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Is Customer Satisfaction Really a Catch-All? The Discrepancy between Financial Performance and Survey Results.

Marketing as a discipline traditionally places customer satisfaction as a focal theme, thereby encouraging considerable amounts of marketing research (Churchill and Surprenant, 1982). Satisfaction is indeed a core marketing concept and, in many cases, retail marketing managers and academicians alike treat the concept as a catch-all term that captures the entirety of consumer results from consumption (Dixon et al., 2010). The expectancy-disconfirmation model provides marketers with a deep understanding of how expectations align with current performance outcomes to arrive at a level of satisfaction (Oliver, 1980; Ganesh et al, 2000). Satisfaction research covers topics including the "gaps" model (Zeithaml et al., 1993), satisfied switchers (Maxharn and Netemeyer, 2002), and an index termed the American Customer Satisfaction Index (theacsi.org), which remains a measuring stick for performance for many companies worldwide, including many retailers (Fornell, 1992).

Despite the richness of the satisfaction concept, researchers find evidence that merely satisfying the retail customer might not be enough to secure strong performance (Blankson et al, 2017; Balabanis et al, 2006; Dahlsten, 2003). Evidence suggests that all too often companies try to retrofit current practice to fit an outdated customer demand model (Dahlsten, 2003), while other evidence suggests the relationship between satisfaction and loyalty is nonlinear (Balabanis et al., 2006). In fact, Volvo Motor Company surprisingly discovered a negative relationship between their customers' reported satisfaction levels and loyalty to Volvo, suggesting satisfaction may not be as vital to success as once thought (Dahlsten, 2003), or at the least, the evidence suggests that satisfaction is insufficient to drive overall performance.

So, what more can there be? Value is emerging as paramount in importance to the marketing community. Marketing authors such as Holbrook (1994), Woodruff (1997), Zeithaml (1988), Woodall (2003), and Babin et al (1994) emphasize value as the ultimate outcome from any consumption experience. The emerging value-dominance theory defines value as an outcome placed at the same level of satisfaction rather than a predictor of satisfaction. Vargo and Lusch (2004) present service-dominant logic paradigm (SDL) that positions value-in-use as a focal marketing concept that requires more attention as the key outcome variable resulting from service. SDL prescribes moving beyond the transaction point as the climax of marketing efforts towards understanding how customers receive value from firm resources. SDL positions value as the result of a company doing something for the benefit of another, in this case, the customer (Vargo and Lusch, 2004). This research compares different value perspectives with satisfaction in a retail context. The overarching research question this work attempts to answer is what drives consumer loyalty and firm performance in a retail setting, and are both driven by the same antecedents? Does customer value or customer satisfaction matter more in retailing?

RESEARCH QUESTION

The overarching research question investigates the roles that value and satisfaction play in creating customer loyalty and positive financial performance. The implications may assist retail managers in closely aligning resources with desired outcomes. In other words, what return comes from an investment in satisfaction vis-a-vis value? Loyalty and both earnings per share (EPS) and return on assets (ROA) serve as major organizational success metrics; however the antecedents to a survey outcome like loyalty and market outcomes like EPS and ROA may differ. Considering excessive outside debt can lead to lower profitability (Titman and Wessels, 1988; Rajan and Zingales, 1995), retail managers investing in resources, be it satisfaction or value, will benefit from understanding if the investment will provide a return in the form of either increased loyalty, better financial performance, both, or neither.

OVERALL VALUE

Zeithaml (1988) suggests four interpretations of value from her research identifying consumer means-end chains from a qualitative laddering approach. (1) Value as low price, where consumers use value and price interchangeably. (2) What I want, where consumers place high value on things with proportionately more benefits. (3) Quality for price, where consumers place high weight on the price-quality heuristic. (4) A get-for-give perspective, where consumers weigh off all that is received versus all that must be sacrificed in consuming a product. The fourth view presents the most encompassing interpretation of value. In it, value received is derived through a deeply personal, and thus subjective, process involving tradeoffs between resources received and resources relinquished.

Babin and Harris (2018) and Babin and James (2010) present the view of value as all the customer gets minus all the customer gives. In this get versus give tradeoff, the greater the customer involvement with a project, the greater the potential that the customer will derive more from the get component, all things being equal. If a customer enjoys a task and thus feels prestige or nostalgia from the purchase or use of a product, the customer then is deriving added value from the task. Thus, the customer receiving emotional benefits can enhance the get components beyond simple ease-of-use features.

This research explores the theoretical notion of overall customer value as a get versus give trade-off from the customer's perspective. The logic follows from Thaler (1985) and from the theoretical consumer value framework (CVF) in Babin and Harris (2018). Overall value includes a tradeoff where the customer weighs the net benefits received to everything perceived to have been given up (or invested) to receive the benefits. The theme is to compare the above view of value with other marketing conceptualizations of value and with satisfaction to determine how each affects outcome measures. In so doing, the relationship between overall value and both financial results (EPS and ROA) and loyalty is captured to determine if overall value affects survey results and financial performance equally. Earnings per share (EPS) and Return on assets (ROA) were chosen as the two dependent variables along with loyalty. EPS is the portion of a company's profit allocated to each outstanding share of common stock and is often considered an indicator of firm profitability and ability to generate sustainable internal funding. EPS is derived from net income after considering shares outstanding. ROA is seen as a profit indictor regarding management effectiveness in use of assets to generate earnings, or similarly stated, the profit per dollar of assets (Ross et al, 1999).

Firms that deliver the most value should create incentives for customer loyalty that ultimately realize themselves in relatively high returns, based on the relative cost of customer retention versus continued customer acquisition. The overriding set of relationships is below in HI and is subsequently derived into individual testable relationships.

HI: Overall value relates positively to loyalty, EPS, and ROA. Therefore, H1a predicts overall value relates positively to loyalty. H1b predicts overall value relates positively to EPS. Lastly, H1c predicts overall value relates positively to ROA.

VALUE AS HEDONIC AND UTILITARIAN

Value is not merely a financial price (Carr and Ring, 2017). Babin et al. (1994) take value derived from a shopping experience and break it into two components: hedonic and utilitarian value. The personal shopping value scale (PSV) consists of utilitarian and hedonic value and was developed specifically for retail research. Utilitarian value represents the ability to complete efficiently the shopping task while hedonic value is the extent to which the customer enjoys and extracts value from the experience itself even aside from any purchase the consumer may make. A hedonically rewarding experience generates positive emotions or feelings independent of any gratification from product acquisition or task completion.

The (PSV) has seen wide interest and study within retailing. Examples in retailing include explaining customer share via positive and negative emotions and image transfer (Babin and Attaway, 2000; El Hedhli et al., 2017), explaining online shopping and hedonism online (Mazaheri et al., 2010), explaining patient and provider expectation congruency (Camp et al., 2017), and explaining tourists' behavior (Duman and Mattila, 2005). International retailing applications have recently been undertaken and support hedonic value relating to survey items such as satisfaction and behavioral intentions (Atulkar and Kesari, 2017). Still other authors have related value to behavioral loyalty including both number of retail store visits and sales (Mencarelli and Lombart, 2017). In a review of the uses and applicability of hedonic and utilitarian value, researchers state the concept has been studied in thousands of applications (Babin and James, 2010). However, few studies have successfully studied value's effect on loyalty and financial results simultaneously to determine if loyalty and financial results have the same predictors. Accordingly, the relationships between hedonic and utilitarian value and loyalty and two performance metrics (EPS and ROA) deserve attention. The overriding set of relationships is below in H2 and 113 and is subsequently derived into individual testable relationships.

H2: Hedonic value relates positively to loyalty, EPS, and ROA. Therefore, H2a predicts hedonic value relates positively to loyalty. H2b predicts hedonic value relates positively to EPS. Lastly, H2c predicts hedonic value relates positively to ROA.

H3: Utilitarian value relates positively to loyalty, EPS, and ROA. Therefore, H3a predicts utilitarian value relates positively to loyalty. H3b predicts utilitarian value relates positively to EPS. Lastly, H3c predicts utilitarian value relates positively to ROA.

CUSTOMER SATISFACTION

Fornell (1992) developed the America Customer Satisfaction Index (ACSI), which is a "weighted composite index based on annual survey data from customers of about 100 leading companies in some 30 industries." This index's intent is to provide a snapshot of the health of the (1) country, (2) industry, and (3) individual firm. The idea is that higher scoring firms should see higher levels of customer loyalty. Satisfaction is defined as an overall evaluation based on the purchase and consumption experience with a product or product offering (Anderson et al., 1994). Satisfaction has been tied to financial performance in the B2B industry considering a single focal firm and includes outcomes studied here including EPS (Williams and Naumann, 2011). Other studies address the satisfaction and sales link across a single retailer and find the relationship to be asymmetric (Gomez et al., 2004). Service research suggests direct relationships from value and satisfaction to intentions, which may complement the magnitude of indirect effects on firm performance (Cronin et al., 2000). Finally, studies have shown evidence of satisfaction in the retail sector to positively affect outcomes such as consumer share but don't include financial outcomes (Carpenter, 2008).

Researchers have raised the question about the diagnosticity of satisfaction in relation to performance. The authors of the satisfaction index themselves realize that skewness is problematic in that 80 percent of nistomers report satisfaction. Additionally, research reports satisfaction having only a one percent explanation of variance when considering market returns due to the high cost to create satisfaction and little corresponding increases to loyalty or share of wallet (Keiningham et al., 2014). Returning to skewness, are practically all customers satisfied? If everyone is satisfied but companies still show a disparity in performance, then what is driving this apparent disparity? This research will relate satisfaction to both loyalty and two performance metrics (EPS and ROA) to determine if satisfaction is the catch-all concept within retailing while simultaneously considering the role of value as a complimentary or alternative marketing outcome. The overriding set of relationships is below in H4 and is subsequently derived into individual testable relationships.

H4: Satisfaction relates positively to loyalty, EPS, and ROA. Therefore, H4a predicts satisfaction relates positively to loyalty. H4h predicts satisfaction relates positively to EPS. Lastly, H4c predicts satisfaction relates positively to ROA.

METHODS SECTION

A professional consumer panel company provided access to U.S. consumer respondents in an effort to create some ability to generalize beyond what crowd-sourced data or data from students would allow. The effective sample size is 436. Each respondent rated each retailer they were familiar with from all retailers appearing in the ACSI listings under either the discount retailer or specialty retailer category within the previous five years and have Financial results to relate. The five concepts assessed for this research include hedonic value, satisfaction, utilitarian value, overall value, and loyalty. If a respondent was unfamiliar with a given retailer, then he/she did not rate that particular retailer, thus accounting for differing sample sizes in Table 1. The instructions and wording used for each single-item scale is provided below. Given the nature of access and the need to limit respondent fatigue, single-items were the only practical solution in this instance. The list of retailers and subsequent number of responses for each is presented in Table 1. Respondents replied to the following items, which were presented in a random order:

* Hedonic value: Think about a typical shopping trip at each retailer shown below. Rate each retailer on the extent to which the shopping trip itself is enjoyable or exciting. In other words, the visit is worthwhile even if you don't buy anything.

* Utilitarian value: Please rate each retailer below based on the extent to which you are able to accomplish the specific task of shopping (find products you need to buy, buy them at a reasonable price). 0 means that zero percent of the task would get completed and 100 means that 100 percent of the task would get completed.

* Overall value: Think about everything gained from your shopping experience with these particular retailers weighed against all the costs of shopping in these stores, and rate the overall value you received from your most recent interactions with that retailer. A score below 50 means the costs outweighed the benefits. A score above 50 means the benefits outweighed the costs.

* Satisfaction: Rate each retailer based on your opinion of how satisfied you are with shopping at each retailer.

* Loyalty: What is the likelihood of continuing to shop at the retailer based on your recent experiences? Zero means zero percent chance and 100 means 100 percent chance.

Company size is included as a control variable through gathering number of employees and dichotomizing the outcome to a one or zero based on a median split (median size = 138,000). Thus, large firms are defined as having above 138,000 employees and medium-size firms are defined here as below 138,000 employees. Of the thirteen firms rated, seven firms were above the median value and six firms were below the median value. Two variables were operationalized to represent firm financial performance using financial data collected from SEC.gov and standardized for analysis.

Each respondent rated each retailer using the scales described above. The presentation of the retailers was randomized to minimize any order effect. Sliding scales were used to capture respondent feedback. A 100-point scale was used in each case. A "don't know" option was provided to respondents who either could not recall a recent experience or simply had not patronized the particular retailer.

DATA ANALYSIS

Demographic analysis finds the gender breakdown is 50 percent female, the median age falls between 41-50 years of age, with the two largest age groups being the 61-70 year olds and the 51-60 year olds (24.8 percent and 20.9 percent, respectively). Fifty percent of the sample lives in a household earning less than $50,000. Forty-five percent of the respondents report a high school degree as the highest educational attainment and 38 percent of the sample report earning an undergraduate degree. No demographic variables discussed above were significant when inserted as control variables; thus demographic variables will not be included in the analysis.

In order to assess possible multicollinearity issues, variance inflation factors (VIF) and the related tolerance scores were obtained for the relevant variables and are presented in Table 2.

A VIF value of one means that no mulitcollinearity exists and higher values mean more multicollinearity. Research indicates that VIFs of five are a concern, and sometimes multiple VIFs greater than two can be problematic (Babin and Zikmund, 2016). For this analysis three of the variables display a value above four. In this case, satisfaction has a variance inflation factor of 4.81, and both utilitarian value and overall value show a variance inflation factor approaching 5. In fact, satisfaction has a correlation near 0.90 with other variables in the model, thus inflating the standard errors problematically. Thus, the VIFs provide evidence of potentially problematic multicollinearity.

To rectify the multicollinearity, principal component analysis was employed to transform the independent variables. The four variables were entered into the principal components analysis with four components extracted. The four components were rotated using the varimax procedure, yielding orthogonal components. In the end, each variable loads highly on only one component providing an obvious interpretation of each component as matching the single corresponding variable. Component scores were computed for each, and because of the varimax rotation, each component represents a mathematically transformed but independent representation of each original variable. Table 3 represents the correlation matrix (PCA loadings) between the components and the original variables. As can be seen, the matching loadings are 0.84, 0.77, 0.80, and 0.75, for hedonic value, utilitarian value, overall value, and satisfaction, respectively.

Thus, the component scores will be used as independent variables to assess the research questions. A general linear model approach will operationalize multivariate regression analysis with firm size, the hedonic factor, overall value factor, satisfaction factor, and utilitarian value factor as independent variables predicting EPS, ROA, and loyalty. This analysis will allow further insight into the research questions with the advantage of uncorrelated predictor variables.

Wilks' Lambda and the corresponding multivariate F provide tests of each model for the overall effect on all dependent variables. All three models yield significant F-statistics (p value < 0.001). The significant multivariate F-statistics provide support for proceeding to the univariate regression analyses and to analyze the relationship between the dependent and independent variables.

Tables 4, 5, and 6 provide univariate regression results. As is clear, each model predicts a significant portion of variance in the corresponding dependent variable. The resulting [R.sup.2] values are 0.088, 0.13, and 0.76, for EPS, ROA, and loyalty, respectively. The standardized (3 coefficients, allowing for a relative comparison of effect sizes, are presented along with unstandardized K, which allow for a direct assessment of the slope in real units. Also, the tables reflect the 95% confidence intervals (CI) as a better indicator of effect size than a reliance on p-values. Hedonic value, overall value, and satisfaction all relate significantly to EPS. The findings indicate that hedonic value (H2b: [beta]=0.11, B=0.31, p<0.001) and overall value (H1b: ([beta]=0.08, B=0.21, p<0.001) relate more strongly to EPS than does satisfaction (H4b: [beta]=0.04, B=0.12, p<0.01). Utilitarian value's CI contains zero and thus, it is not a significant predictor of EPS thus not supporting H3b. All of the effects control for a significant and positive influence of firm size ([beta]=0.25, B=1.38, p<0.001).

Hedonic value, overall value, and satisfaction components all significantly predict ROA, as evidence by the lack of 0 in the corresponding CI (Table 5). Hedonic value (H2c: [beta]=0.07, B=0.20, p<0.001) and overall value (HIc: [beta]=0.06, B=0.19, p<0.001) display the largest effects on ROA, compared to utilitarian value (H3c: [beta]=-0.04, B=-0.11, p<0.05), while the effect of the satisfaction component on ROA is not significant thus not supporting H4c. Once again, the effects control for firm size, which relates to ROA significantly indicating relatively large companies experience more positive returns on assets ([beta]=0.35, B=2.11, p<0.001).

In contrast to the two financial performance metrics, all loyalty predictors show significant, positive effects. Satisfaction relates positively, with the highest standardized effect on loyalty (H4a: [beta]=0.54, B=18.18, p<0.001), with overall value second (Hla: [beta]=0.44, B=14.88; p<0.001), followed by utilitarian value (H3a: P=0.39, B = 0.13.23, p<0.001) and hedonic value (H2a: [beta]=0.35, B=12.01, p<0.001). Once again, these effects take into account size as a control variable. For perceived loyalty, size displays a relatively small but significant effect suggesting more loyalty for larger firms ([beta]=0.02, B = 1.28, p=0.03).

DISCUSSION

The research question presented in this paper asks, "What are the dominant drivers of loyalty and firm financial performance?" A survey modeled after the ACSI provides data on the diagnostic effects of value, its key dimensions, satisfaction, and loyalty, in terms of predicting firm performance. Specifically, how do consumers' value perceptions compare to asking customers about their satisfaction in explaining firm performance? This research relates multiple value perspectives and the traditional satisfaction construct with EPS, ROA and loyalty for retailers. Results, perhaps unsurprisingly, show that satisfaction, value, and loyalty are positively related in all contexts. Thus, a correction for multicollinearity allows a more valid comparison of the effects. The relationship between perceived overall value provided by a retailer and both EPS and ROA is stronger than the relationship between satisfaction and EPS or ROA. The question seems to be answered best by separating loyalty, captured here as an attitudinal concept, from financial performance, because the drivers of the two appear to diverge. If loyalty is the goal, satisfaction seems to be most diagnostic but, perhaps, the reason for this finding is the measurement of the two and the inability of the customer to separate the two concepts. When financial performance is measured, satisfaction appears to take a backseat to the value components' predictability, including particularly the role of hedonic value and EPS and hedonic value, overall value, and utilitarian value and ROA.

Marketing managers competing in the SDL era strive to understand what operant resources the retailer provides and how these resources relate to marketing management outcomes. Loyalty, here being an attitudinal survey measure similar to satisfaction, is often a stated goal of marketing managers. Similarly, financial outcomes such as EPS and ROA are important outcomes for marketers if marketing variables affect these outcomes.

Satisfaction is often seen as a catch-all measure thought to drive all outcomes. In this light, the relationship between value and outcome variables (survey and marketbased) often is examined with value operating through satisfaction (Zhong and Mitchell, 2010; Caruana, 2000; and Orth et al., 2010). Results here suggest that theories supposing satisfaction as mediating retail variables' effects on retail market success may be inaccurate. The results presented here suggest positive and significant effects for hedonic shopping value and EPS, an effect that does not depend on loyalty to work and when placed with satisfaction in fact overshadows the concept. EPS is thought to be a good measure of a firm's profitability and stock price and thus the findings that hedonic value relate to this measure more so than do the other variables (UV, satisfaction, and overall value) is important to retail decision-makers. The findings which correlate hedonic value with EPS and ROA is theoretically appealing and more diagnostic given the traditional model which places the hedonic and utilitarian value components relating to overall value and satisfaction. The hedonic value relationship with both EPS and ROA suggest that retailers can influence these market variables with a pleasing experience as an operant resource. Additionally, the relationship between hedonic value, utilitarian value, and overall value and ROA is stronger than the relationship between satisfaction and ROA which fails to reach significance. Thus, for retailers, customers expect to be satisfied, however, a valuable experience where the task is simple to complete combined with a pleasing retail experience results in higher total profits with respect to ROA.

LIMITATIONS, CONTRIBUTIONS, AND FUTURE RESEARCH

The limitations and contributions will be discussed next. This work attempts to assess the relative relationship between value-in-use, as captured by utilitarian, hedonic, and overall value, and performance metrics such as EPS and ROA. In this case, given that thirteen retailers are included, a multi-level or mixed-models analysis is not reported to allow for a more straight-forward presentation. Future research should increase the number of focal firms and respondents to allow for such an analysis.

A second limitation in this work deals with using single-item independent variables. However, every attempt was made to ensure that the definition for each variable coincided as closely as possible with the measure used above to ensure valid measures. Future research should attempt to replicate the work with a greater variety of attitudinal and market constructs.

Finally, future research should investigate and include other predictor variables including tax rates, interest rates, and dividend rates, which can affect ROA and EPS if the goal is to fully explain ROA or EPS. These predictors towards financial outcome variables could capture variance not explained in this research. These predictors have been used in previous research (Ross et al., 1999) and are beyond the scope of the current research.

The practical contributions assess the extent to which providing and measuring success based on satisfaction is enough. The disconnect between customer satisfaction and outcome variables is well documented (see Woodruff, 1997; Reichheld, 2003). This research suggests that retail success in terms of EPS and ROA occurs by providing customers an overall experience they value (overall value), and by directing resources towards providing customers with a gratifying experience. Future research should expand the number of and variety of financial outcomes including profit and stock price. Additionally, experimental research manipulating value elements (hedonic and utilitarian value) holding other factors constant across several comparable branded stores would allow causation to be analyzed with store sales as an attractive outcome.

From a managerial point of view, an examination of the change in share value given a change in hedonic value is appropriate. The hedonic value effect on EPS of 0.311. Taking it and transforming it back to its original metric would equate to an equivalent beta of 0.012. That means that a single point change in hedonic value increases EPS by 0.012 after controlling for firm size. While this may not seem like a huge difference, for every 1,000 shares the one point difference in HV returns $12. Many of the firms here have millions of shares outstanding.

Finally, firms finance their projects either internally or externally. The growth generated from obtaining financing from new creditors or shareholders may not be sustainable because the higher costs associated with using them. Corporate debt has the possibility of default. Excessive borrowing may significantly increase firms' financial distress costs, which increases cost of using capital and consequently reduces return from investment. Issuing new equity is generally viewed by investors as a signal for overvalued stocks. In addition, additional equity financing dilutes profits attributable to existing shareholders. As such, equity financing in general leads to a drop in current stock price. Pecking order theory proposed by Myers (1984) suggest firms prefer internal funds to external funds to reduce costs of using capital and creates higher return for shareholders. Thus, higher EPS and ROA, holding all else constant, would result in more internal fund generation leading to lower cost of investment and therefore more positive long-term growth potential.

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Kevin W. James

Assistant Professor of Marketing

The University of Texas at Tyler

kjames@uttlyer.edu

Hui James

Assistant Professor of Finance

The University of Texas at Tyler

hjames@uttyler.edu

Barry J. Babin

Chair, Department of Marketing and Analysis

Louisiana Tech University

bbabin@latech.edu

Janna M. Parker

Assistant Professor of Marketing

James Madison University

parke4jin@jmu.edu
Table 1
Retailer Effective Sample Size

                         Effective
Retailer                Sample Size

Dillard's                   213
Dollar General              321
JC Penney                   376
Kohl's                      342
Kroger                      233
Macy's                      306
Nordstrom                   203
Publix                      172
Safeway                     200
Sears                       389
Target                      406
Wal-Mart                    436
Whole Foods                 207

Table 2
Retail Multicollinearity Table Including VIF and Tolerance

MEASURE      Tolerance       VIF

HV              0.31         3.24
UV              0.21         4.66
OVALUE          0.22         4.53
SAT             0.21         4.81

Table 3
Retail Evaluation Principal Component Score Variable Correlations
(Loadings)

                                    Measured Variables

Component                       HV      OV      SAT     UV

Hedonic Value Component        0.84    0.34    0.38    0.39
Overall Value Component        0.31    0.80    0.40    0.38
Satisfaction Component         0.30    0.36    0.75    0.35
Utilitarian Value Component    0.32    0.37    0.37    0.77

Table 4
Univariate Results for EPS

                                        C.I.

EPS                     df      F       Sig.      t      [beta]

Corrected Model         5     67.43     0.00
Intercept                               0.00    17.87
Size                    1               0.00    15.36     0.25
Hedonic Value           1               0.00     7.04     0.11
Overall Value           1               0.00     4.76     0.08
Satisfaction            1               0.01     2.65     0.04
Utilitarian Value       1               0.39     0.87     0.01

                                   C.I.

EPS                      B      Lower    Upper

Corrected Model
Intercept               1.17     1.04     1.30
Size                    1.38     1.20     1.55
Hedonic Value           0.31     0.23     0.40
Overall Value           0.21     0.12     0.30
Satisfaction            0.12     0.03     0.20
Utilitarian Value       0.04    -0.05     0.13

EPS R Squared = 0.088

Table 5
Univariate Results for ROA

                                      C.I.

ROA                     df      F       Sig.      t      [beta]

Corrected Model          5    107.80    0.00
Intercept                               0.00    73.25
Size                     1              0.00    21.98     0.35
Hedonic Value            1              0.00     4.20     0.07
Overall Value            1              0.00     4.01     0.06
Satisfaction             1              0.07     1.80     0.03
Utilitarian Value        1              0.02    -2.28    -0.04

                                   C.I.

ROA                      B      Lower    Upper

Corrected Model
Intercept               5.13     5.00     5.27
Size                    2.11     1.92     2.30
Hedonic Value           0.20     0.11     0.29
Overall Value           0.19     0.10     0.28
Satisfaction            0.09    -0.01     0.18
Utilitarian Value      -0.11    -0.20    -0.02
ROAR Squared = 0.13

Table 6 Univariate Results for Loyalty

Loyalty                                C.I.

                        df       F      Sig.      t      [beta]    B

Corrected Model         5     2182.00   0.00
Intercept                               0.00    150.60           64.41
Size                    1               0.03     2.22    0.02     1.28
Hedonic Value           1               0.04     2.02    0.35    12.01
Overall Value           1               0.00    10.89    0.44    14.88
Satisfaction            1               0.00    29.43    0.54    18.18
Utilitarian Value       1               0.00     8.49    0.39    13.23

Loyalty                      C.I.

                       Lower    Upper

Corrected Model
Intercept              63.57    65.24
Size                    0.15     2.40
Hedonic Value          11.46    12.56
Overall Value          14.33    15.44
Satisfaction           17.63    18.73
Utilitarian Value      12.67    13.79

Loyalty R Squared = 0.76
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Author:James, Kevin W.; James, Hui; Babin, Barry J.; Parker, Janna M.
Publication:Journal of Managerial Issues
Date:Jun 22, 2019
Words:6013
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