Executive Summary
VisionEcoWear is a medium-sized, U.K.-based company specializing in designing and selling eyewear in the U.K. market. The company intends to partner with other companies for joint production and increased sales. In addition, VisionEcoWear aims to retain its current customers and ensure their satisfaction through this alliance. Most firms around the globe have continuously engaged in several alliances with other firms in the same industry to improve their market efficiency and add value to their operational activities. Recent market trends have shown that organizations that form alliances with their competitors have experienced increased sales and achieved higher growth rates.
Therefore, this paper evaluates the effects of brand alliances on VisionEcoWear and explores how the company can utilize these alliances to maintain its current level of customer satisfaction. Through an analysis of the critical elements of consumer satisfaction, the paper concludes that brand alliances enhance the firm’s value and increase customer satisfaction, based on the data collected. Therefore, it is recommended that VisionEcoWear partner with other companies and competing firms in the design and other industries to increase its production levels in the future.
Introduction and Background
Brand alliance is a corporate strategy in which companies combine two or more of their own brands. It is a marketing growth strategy that some companies have previously employed. This strategy also involves deliberately pairing two brands with one another from a marketing perspective.
The critical areas of brand alliances arise in product advertisement, placement, and distribution (Fernandes et al., 2022). The early theories of brand alliances focused on the product categories that fit in the alliances. However, the current technique relates the respective categories of products to consumer satisfaction.
Forming alliances among companies arises from their interaction with one another. Brand alliances involve dealing with abstract concepts in the consumer’s mind. The idea that things can work together when placed together is a key contributor to the brand alliance strategy employed by firms (Angulo-Ruiz et al., 2018).
In this concept, various dissimilar brands are brought together in terms of organizational goals, benefits, and performance situations. For instance, the alliance between Breitling and Bentley would be viewed as a low-fit product category. However, in the end, they met the appropriate goals of consumers by offering luxury appeals as needed by the consumers.
Various companies have formed alliances in their operations through partnering in different industries. These alliances create a portfolio of operational management for these companies. Amongst the recent alliances formed by different companies is the co-promotion between Breitling watches and Bentley cars. In this alliance, the two companies used the brand “Breitling for Bentley.” Most consumers viewed these alliances as a logical fit to meet their needs (Martin & Javalgi, 2019).
Another alliance formed in recent years is the Toyota and Mazda alliance. Some benefits of brand alliances include acquiring new resources in various fields, accessing new markets and products, and improving the output of the firms in the alliance. When companies collaborate, new investment decisions enable managers to set priorities in their production units, which in turn forecast the growth levels of the firms (Hair, Ortinau, and Harrison, 2021).
Previous studies have emphasized the value driven by brand alliances to the companies. Therefore, this paper analyzes the benefits of brand alliances to VisionEcoViewer, a company that designs and supplies eyewear in the U.K. market. The study concentrates on two specific issues to perform the data analysis. The first issue is evaluating the value of brand alliances for this company. The next concern involves determining how key elements, such as user experience, purchase frequency, sustainability, product quality, and price, influence customer satisfaction.
The paper presents two main research questions that it aims to address.
- H1: How well do the user experience, purchase frequency, sustainability, product quality, and price predict customers’ satisfaction with VisionEcoWear?
- H2: Which is the best predictor of customer satisfaction?
- H3: The overall question is, what is the overall benefit of brand alignment to the VisionEcoWear company?
Literature Review
Brand alliance concepts are selected by firms and derived from the needs and demands of consumers through their regular purchasing behavior. The brand concept, therefore, positions and differentiates various brands and commodities of the same category (Angulo-Ruiz et al., 2018). Besides aligning with the company brand concept, other companies form alliances for their functional and expressive motives.
The functional motive of brand alliance is the motivation for an alternative solution arising from the customer’s problems and the need for an organization to improve its performance. The expressive motive of brand alliances arises from the need to motivate searches, which can fulfill internal desires such as self-enhancement, role positioning, membership in individual groups, and ego enhancement for every member (Markovic & Bagherzadeh, 2018). Hence, brand alignment is a firm effort to distinguish a brand’s products and features. It also ensures that potential customers are satisfied.
When evaluating the best category fit for alliances, the company assesses whether the products complement or can substitute for one another. In addition, the firm focuses on products that share the same characteristics and can be consumed in the same situation or serve the same function (Ko et al., 2020). Hence, alliancing involves comparing two different products in terms of their attractiveness and attributes. Generally, firms evaluate the attributes individually, and their overall contribution to the value addition is analyzed by combining the resultant contribution of each product.
Consumers typically evaluate whether the association between the two products is similar and provides the best satisfaction. Hence, the brand alliance concept is a category structure in the minds of consumers. They possess interrelationship attributes that consumers have formed beliefs and developed emotions through their everyday experiences with the products (Fernandes et al., 2022).
In brand alliances, the individual brands combine their established concepts and association with the alliance. In this strategy, two associations co-exist and should therefore be considered separately. Hence, when consumers encounter incoherence, they should carefully scrutinize all available information in these alliances.
When there is a low category fit, where two processes cannot align, firms can develop two strategies. The first strategy involves assimilating new information into the existing schemas, and the second involves establishing a new schema. Most firms view assimilation as the most feasible technique when assisting consumers in comparing the various brand concepts (Hair, Ortinau, and Harrison, 2021).
When a customer has a positive experience, it is transferred to the brand alliance, making the customer evaluate the alliance favorably. The assimilation of a new schema cannot resolve the lower levels of fit in two products, as this process requires more resources from the customer and does not guarantee a successful outcome (Martin & Javalgi, 2019). This process is necessary when aligning two products: one for a functional brand and another for an expressive brand.
Expressive branding is composed of non-related products from different firms with unique features. When a firm intends to build a new schema, critical processes require high motivation and the ability to resolve inconsistencies in product demand. An organization should analyze and assimilate existing products before forming brand alliances (Angulo-Ruiz et al., 2018). Generally, alliancing benefits positively impact the productivity and output of the firm, improving the quality of its products in the market. A firm that takes advantage of this strategy would achieve a high growth level and an improved competitive advantage, as its products would be marketed in conjunction with the other partnering company.
Data Analysis Method
Quantitative data analysis was performed, which involved analyzing the raw data collected and processing them into numerical data for interpretation and presentation. The data were analyzed using this method since actual data were collected from a sample of respondents representing the general population (Martin & Javalgi, 2019). Hypothesis testing was used to assess the relationship and establish the underlying facts regarding the independent and dependent variables. Additionally, the mean and frequencies of the data were calculated to illustrate the trend in the listed items.
Data collected from 120 customers were recorded and analyzed in SPSS to determine the relationship between each factor and their general contribution to customer satisfaction. Specifically, seven points were investigated to respond to the research questions. These elements included: user experience, purchase frequency, sustainability, product quality, price, and level of satisfaction. Specifically, the study aimed to establish the relationship between individual variables and customer satisfaction levels. Each item was listed on the Likert scale based on its measurements.
The user experience was measured based on VisionEcoWear’s friendly website. Purchase frequency was measured by the frequency with which consumers purchased goods from the company. The sustainability of the brand was measured, as it is perceived as sustainable, and the product quality was assessed by the number of high-quality goods offered by VisionEcoWear.
In addition, the focus was on determining the factor that gave the best customer satisfaction prediction. The analyzed data were presented in tables, charts, and graphs to show the association between the variables. A multiple regression analysis was done to establish the effect of independent variables on the dependent variable (Markovic & Bagherzadeh, 2018). Descriptive statistics were computed using means, frequencies, standard deviations, variances, and correlation coefficients.
Key Assumptions Made
- All 120 observations were analyzed.
- The components of variables were uniquely coded to replace the main elements.
- The customer level of satisfaction was the dependent variable, while other components were the independent variables.
- A 95% confidence level was used in the computation of descriptive statistics.
- A multiple regression analysis examined the relationship between customer satisfaction and the six other factors.
Data Analysis and Interpretation
The Initial Data Screening
When the analyses were performed, the results showed a predicted value mean of 5.20, with a standard deviation of .955 from a sample of 120. Additionally, the mean standard error of the predicted value was 180, the standard deviation was .057, and one value was missing. This information is presented in Table 1 below.
Table 1: Initial Data Screening: Residuals Statistics
From the table above, the residual means were 0 with a standard deviation of 0.824. However, the deleted residual mean was -.004 with a standard deviation of 1.018. Therefore, these results show that when a missing value is included in the output, the mean of the variables decreases while the standard deviation increases. Hence, by including a new variable, there would be a general increase in the standard deviation of the whole model and a lower mean from the model components.
Descriptive Statistics
Upon analyzing the descriptive statistics, the following result is obtained. Customer satisfaction had a mean of 5.20 with a standard deviation of 1.261, purchasing frequency had a mean and standard deviation of 4.05 and 1.076, respectively, and sustainability had a mean and standard deviation of 5.37 and 0.961, respectively. In addition, the user-friendliness, quality products, and prices had means and standard deviations of 5.77 and 1.214, 3.49 and.879; and 3.335 and 1.752, respectively. These results are shown in Table 2 below.
Table 2: Descriptive Statistics
Regression Analysis
When the regression analysis was conducted, the following results were found: r=.757, r-squared=..573 573, adjusted r-squared=.554, standard error of the estimate=.842, f=30.607. These results are presented in Table 3 below.
Table 3: Regression Model Summary: Model 1
Analysis of Variance (ANOVA)
When the variances were analyzed to establish the variables’ values’ differences, the regression model’s mean square was 21.686. In contrast, the resultant variance had a mean square of 709. The sum of squares in the regression model was 108.428 with df=5, while the residual model had a sum of squares of 80.772 with df=114. The missing value was at sig=.000. These results are shown in Table 4 below.
Table 4: ANOVA
Histogram and Normal P-P Plots
These charts were drawn to show the interrelationship between independent and dependent variables. Customer satisfaction was the dependent variable in the histogram, while all six other items were independent variables. Figure 1 below demonstrates the histogram of customer satisfaction and other variables.
The histogram above showed an overall mean of -8.71 with a standard deviation of 979. The curve’s movement lengthens towards the right as the shift is minimal on the left. The histogram’s maximum peak is at a frequency of 17, while the curve is at a minimum with a value of 0. The distance between the left and right tails is -3, and the curve extends to a maximum of 2.5 towards the right.
The P-P curve, as shown in Figure 2 below, shows the regular distribution curve changes.
Discussion, Limitations, and Conclusions
Discussion
From the analysis, the mean of user-friendliness was the highest, while the company prices had the lowest mean. Regarding standard deviation, the prices had the highest value, while quality goods had the lowest standard deviation. These results support the value of brand alliances to an organization.
From the onset, the benefits of alliancing include a firm rise in customer satisfaction and an organization’s products’ ability to retain customers. Theoretically, when consumers are satisfied, they are retained, which is one of the objectives of brand alliancing (Angulo-Ruiz et al., 2018). Based on the first research question, there was a positive relationship between the customer level of satisfaction and the other variables.
From the analysis, it can be concluded that the R of.757 showed a positive relationship between the independent and dependent variables. Hence, when one unit of an independent factor was changed, there was a positive change in a unit of the dependent variable. The F-critic of 30.607 further shows severe multicollinearity of the variables, and the high value evidences this in the standard error of the estimate at.842. The R-squared of.573 and the adjusted R-squared of.554 shows that when a new variable is included in the model, there would still be a positive output. Based on the second research question, user-friendliness was the best predictor of customer satisfaction since it had the highest mean of 5.77, with a relatively high standard deviation of 1.214.
Limitations
The current study, however, had some limitations leading to inadequate performance. The first limitation was that the data analyzed could have been more extensive, as 120 respondents in the overall design market were used to represent the whole population. In addition, the measures of the variables were restricted to only one measure, which affected the output of the analysis. Therefore, future researchers should focus on analyzing the impacts of these factors with detailed data, such as more than 1000 observations, and also include other measures of the variables to test for all areas of concern in this study. In addition, several other studies should be done in other areas of marketing to establish the effect of the variables on customer satisfaction from varied fields.
Conclusion
The curve moved towards the right, showing that the customer satisfaction levels increased by including the variables in the study. Hence, with all the items considered, there would be a general increase in customer satisfaction in the company. The results further affirm that when the firm focuses on improving the quality of its products, lowering its prices, improving its websites, and manufacturing and selling sustainable products, customers will likely be satisfied in the long run.
Reference List
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Hair, J.F., Ortinau, D.J. and Harrison, D.E. (2021) (5th edition). “Introduction to research in marketing: the marketing research process.” McGraw Hill Education international editions. New York.
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