Complexity of Learning Lexicographic Strategies - MyAssignmenthelp

Question: Discuss about the Complexity of Learning Lexicographic Strategies. Answer: Introduction: Survey among consumers of Schmeckt Gut Energy Bars carried out in 5 districts namely, A, B, C, D, and E reflected mixed response on satisfaction level of consumption- a mean value of 7.27. Resultantly, weight of the bars was recorded to understand its impact, if any, on the degree of satisfaction among its consumers, thereby detailing certain concrete recommendations to address the situation. This report in consideration to the purpose stated, carried out certain statistical analysis on the predictor variable- weight of the energy bars and the response variable- customer satisfaction to establish the causality of the former on the latter. Statistical tools namely mean and standard deviation was carried out to understand the standard weight of bars across districts, followed by Pearson Correlation and Linear regression. Mean distribution of Schmeckt Gut Energy Bars across 5 districts reflected varied weight distribution initiating below 46 grams to above 48 grams (see Figure 1 below), despite the standard weight being specified as 47 grams. However, since majority of the weight examined remained within 46.90 to 47.20, slightly above and below the standard margin, the average weight distribution, taking all the districts together project a mean value of 46.88, establishing approximately standardized weight, when taken on average. Standard deviation of .70 obtained from the descriptive analysis justifies the concentration of data around mean value of weight (see table 1, below). Descriptive Statistics N Minimum Maximum Mean Std. Deviation Weight 160 45.20 49.00 46.8850 .70105 CS 108 3.00 10.00 7.2778 2.11728 Frequency distribution of customer satisfaction with Schmeckt Gut Energy Bars further presents affirmative results with 53.7% rating the bars between 8 to 10 (see figure 2 below). Hence mean value of consume response project an above average value of 7.2, with standard deviation of 2.1 validating the concentration data to certain extent (see table 1, above). Having established the mean values of both customer satisfaction and weight of the energy bars, it was now imperative to understand if there exist any linear relationship between the two variables. This imperativeness can be reasoned with the necessity to recommend effective strategies, which can be shaped if the causality of weight of energy bars on customer satisfaction is established. If not established, other parameters like ingredients, taste, price to name a few can be applied further, to strategize the degree of satisfaction among consumers. Bivariate correlation and linear regression, principal statistical methodology for observational experiments were applied to establish linear relationship and causality, where Pearson coefficient value projected its invariance to linear transformation of either variables (Rodgers and Nicewander; p.61). As seen in Table 2 below, weight and customer satisfaction established a negative relationship ( -.161) with significance at .10 index (0.9 6) and hence a negative causality of beta value (-.54). The results refer to inverse movement between weight of energy bars and customer satisfaction. Weight CS Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta Weight Pearson Correlation 1 -.161 47.413 .242 195.909 .000 Sig. (2-tailed) .096 CS Pearson Correlation -.161 1 -.054 .032 -.161 -1.677 .096 Sig. (2-tailed) .096 R square value in regression model too project a lower degree of variance (.026) with F value at 2.812, establishing the model not fit to regression equation and thus accepting the null hypothesis that there exists no relationship between weight of energy bars and customer satisfaction. Model R R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson F Sig. 1 .161a .026 .017 .69957 2.051 2.812 .096b Following the acceptance of null hypothesis that there exists no relationship of weight on customer satisfaction, this report moves forth in developing some concrete recommendations which purports to serve as guidance to decision making by Schmeckt Gut. Apart from the inverse relationship forming rationale for recommendation, the varying ratings of customers of the energy bar, going as below as 3 also serves as motivation. Besides, the deviating range of weight- from approximately 45 grams to 48 grams also serves as rationale for this recommendation. Based on Nicholas Bernoulli, John von Neumann, and Oskar Morgensterns Utility theory, consumers are rational beings who invest in only those products which maximize their well-being (Fishburn, 1989). Prospect theory propounded by Daniel Kahneman and Amos Tversky additionally attaches value and endowment as core elements based on which consumers choose their products (Kahneman Tversky, 1979). Following these two theories, Schmeckt Gut is recommended to develop the quality of their energy bars in terms of nutrition along with variety and unicity, which will make the bars precious to consume owing to unavailability of such elements in similar products in the market. Apart from acting as meal replacement, Schmeckt Gut energy bars should act as complementary choice for fitness conscious consumers or pregnant women, with ingredients like rolled oats, rice, seeds (like flaxseed or chia), nuts and whey isolate or pea blend as vegetarian options. Decision making should also focus in line with lexicographic strategy, where consumers evaluate products on most important attribute before buying (Schmitt Martignon, 2006). Here, if the energy bars are developed focusing of a target audience of pregnant women, this will potentially up the market, providing competitive edge to Schmeckt Gut in the market. Further, marketing theory of involvement propounds consumers to be applying cognitive effort to their decision-making process for acquisition of products perceived to be of greater importance. Following the theory, Schmeckt Gut is recommended to conduct a detailed survey on its consumers or target audience understanding the important elements they perceive should be added to energy bars. Such involvement of consumers in developing of products and decision-making will not only help build strong consumer relation with the brand but will also help align the organizations goal with its end user. Nonetheless, recommendation is made to consider having a larger number of specialized products each target a different set of audience, rather than loading all features into one product, as that not only affects the quality but also question its usability among consumers, hampering maximization of their long-term satisfaction (Thompson, Hamilton, Rust, 2005). To conclude, the report was limited to one parameter- weight of the energy bars in understanding consumer relationship, which if been wholistic would have contributed in making the recommendations more practically applicable and in-depth. Herein lies the future scope of report where in-depth studies on consumer perception on various important and not-so-important parameters can be studied along with effect of satisfaction of different sub-categories of energy bars on concerned target audience. Such detail will enable the board to develop effective decisions. References Fishburn, P. C. (1989). Retrospective on the Utility Theory of von Neumann and Morgenstern. Journal of Risk and Uncertainty, 2, 127158. Retrieved from https://link.springer.com/article/10.1007/BF00056134 Kahneman, D., Tversky, A. (1979). Prospect Theory: An Analysis of Decision Under Risk. Econometrica, 2, 263. Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1505880 Rodgers, J. L., Nicewander, W. A. (1988). Thirteen Ways to Look at the Correlation Coefficient. The American Statistician, 42(1), 5966. Retrieved from https://www.stat.berkeley.edu/users/rabbee/correlation.pdf Schmitt, M., Martignon, L. (2006). On the Complexity of Learning Lexicographic Strategies. Journal of Machine Learning Research, 7, 5583. Retrieved from https://jmlr.org/papers/volume7/schmitt06a/schmitt06a.pdf Thompson, D. V., Hamilton, R. W., Rust, R. T. (2005). Feature Fatigue: When Product Capabilities Become Too Much of a Good Thing. Journal of Marketing Research, 42(November), 431442. Retrieved from https://www.rhsmith.umd.edu/files/Documents/Faculty/FeatureFatigueWhenProductCapabilitiesBecomeTooMuchOfAGoodThing.pdf