Ranking of choice cues for smartphones using the Best–Worst scaling method

The number of studies on the use of choice cues in the purchase decision of a smartphone does not appear to be extensive, given the size and rate of growth of the market. Surprisingly, it appears that no study of this type in the Chinese context has been undertaken. Therefore, the purpose of this paper is to fill the existing gap in the marketing literature in this area.,Best–Worst (BW) scaling method was used in the study. It is suggested that the method overcomes some of the biases commonly found in surveys where Likert-type scales are used, and it has superior discriminating power, because respondents are asked to rank the most and the least important factor from a group, and are thereby forced to make tradeoffs between factors.,Among the 13 choice cues, connectivity, price and memory capacity are found to be the most important, whereas recommendation from others, ease of handling and availability of apps are found to be the least important. Findings due to gender, income and age difference were also analyzed and discussed for orderly decision-making purposes.,The ranking of factors showing what choice cues consumers consider most or least important in a particular market helps practitioners to develop appropriate adaptation strategies for the market. The comparison of findings for gender, income and age difference can further help practitioners to devise various alternative marketing strategies for different market segments and identify underserved segments, if any.,The BW scaling method, however, appropriate in ranking order of importance, had never been used in ranking choice cues of smartphone purchase. Moreover, there seems to be a dearth of studies about ranking of choice cues on smartphone purchases in the Chinese context.

[1]  T. Peters,et al.  Best--worst scaling: What it can do for health care research and how to do it. , 2007, Journal of health economics.

[2]  Paul Wang,et al.  Using best-worst scaling method to examine consumers’ value preferences: A multidimensional perspective , 2014 .

[3]  Armand V. Cardello,et al.  Direct and indirect hedonic scaling methods: A comparison of the labeled affective magnitude (LAM) scale and best–worst scaling , 2009 .

[4]  Rosemary R. Seva,et al.  The influence of cellular phone attributes on users' affective experiences: a cultural comparison. , 2009 .

[5]  Jordan J. Louviere,et al.  An introduction to the application of (case 1) best–worst scaling in marketing research , 2013 .

[6]  Carl Johan Lagerkvist,et al.  Consumer preferences for food labelling attributes: Comparing direct ranking and best–worst scaling for measurement of attribute importance, preference intensity and attribute dominance , 2013 .

[7]  An Investigation of Customers to Explain the Purchase Intentions for Expensive Mobile Phone , 2013 .

[8]  Jordan J. Louviere,et al.  Best-Worst Scaling: Theory, Methods and Applications , 2015 .

[9]  J. Louviere,et al.  A comparison of importance weights and willingness-to-pay measures derived from choice-based conjoint, constant sum scales and best-worst scaling , 2008 .

[10]  Jordan J. Louviere,et al.  Measuring values using best‐worst scaling: The LOV example , 2007 .

[11]  S. Jaeger,et al.  Best–worst scaling: An introduction and initial comparison with monadic rating for preference elicitation with food products , 2008 .

[12]  Jagwinder Singh,et al.  Mobile Handset Buying Behavior of Different Age and Gender Groups , 2009 .

[13]  Lucie Sirieix,et al.  A cross-cultural comparison of choice criteria for wine in restaurants , 2009 .

[14]  L. Lockshin,et al.  Building brand salience for commodity‐based wine regions , 2009 .

[15]  Steven H. Cohen Maximum Difference Scaling: Improved Measures of Importance and Preference for Segmentation , 2003 .

[16]  Eli Cohen,et al.  Applying best‐worst scaling to wine marketing , 2009 .

[17]  A. Marley,et al.  Best-worst scaling: theory and methods , 2014 .

[18]  L. Thurstone A law of comparative judgment. , 1994 .

[19]  Attila Endre Simay Mobile phone usage and device selection of university students , 2009 .

[20]  Harry T. Lawless,et al.  Sensory Evaluation of Food: Principles and Practices , 1998 .

[21]  J. Louviere,et al.  Determining the Appropriate Response to Evidence of Public Concern: The Case of Food Safety , 1992 .

[22]  Peter C. Verhoef,et al.  Expanding Business-to-Business Customer Relationships: Modeling the Customer's Upgrade Decision , 2008 .

[23]  Karen Lim Lay-Yee,et al.  FACTORS AFFECTING SMARTPHONE PURCHASE DECISION AMONG MALAYSIAN GENERATION Y , 2013 .

[24]  Mesay Sata,et al.  Factors Affecting Consumer Buying Behavior of Mobile Phone Devices , 2013 .

[25]  Mithilesh Pandey,et al.  Consumer Preference Towards Smartphone Brands, with Special Reference to Android Operating System , 2015 .

[26]  Paul Wang,et al.  Best–worst scaling: A new method for advertisement evaluation , 2015 .

[27]  Tripat Gill,et al.  Convergent Products: What Functionalities Add More Value to the Base? , 2008 .

[28]  J. Louviere,et al.  Discrete Choice Experiments Are Not Conjoint Analysis , 2010 .

[29]  Raquel Olivas,et al.  Consumer preferences for wine applying best‐worst scaling: a Spanish case study , 2012 .

[30]  Sara R. Jaeger,et al.  Comparison of five common acceptance and preference methods , 2008 .

[31]  Consumer preference towards mobile phones: An empirical analysis , 2016 .

[32]  Michel Laroche,et al.  Which decision heuristics are used in consideration set formation , 2003 .

[33]  D. Paulhus Measurement and control of response bias. , 1991 .

[34]  J. Louviere,et al.  Some probabilistic models of best, worst, and best–worst choices , 2005 .

[35]  J. Salo,et al.  Factors Affecting Consumer Choice of Mobile Phones: Two Studies from Finland , 2005 .

[36]  Steve Goodman,et al.  An international comparison of retail consumer wine choice , 2009 .

[37]  A. A. J. Marley,et al.  The best-worst method for the study of preferences: Theory and Application , 2010 .

[38]  D. McFadden Conditional logit analysis of qualitative choice behavior , 1972 .

[39]  Neelotpaul Banerjee,et al.  A Comparative Study on Factors Affecting Consumer’s Choice on Purchasing a Cellular Phone across India & US , 2016 .

[40]  Reaz Uddin,et al.  FACTORS AFFECTING CUSTOMERS ' BUYING DECISIONS OF MOBILE PHONE : A STUDY ON KHULNA CITY , BANGLADESH , 2014 .

[41]  Surachman,et al.  An Empirical Internal Perceptions Study of the Implementation Supply Chain Management in Indonesian Manufacturing Companies As a Mediating Factor of Information Technology Utilization to Organization Performances , 2013 .

[42]  Sanjeev K. Bansal,et al.  An Investigative Study of the Mobile Operating System and Handset Preference , 2016 .

[43]  Matti J. Haverila,et al.  Cell phone product-market segments using product features as a cluster variate: a multi-country study , 2013 .

[44]  Roland T. Rust,et al.  Feature Fatigue: When Product Capabilities Become Too Much of a Good Thing , 2005 .

[45]  Personal Factors Influencing Consumers' Buying Decision of Mobile Phone: A Case of Ethiopia, Dilla City , 2015 .

[46]  Amit Bhattacharyya Differential Voltage Current Conveyor Based One-Shot Pulse Generator Circuit Implementation , 2016 .

[47]  N. Mramba Does the Brand Name Matter to Purchase Decision? The Case of Mobile Phone , 2015 .

[48]  Matti J. Haverila Mobile Phone Feature Preferences, Customer Satisfaction and Repurchase Intent among Male Users , 2011 .

[49]  Norazah Mohd Suki,et al.  Students demand for smartphones structural relationships of product features, brand name, product price and social influence , 2013 .

[50]  Abhik Roy,et al.  Chinese puzzles and paradoxes: conducting business research in China , 2001 .

[51]  P. Ganesan,et al.  Smart Phone Attribute Choice and Brand Importance for Millennial Customers , 2014 .

[52]  Md. Zillur Rahman Siddique,et al.  Product Features Affecting Buying Decision for Mobile Phone Handset: A Study on Tertiary Students Segment in Bangladesh , 2013 .

[53]  V. A. Nasir,et al.  Discovering behavioral segments in the mobile phone market , 2010 .