Individual Assignment 2: Key Guidelines & Insights

Introduction

The study aims to analyze key factors that influence on customer’s likelihood of product purchase within the context of Business-to-Business (B2B) in the HBAT dataset. The key variables tested in the study include Product Quality, Compliant Resolution, Salesforce Image, Competitive Pricing, and Delivery Speed. The dataset in HBAT will be analyzed using SPSS techniques that contain responses from 100 participants. To understand the relationship between key variables in the dataset, multiple regression analysis would be utilized and interpretations would be drawn comparing with existing literature.

Main Body

Part A: Findings and Interpretations Using the Enter Method

Enter Method

The first method under multiple regression analysis used is the Enter Method. The Enter method organizes all predictors into the regression model at once (Gawrych, Cichoń & Kiejna, 2021). The respective approach can make it easy to see how each variable contributes to the overall prediction without changing the order of entry to reflect significance. The method tends to be effective as this allows equal weight to all predictors, which then gives a view into the relative strength and contribution of every variable in the model as a whole.

Results and interpretations

The results have been drawn using SPSS analysis by undertaking the use of a multiple regression approach (Appendix 1). The enter method was performed to determine the possible relationship between the key variables in the dataset to evaluate the purchase likelihood of B2B customers. The results were obtained considering, ‘Product_Quality’,‘Complaint_Resolution’, ‘Salesforce_Image’, ‘Competitive_Pricing’, and ‘Delivery_Speed’ as the independent variable and ‘Likely_to_Purchase’ as the dependent variable in the dataset (Appendix 2). The following steps were taken to obtain results under the selected method, Enter Method (Appendix 2);

• Step 1: Navigating to Analyze -> Regression -> Linear
• Step 2: Dependent variable as, ‘Likely_to_Purchase’
• Step 3: Independent variable as, ‘Product_Quality’,‘Complaint_Resolution’, ‘Salesforce_Image’, ‘Competitive_Pricing’, and ‘Delivery_Speed’
• Step 4: Method selection as ‘Enter Method’
• Step 5: Run Analysis

Table 1: Model Summary

Table 1 determines R-squared value achieved under the model in the Enter method was 0.451 suggestinga 45.1% variance in customers' likelihood to make a purchase considering variables of product quality, complaint resolution, salesforce image, competitive pricing, and delivery speed. An Adjusted R-squared of 0.422 implies that, having adjusted for the number of predictors, the remaining variance to be explained by the predictors is around 42.2%, which in this case would indicate a pretty good model fit within the B2B context for these variables. Furthermore, the standard error of the estimate being 0.7118 provides an estimate of the typical deviation between observed and predicted values of the dependent variable, meaning that it has a rather moderate prediction accuracy for the model.

Table 2: ANOVA Table

Table 2 interprets the ANOVA results that support the overall significance of the model because the F-statistic used is 15.445 and has a p-value of 0.000 below the threshold value of 0.05. This result shows that the model significantly explains part of the variability in the likelihood of purchasing, establishing strong evidence that these predictors impose collective effects on the customers' purchase intentions.

Findings and Discussions

Diputra and Yasa (2021) state that product quality has often been proven to affect customer satisfaction and loyalty, in turn,purchasing behaviour. The findings of the mentioned study agree with the results obtained under this study. The model summary table provided results based on testing ‘Product_Quality’ as the independent variable impact on the ‘Likely_to_Purchase’ dependent variable. It can be discussed that the test interprets the 0.672 R-value that defines the strong positive influence of‘Product_Quality’ on ‘Likely_to_Purchase’.

Furthermore, Jeanpert, Jacquemier-Paquin, and Claye-Puaux (2021) emphasizes the importance of customer service as well as complaint handling for increased customer satisfaction and loyalty. The findings of the mentioned studydetermine that complaint handling can considerably impact the purchasing opinion of customers within the marketplace. It can be discussed that results interpreted through Enter Method multiple regression analysis interpret an R-squared value of 0.451. The results align with the findings of the above-mentioned study that determine the influence of complaint-handling strategies adopted by the business on manipulating the purchase decisions of the customers in the marketplace.

Part B: Findings and Interpretations Using the Stepwise Method

Stepwise Method

The second method under multiple regression implements a Stepwise method. The stepwise method is a sequential approach thatis built up incrementally by adding a set of predictors according to their statistical significance (Ahmed et al., 2021). This method is important in ranking the most influential variables and selecting predictors that explain the variable of interest, excluding those that contribute meaningfully to an explanation.

Results and interpretations

The results under the Stepwise multiple regression method are drawn considering important variables in the dataset that impact on purchasing likelihood of customers. The independent variables including, ‘Product_Quality’,‘Complaint_Resolution’, ‘Salesforce_Image’, ‘Competitive_Pricing’, and ‘Delivery_Speed’ were input into the model by selecting the Stepwise method in the linear regression model (Appendix 3). The considered variables tend to influence the dependent variable, ‘Likely_to_Purchase’. The following steps were taken to obtain results under the selected method, Stepwise method (Appendix 3);

• Step 1: Navigating to Analyze -> Regression -> Linear
• Step 2: Dependent variable as, ‘Likely_to_Purchase’
• Step 3: Independent variable as, ‘Product_Quality’,‘Complaint_Resolution’, ‘Salesforce_Image’, ‘Competitive_Pricing’, and ‘Delivery_Speed’
• Step 4: Method selection as ‘Stepwise method
• Step 5: Run Analysis

Table 3: Model Summary

Table 3 interprets the results obtained under the Stepwise multiple regression model applied. In Model 1, only ‘Product_Quality’ was entered into the model as a predictor, thus having an R-squared of 0.214. This would mean that this specific product quality explains 21.4% of the variance in the customers' likelihood to buy. The relatively high R-square for a single predictor suggests that the quality of the product is highly related to purchasing likelihood. The Adjusted R-squared is 0.206, nearly as large as the R-squared, similar to that of Model 1 with only one predictor, and the standard error of 0.8343 reflects the mean difference between observed and predicted values based on this one-variable model.

The second model adds ‘Salesforce_Image’ as a second predictor and increases the R-squared value to 0.377. This means that the effect of product quality plus salesforce image now explains 37.7% of the variation in the likelihood of purchase. With the adjusted R-squared at 0.364, including Salesforce Image significantly enhances the fit of the model even after considering the number of predictors. The standard error goes down to 0.7467, which means increasing model complexity improves predictive accuracy.

In Model Three, ‘Complaint_Resolution’ as a third predictor raises the R-squared to 0.445. Here, at the stage of including all three predictors, the model explains 44.5% of the variation in the likelihood of purchasing. The Adjusted R-squared of 0.427 indicates further that this three-variable model gives a very good explanatory fit. The standard error has been decreased to 0.7084, and thus speaks of an even stronger prediction of purchase likelihood, illustrating that adding Complaint Resolution increases the overall precision of the model.

The variable ‘Delivery_Speed’ was excluded from the analysis because the variable did not meet the statistical significance. The Stepwise method undertakes variable predictors in the test that upraises the model considering the most impactful predictors to establish the relationship between the dependent and independent variables.

Table 4: ANOVA

Table 4 interprets ANOVA results obtained under the Stepwise method. For model 1, the F-statistic stands at 26.621 with a p-value of 0.000, thus it proves that product quality, X6 is associated with likelihood to purchase. In this scenario, the p-value of the model is less than 0.05; it is unlikely that the relationship between product quality and likelihood to purchase by chance. Furthermore, model 2 enhances the model significance with the addition of Salesforce Image X12, having an F-statistic of 29.288 and a p-value of 0.000. The rise in the F-statistic depicts the impact of adding Salesforce Image to the explanatory power of the model. Human interactions and the effectiveness of the salesforce play a critical role in determining the likelihood of purchasing, as does the perception of customers towards the salesforce. Finally, model 3 Including Complaint Resolution Model 3 includes Complaint Resolution (X9), which gives it an F-statistic of 25.617 and a p-value of 0.000 to further establish that the model is important. The final model shows that Complaint Resolution brings significant predictability to the model, implying its role in customers' purchase decisions. The big F-statistic with a low p-value indicates that the model, with the addition of product quality, sales force image, and complaint resolution explains the variance in the probability of purchasing pretty well.

Findings and Discussions

Alnakhli, Inyang, and Itani (2021) demonstrated that a sophisticated and strong salesforce can build trust and satisfaction with customers, thus influencing the likelihood of purchasing. It can be discussed that the findings of our study appear to support the idea that in addition to quality, customers are also sensitive to their relationship with the salesforce. The results interpreted an increase in R-square value at0.377 in model 2 suggesting that B2B purchasing involves complex and lengthy relationships more than often, and an excellent image of the salesforce develops confidence for customers and maintains the decision to purchase.

Moreover, Zhao et al. (2021) found out that in B2B transactions, customers often give precedence factors compliant resolution when considering purchases. It can be discussed that in B2B contexts where purchases may be perceived as investments, buyers might concentrate on complaint resolution as an important consideration when considering a purchase. The findings of the mentioned study align with the present study as interpreting the highest value in the model summary table of .445 as the R-square value. The results suggest that the business having string complaint resolution arrangements is likely to facilitate a decent demand of customer likelihood of purchases within the B2B scenario marketplace.

Conclusion

The study examines the critical determinants of purchase likelihood in a B2B context, using the HBAT data. The study results of the multiple regressions with both the Enter and Stepwise approaches point toward critical factors such as product quality, sales force image, and complaint resolution in purchase decision-making. The enter method produced a cumulative effect of all variables tested and explained 45.1% of the variance in the likelihood of buying. The stepwise method indicated product quality, sales force image, and complaint resolution are some of the most significant contributors to the model precision as each one was added to the model in sequence. The analysis supports the assertion that purchasing decisions in B2B are multifaceted-while recognizing product quality, they also rely on interpersonal factors like the image of the salesforce, and effective complaint handling, among others. These results are consistent with the extant literature emphasizing trust, relationship management, and customer service aspects in the B2B context. It was concluded that businesses must invest in quality products and strong customer-facing teams, mainly at the complaint resolution and salesforce presentation levels, for the businesses to drive the customer's purchase decision.