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Product Testing of Fruit-Flavored Soft Drinks at Fresh Cola through Experimental Designs

Abstract

Fresh Cola, as a third-tier non-alcoholic carbonated beverage company, produces and markets carbonated soft drinks and bottled water. In the meeting of the board of governors of the company, it was unanimously decided that Fresh Cola must consider entering the non-alcoholic fruit-flavored beverage market segment, considering the huge market potential of this segment. The board also suggested that the company should develop standalone brand names for different fruit flavors. Accordingly, market research was commissioned to assess this opportunity further. The company hired a leading marketing research firm, IIRI, and briefed the concerns of the board to them. Later, through an exploratory investigation, IIRI identified the possibility of introducing three different varieties of fruit-flavored drinks to their product line. These flavors were apple, strawberry, and grape, with suggested brand names as Bravo, Delight, and Cool. Based upon these findings, IIRI decided to conduct four experiments to check the feasibility of introducing the new product into the market and assess its purchase likelihood among consumers. This case study is mainly centered on the organization and interpretation of these experimental designs.

This case was prepared for inclusion in Sage Business Cases primarily as a basis for classroom discussion or self-study, and is not meant to illustrate either effective or ineffective management styles. Nothing herein shall be deemed to be an endorsement of any kind. This case is for scholarly, educational, or personal use only within your university, and cannot be forwarded outside the university or used for other commercial purposes.

2026 Sage Publications, Inc. All Rights Reserved

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Resources

Click here to download the SPSS dataset for Experiment 1.

Click here to download the SPSS dataset for Experiment 2.

Click here to download the SPSS dataset for Experiment 3.

Appendix 1: Definitions

  • Alpha risk (level of significance): Researcher's prerogative of maximum permissible type 1 error.
  • Balanced design: An experimental design having equal number of observations for all possible combinations of factor levels.
  • Balanced factorial design: A balanced factorial design is a form of factorial design where all factor level combinations (cells) have equal sample sizes (see also factorial design).
  • Dependent variables: Variables that measure the effect of the independent variables on the respondents.
  • Design control: A method of controlling extraneous variable by the use of specific experimental designs.
  • Experiment: The process of manipulating a set of independent variables so as to measure their effect on one or more dependent variables, while controlling for the extraneous variables.
  • Experimental design: A set of procedures specifying (i) the test units and sampling procedures, (ii) independent and dependent variables, and (iii) extraneous variables and ways to control them.
  • Extraneous variables: All variables, other than the independent variables, that influence the response of the respondents.
  • Factorial design: A form of experimental design which consists of two or more treatments (also called factors), where each treatment is manipulated at different levels, and whose experimental units take on all possible combinations of these levels across all such factors.
  • Independent variables: Variables that can be manipulated by the researcher and whose effect are measured and compared.
  • Power of test: The probability of rejecting a null hypothesis when it is false.
  • Randomization: A method of controlling experimental biases that involves random assignment of test units to experimental groups or randomly assigning treatments to experimental groups or both.
  • Systematic random sampling: This involves randomly picking the first test unit (subject) for the experiment and selecting every nth person thereafter (walking out of the mall or from the list of test units provided).
  • Test units: Respondents, such as individuals, organizations, etc., whose response to independent variables (or treatments) are being studied.
  • Type 1 error: Occurs when the sample results lead to the rejection of null hypothesis which is true. Also known as alpha (risk) error.
  • Type 2 error: Occurs when the sample results lead to the non-rejection of the null hypothesis which is false. Also known as beta error.

Appendix 2: SPSS syntax for pairwise comparisons and interaction plots in factorial ANOVA (experiment 3)

  • UNIANOVA
  • Likelihood by Storetype Flavor Age
  • /METHOD =SSTYPE (3)
  • /INTERCEPT = INCLUDE
  • /DESIGN = Storetype Flavor Age Storetype* Flavor Storetype*Age Age*Flavor Storetype*Flavor*Age
  • /EMMEANS = TABLES (Age* Flavor*Storetype ) COMPARE (Flavor) ADJ(SIDAK)
  • /PLOT = PROFILE (Storetype*Flavor*Age)
  • /CRITERIA = ALPHA(.05).

This case was prepared for inclusion in Sage Business Cases primarily as a basis for classroom discussion or self-study, and is not meant to illustrate either effective or ineffective management styles. Nothing herein shall be deemed to be an endorsement of any kind. This case is for scholarly, educational, or personal use only within your university, and cannot be forwarded outside the university or used for other commercial purposes.

2026 Sage Publications, Inc. All Rights Reserved

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