to the second order factors and latent structural modelling, two major errors are less likely to occur when the component-based PLS is used; namely, inadmissible solutions and factor indeterminacy (Haas & Hansen, 2007), which is


455

Table 1 Characteristics of respondents. Measures

Items

Frequency

Percent (%)

Measures

Items

Frequency

Percent (%)

Gender

Male Female

159 110

59.1 40.9

Education

Age

18e24 25e34 35e44 Over 44 Less than 3 4e6 7e9 10 or more Other

89 109 51 20 68 108 23 38 32

33.1 40.5 19.0 7.5 25.3 40.1 8.6 14.1 11.9

Some college Bachelor Master PhD and above

16 79 104 70

5.9 29.4 38.7 26.0

I mostly take the review on

Business Recreation & Hoteling Food & Health Regular affairs Entertainment Other

39 44 36 51 35 64

14.5 16.4 13.4 19.0 13.0 23.8

How often you visit the site to write the review [visit/week]

an advantage of PLS compared with the factor-based covariance fitting approach (e.g., LISREL, EQS, COSAN, and EZPATH). It is also possible to formalize a model for PLS assessment through both reflective and formative constructs (Davis et al., 1989). The suggested model of study was tested by PLS Smart Version 2.M3. The PLS analysis involves two stages: (1) the assessment of the measurement model, including the reliability and discriminant validity of the measures; and, (2) the assessment of the structural model, which consists of path coefficients and R2 values. 5. Results 5.1. Measurement model Individual item loadings and internal consistency were examined as a test of reliability. Individual item loadings greater than 0.7 are considered to be adequate (Chin & Newsted, 1999; Tamjidyamcholo, Bin Baba, Tamjid, & Gholipour, 2013). As shown in Table 2, the loadings for all measurement items are above 0.7. This demonstrates that there is sound internal reliability. In addition, internal consistency was assessed through Cronbach's alpha. As shown in Appendix A, the Cronbach's alpha for all constructs is Table 2 Confirmatory factor analysis result. Measures

Items

CR

AVE

Loading

S.E.

t-value

Regular (A)

RE 1 RE 2 RE 3 RE4 CO 1 CO 2 CO 3 CO4 SU 1 SU 2 SU 3 SU 4 EXP 1 EXP 2 EXP 3 P P1 PP 2 PP 3 DCF 1 DCF 2 DCF 3 SW 1 SW 2 SW 3 SA1 SA 2 SA 3

0.763

0.454

0.818

0.532

0.876

0.643

0.873

0.696

0.886

0.722

0.889

0.727

0.583 0.794 0.759 0.516 0.68 0.832 0.769 0.618 0.827 0.877 0.878 0.59 0.834 0.872 0.843 0.854 0.893 0.81 0.789 0.85 0.863 0.84 0.772 0.696 0.792 0.822 0.828

0.067 0.014 0.024 0.05 0.048 0.019 0.029 0.074 0.009 0.016 0.017 0.036 0.01 0.03 0.008 0.011 0.011 0.048 0.022 0.017 0.008 0.008 0.057 0.051 0.021 0.015 0.013

8.593 54.053 31.652 10.151 14.02 42.797 26.721 8.341 88.444 53.500 51.766 16.486 82.39 28.636 99.09 77.379 80.59 16.787 36.167 48.673 107.59 98.394 13.546 13.536 37.554 55.831 61.354

Comparative (B)

Suggestive (C)

Expectations

Perceived performance

Disconfirmation

Sentiment Words

Satisfaction

0.814

0.595

0.855

0.663

greater than 0.6, which is considered to be adequate (Fornell & Larcker, 1981; Tamjidyamcholo & Sapiyan Bin Baba, 2014). Multiple approaches can be applied to estimate second-order factors (Chin, Marcolin, & Newsted, 2003). The repeated indicator approach, also known as the hierarchical component model, is the €ller, most applied approach to assess second-order factors (Lohmo 1989). A second-order factor is directly measured by using the items of all its lower-order factors. The second most used approach is to formulize a model for the pathways between the lower-order and higher-order factors (Edwards, 2001). It is possible to utilize this approach in calculating second-order factors in PLS through implementing numerous first-order factors. The latter approach was applied to generate the second-order variable (opinions) in the present study. Composite reliability (CR) and average variance extracted (AVE) were measured for estimating convergent validity. According to PLS analysis, the lowest recommended level of reliability is 0.7 (Hair, Anderson, & Tatham, 1987), and the lowest desirable level of AVE is 0.5 (Fornell & Larcker, 1981). In our study, the range of composite reliabilities was 0.763e0.889, which exceeded the threshold values for acceptable convergent validity. For all constructs, except the regular construct, the AVE exceeded the recommended value of 0.50 (0.532e0.727). Although there is a lower score for AVE in the regular construct (0.454), the composite construct reliability score indicated acceptable construct reliability. In addition, discriminant validity is measured using AVE. The square root of AVE should be greater than the correlations among the constructs. In other words, the extent of variance that exists in both a latent variable and its body of indicators must exceed the shared variance between the latent variables. The intercorrelation of constructs and variance that exist in both latent variables and their indicators are depicted in Table 3. The square root of the AVE is represented by the diagonal elements in Table 3. This indicates that the square root of each AVE value is bigger than the off-diagonal elements; thus, inferring that there is an acceptable and logical extent of discriminant validity in the assessment model with regard to all the constructs. In addition, the outcomes of the measurement analysis demonstrate that the degree of discriminant validity in all constructs and measures is reasonable and adequate. 5.2. The structural model Since we achieved convincing results from the reliability and validity tests in the previous sections, we proceeded to testing our proposed hypotheses. In this section, we assess our proposed model through structural equation modelling (SEM) to examine our hypotheses. The test of the structural equation model includes an estimation of the path coefficients and R2 values. The path coefficients indicate the strengths of the relationships between the

456


Table 3 Correlation between research constructs.

Regular (RE) Comparative (CO) Suggestive (SU) Expectations (EXP) Perceived performance (PP) Disconfirmation (DCF) Sentiment Words (SW) Satisfaction (SA)

RE

CO

SU

EXP

PP

DCF

SW

SA

0.673 0.551 0.325 0.257 0.301 0.244 0.186 0.323

0.729 0.488 0.345 0.405 0.296 0.273 0.355

0.802 0.154 0.306 0.243 0.159 0.326

0.834 0.529 0.443 0.269 0.341

0.85 0.551 0.334 0.448

0.853 0.341 0.553

0.771 0.32

0.814

Note: The bold numbers in the diagonal row are square roots of the average variance extracted.

endogenous and independent variables, and the R2 values represent the amount of variance explained by the independent variables. The results of hypothesis testing using PLS are summarized in Table 4. In Fig. 5, the R2 values, which reflect the predictive power of the model, are depicted within the rectangle of each endogenous variable. The model explains 0.1 of the variance in expectations, 0.335 in disconfirmation, 0.28 in perceived performance, 0.116 in sentiment words, and 0.233 in satisfaction. Furthermore, Fig. 2 show the results of the path coefficients. The path coefficient from opinions to expectations is positive and statistically significant (b ¼ 0.315, p < 0.01). This implies that expectations are effectively influenced by opinions (regular, comparative and suggestive); thus verifying Hypothesis 1. The results also show that expectations significantly and meaningfully affect disconfirmation (b ¼ 0.211, p < 0.01) and perceived performance (b ¼ 0.280, p < 0.01), which supports Hypotheses 2 and 3. The results illustrate that perceived performance has a significant effect on disconfirmation (b ¼ 0.440, p < 0.01) and satisfaction (b ¼ 0.384, p < 0.01); thus, verifying hypotheses 4 and 5. The path coefficients indicate the strengths of the relationships between the disconfirmation and sentiment words (b ¼ 0.341, p < 0.01). Therefore, Hypothesis 6 is validated. The results show that sentiment words significantly affect satisfaction (b ¼ 0.192, p < 0.01), which supports Hypothesis 7. 6. Discussion and implications 6.1. Discussion The main objective of the present study was to identify and understand the effect of multiple types of opinion and sentiment words on users' expectations and satisfaction. An empirical study was conducted to test the theoretical model. In particular, seven out of seven hypotheses are confirmed. In line with the previous research indicating the importance of online reviews in consumers' purchase decisions, our results show that opinion types (such as regular, comparative, or suggestive) are key to raise consumer's expectations (Chen & Xie, 2008; Dellarocas, 2006). It is also observed that comparative opinions are more influential than regular and suggestive opinions. According to the results of the analysis in forming opinions of individuals, the comparative constructs show the strongest result (b ¼ 0.453, t ¼ 28.558). The findings of this study imply that the opinions forming the regular and

suggestive constructs have similar effects on individuals. Therefore, it is considered that opinion type C and type A can be influential in making better decisions when consumers are interested in finding their own experience instead of seeking comparisons and needing suggestions. This is supported by previous studies that suggest that when people have no clear expectations of performance, it might be practical for them to assume that all possible feedback is equally likely (Fox & Rottenstreich, 2003). The results of data analysis affirm that at the pre-purchase phase, users' expectations are positively related to perceived usefulness as well as influence the disconfirmation. This is consistent with other studies that found expectations to be an important predictor of disconfirmation and perceived performance (Spreng et al., 1996; Susarla, Barua, & Whinston, 2003). The perceived performance was predicted to directly and indirectly influence customer satisfaction. Our findings provide significant support for this direct and indirect relationship. For the indirect relationship, the perceived experience of users with the purchased item may or may not meet the expectations that ultimately influence disconfirmation. This result is supported by the previous findings of blog postings and the case of casinos (Wong & Dioko, 2013; Zehrer, Crotts, & Magnini, 2011). The direct relationship of perceived performance and satisfaction is supported by the students’ perceived usefulness with e-text and satisfaction (Stone & Baker-Eveleth, 2013). Moreover, the results of this research indicate that at the postpurchase stage, sentiment words (positive, negative and neutral) significantly mediate between disconfirmation and satisfaction. Therefore, it can be derived that three kinds of sentiment words i.e. positive, negative and neutral are the consequence of the users' perceived experience with a purchase. This is interesting because it adds to the expectancy disconfirmation theory. Accordingly, our results are supported by marketing research, in that customer satisfaction through positive, negative and neutral experience is judged (Wong & Dioko, 2013). The above findings reveal that the consumers' opinions can be analysed from different aspects e.g. sentiment words, which show's different states of consumers' moods. In summary, sentiment analysis plays a vital role to determine a preliminary assessment of how and to what extent some event may be heading towards a crisis, risk, profit, loss, etc. and may help in averting the situation. The crisis could take any form such as a wrong decision in business that may lead to a loss. For example in the EHEC (Escherichia coli bacteria outbreak in Europe) outbreak

Table 4 Results of hypothesis testing. Hypotheses Hypothesis Hypothesis Hypothesis Hypothesis Hypothesis Hypothesis Hypothesis

Results 1. 2. 3. 4. 5. 6. 7.

Multiple opinion types at the pre-usage stage are associated with expectations of online users. Expectations at the pre-usage stage are associated with disconfirmation. Expectations are positively associated with perceived performance. Users' perceived performance is positively associated with disconfirmation. Users' perceived performance is directly associated with the users' satisfaction. Disconfirmation at the post-usage stage is positively associated with sentiment words. Users' disconfirmation is associated with their satisfaction and the impact is mediated through the sentiment words.

Supported Supported Supported Supported Supported Supported Supported

(t=19.042)

Regular(A)

0.336*** (t=28.558) 0.453***

Comparative (B)

Suggestive(C)

Opinions R2=1

(t=9.006) 0.315*** H(1)

Expectations

(t=3.436) 0.211***

R2=0.1

H(2)

(t=6.431) 0.341*** Disconfirmation R2=0.335 H(6)

Sentiment Words 2

(t=8.712) 0.192*** H(7)

R =0.116

Satisfaction R2=0.233

(t=17.331) 0.460***

H(3) (t=32.988) 0.529***

H(4)

(t=7.176) 0.440***

H(5) (t=7.425) 0.384***

Perceived Performance 2

Significant path

R =0.280

*p