Analysis of Consumer Value Using Semantic Network: The Comparison of Hierarchical and Nonhierarchical Value Structures

This study compares the value structure of consumers derived from a nonhierarchical method that only requires grouping similar components and a hierarchical method that needs additional steps to classify components into the levels of abstraction, using semantic network analysis. The overall process of understanding consumers' value structure consists of data collection, data structuring, and network analysis using UCINET 6.0. A case study was conducted to identify the value structure of teenage Internet use behavior. Based on the relative ranking of words with the smallest farness from others, the nonhierarchical method showed "beauty" as the key value of teenagers, while the hierarchical method revealed "warm relationship" as the critical value in their use of the Internet. This nonhierarchical method showed the ability to elicit more diverse values, depending on the characteristics of consumer groups when compared with conventional hierarchical method. © 2016 Wiley Periodicals, Inc.

[1]  Minna Pura Linking perceived value and loyalty in location‐based mobile services , 2005 .

[2]  Roger J. Calantone,et al.  Internationalization and the Dynamics of Product Adaptation—An Empirical Investigation , 2004 .

[3]  Anthony Bonato,et al.  A course on the Web graph , 2008 .

[4]  Hakil Moon,et al.  Product Design Innovation and Customer Value: Cross‐Cultural Research in the United States and Korea , 2013 .

[5]  Jonathan Cagan,et al.  Creating Breakthrough Products: Innovation from Product Planning to Program Approval , 2001 .

[6]  Jerry C. Olson,et al.  Consumer Behavior and Marketing Strategy , 1990 .

[7]  L. Freeman Centrality in social networks conceptual clarification , 1978 .

[8]  T. J. Reynolds,et al.  A hard look at hard laddering , 2009 .

[9]  Gordon Rugg,et al.  Eliciting information about organizational culture via laddering , 2002, Inf. Syst. J..

[10]  P. Nedungadi Recall and Consumer Consideration Sets: Influencing Choice without Altering Brand Evaluations , 1990 .

[11]  T. J. Reynolds,et al.  Consumer Understanding and Advertising Strategy: Analysis and Strategic Translation of Laddering Data , 2001 .

[12]  D. MacInnis,et al.  Characteristics of portrayed emotions in commercials: when does what is shown in ads affect viewers? , 1995 .

[13]  Ali A. Yassine,et al.  Evaluating product development systems using network analysis , 2009 .

[14]  Klaus Krippendorff,et al.  Content Analysis: An Introduction to Its Methodology , 1980 .

[15]  Adam Sagan,et al.  The quality of ladders generated by abbreviated hard laddering , 2010 .

[16]  L. Freeman,et al.  The Development of Social Network Analysis: A Study in the Sociology of Science , 2005 .

[17]  Elizabeth Cowley,et al.  The Moderating Effect of Product Knowledge on the Learning and Organization of Product Information , 2003 .

[18]  Amitav Chakravarti,et al.  Knowing Too Much: Expertise-Induced False Recall Effects in Product Comparison , 2011 .

[19]  Tânia Modesto Veludo-de-Oliveira,et al.  Discussing Laddering Application by the Means-End Chain Theory , 2006 .

[20]  E. Hirschman,et al.  The Experiential Aspects of Consumption: Consumer Fantasies, Feelings, and Fun , 1982 .

[21]  H. H. Kassarjian Content Analysis in Consumer Research , 1977 .

[22]  Rik Pieters,et al.  Using means‐end structures for benefit segmentation: An application to services , 1999 .

[23]  M. Holbrook Consumer Value: A Framework for Analysis and Research , 1999 .

[24]  M. Wedel,et al.  An investigation into the association pattern technique as a quantitative approach to measuring means-end chains , 1998 .

[25]  Leslie H. Vincent,et al.  Objective and Subjective Knowledge Relationships: A Quantitative Analysis of Consumer Research Findings , 2009 .

[26]  P Leppard,et al.  Improving means-end-chain studies by using a ranking method to construct hierarchical value maps , 2004 .

[27]  T. J. Reynolds,et al.  Laddering theory, method, analysis, and interpretation. , 2001 .

[28]  V. Zeithaml Consumer Perceptions of Price, Quality, and Value: A Means-End Model and Synthesis of Evidence: , 1988 .

[29]  P. Bonacich Factoring and weighting approaches to status scores and clique identification , 1972 .

[30]  John-Paul Hatala Social Network Analysis in Human Resource Development: A New Methodology , 2006 .

[31]  Stephen P. Borgatti,et al.  Centrality and network flow , 2005, Soc. Networks.

[32]  K. Grunert,et al.  Measuring subjective meaning structures by the laddering method: Theoretical considerations and methodological problems , 1995 .

[33]  I. G. Saura,et al.  Value dimensions, perceived value, satisfaction and loyalty: an investigation of university students’ travel behaviour , 2006 .

[34]  Chin‐Feng Lin,et al.  Attribute-consequence-value linkages: A new technique for understanding customers' product knowledge , 2002 .

[35]  Ronald J. Brachman,et al.  What's in a Concept: Structural Foundations for Semantic Networks , 1977, Int. J. Man Mach. Stud..

[36]  Aaron Smith,et al.  Social Media & Mobile Internet Use among Teens and Young Adults. Millennials. , 2010 .

[37]  M. Holbrook,et al.  The value of value: Further excursions on the meaning and role of customer value , 2011 .

[38]  H. Bernard,et al.  Words as Actors: A Method for Doing Semantic Network Analysis , 1996 .

[39]  J R Anderson,et al.  Retrieval of information from long-term memory. , 1983, Science.

[40]  Kalpesh Kaushik Desai,et al.  Descriptive Characteristics of Memory-Based Consideration Sets: Influence of Usage Occasion Frequency and Usage Location Familiarity , 2000 .

[41]  M. Rokeach The Nature Of Human Values , 1974 .

[42]  Laura M. Koehly,et al.  Social Network Analysis: A New Methodology for Counseling Research. , 1998 .

[43]  Byungun Yoon,et al.  A text-mining-based patent network: Analytical tool for high-technology trend , 2004 .

[44]  Leonard D. Goodstein,et al.  Measuring customer value: Gaining the strategic advantage , 1996 .

[45]  Kaisa Väänänen-Vainio-Mattila,et al.  Value of Information Systems and Products: Understanding the Users’ Perspective and Values , 2009 .

[46]  Clement Mok,et al.  Designing Business: Multiple Media, Multiple Disciplines , 1996 .

[47]  John Scott Social Network Analysis , 1988 .

[48]  Brian Wansink,et al.  New Techniques to Generate Key Marketing Insights , 2000 .

[49]  G. Bower,et al.  A propositional theory of recognition memory , 1974, Memory & cognition.

[50]  Jerry C. Olson,et al.  Means-end chains: Connecting products with self , 1991 .

[51]  R. Wood,et al.  Managing customer value : creating quality and service that customers can see , 1994 .

[52]  Rob Cross,et al.  A Relational View of Information Seeking and Learning in Social Networks , 2003, Manag. Sci..

[53]  Matthew. W. Spitzer,et al.  The Mind within the Net: Models of Learning, Thinking, and Acting , 1999 .

[54]  Tânia Modesto Veludo-de-Oliveira,et al.  Laddering in the practice of marketing research: barriers and solutions , 2006 .

[55]  Jason M. Carpenter Consumer shopping value, satisfaction and loyalty in discount retailing , 2008 .

[56]  R. B. Woodruff,et al.  Know Your Customer: New Approaches to Understanding Customer Value and Satisfaction , 1996 .