Elsevier

Journal of Business Research

Volume 64, Issue 11, November 2011, Pages 1195-1200
Journal of Business Research

Consumer responses to high-technology products: Product attributes, cognition, and emotions

https://doi.org/10.1016/j.jbusres.2011.06.022Get rights and content

Abstract

The present study investigates how high-technology attributes influence consumer responses. Based on Mehrabian and Russell's Stimulus–Organism–Response (S–O–R) framework (1974), this study proposes that high-technology product attributes elicit consumers' cognitive (attitude) and affective states (pleasure and arousal), contributing to their approach–avoidance behavior. High-technology product users (N = 408) participated in surveys. The results provide support for the model. Among six factors of high-technology product attributes (usefulness, ease of use, innovativeness of technology, visual appeal, prototypicality, and self-expression), the latter four have major influences on approach behavior through attitude (cognitive state) and pleasure (affective state). Supplemental analysis shows that attitude and pleasure influence approach–avoidance behavior directly, but that arousal affects approach–avoidance behavior indirectly via pleasure.

Research highlights

► We model that high-technology attributes elicit cognitive and affective states, predicting approach-avoidance behavior. ► Four attributes (innovativeness, visual appeal, prototypicality, and self-expression) have impact on approach behavior. ► Attitude and pleasure influence approach-avoidance behavior. ► Arousal affects approach-avoidance behavior indirectly via pleasure.

Introduction

Attributes of a product contribute to the success of product marketing (e.g., Meyers-Levy and Tybout, 1989). Technology products are “products that are the result of technology and which require substantial shifts in behavior of at least one member of the product usage channel” (Gardner, Johnson, Lee, and Wilkinson, 2000, p. 1053). Examples of technology products include, among many, mobile phones, PDAs, netbooks, high-tech TVs, e-readers, and GPS devices. Compared to other products, technology products tend to have short product life cycles (Riggs, 1983) and provide consumers with noteworthy changes in such product functions as technology-driven functions, designs, and/or services (Gardner et al., 2000). Technology products, typified by convergence, also require a technology-enabled functionality, which provides the product with manifold qualities (Gill and Lei, 2009). Accordingly, consumers evaluate certain functionalities of the product differently, and have thus different attitudinal and behavioral responses (Hong and Wyer, 1998, Ko et al., 2008).

The continual growth of the high-technology product marketplace is evident irrespective of the sluggish economy. According to Packaged Facts (2006), women in the U.S. spend $55 billion annually for technology purchases, and experience significant changes in their lives due to technology. Given the rapidly evolving technology and ceaseless development of new technology products, a key for success in the high-technology product business is to improve current knowledge about users' behaviors, particularly in response to multiple functions of technology products (Cooper and Kleinschmidt, 2000).

Previous marketing research of technology product attributes has a few distinctive patterns. First, many studies focus on a few attributes which mainly pertain to performance functions (e.g., price, brand, quality) (Chang and Wildt, 1994, Nowlis and Simonson, 1996), leaving out other aspects such as design (appearance) and social qualities of technology products. Second, conceptual studies which develop the dimensionality of technology product attributes call for empirical examination (Horváth and Sajtos, 2002, Rindova and Petkova, 2007). Third, prior research mostly examines outcomes of product attributes such as brand choice (Nowlis and Simonson, 1996), preference persistence (Muthukrishnan and Kardes, 2001), purchase intention (Chang and Wildt, 1994, Ko et al., 2008), and value (Gill and Lei, 2009), thereby not providing an understanding of the psychological process underlying the relationship between product attributes and consumer behaviors.

The current study aims to extend and complement existing research by: (1) identifying a comprehensive set of technology product attributes that play a role in consumer adoption of technology products, and (2) investigating the underlying process whereby product attributes influence consumer behavior (approach–avoidance behavior) based on the Mehrabian and Russell (1974).

Section snippets

Mehrabian and Russell's Stimulus–Organism–Response (S–O–R) framework

This study builds on Stimulus–Organism–Response (S–O–R) framework (Mehrabian and Russell, 1974) proposing that when an individual encounters a stimulus (S), he/she develops internal states (O), which in turn dictate his/her responses (R) (Mehrabian and Russell, 1974). That is, stimuli (e.g., object stimuli and social psychological stimuli) develop individuals' cognitive and emotional states, which in turn determine behavioral responses of approach or avoidance. The validity of the model has

Method

This study used paper-and-pencil surveys to collect data. Potential participants recruited from undergraduate classes and public places (e.g., cafeteria, campus) located at a Midwestern university in the U.S. participated in surveys. Among those having agreed to participate in a survey, respondents who use a technology product took the survey. The questionnaire comprised five parts: (1) an introduction including the definition and examples of technology products, (2) technology product

Results

Among a total of 438 surveys gathered, listwise deletion remained 408 for data analysis. Over 70% of the sample ranged between 18 and 30 years old (90.0%) with the mean being 23.3 years old. Males comprised 50.2%. The majority was Caucasian (64.5%) and was pursuing or completed college degrees (86.5%).

Discussion and conclusions

This study entails several theoretical implications for understanding consumer responses to an ever-growing product category, high-technology products. First of all, research has widely applied Mehrabian and Russell's (1974) S–O–R framework in retailing (e.g., Donovan and Rossiter, 1982, Eroglu et al., 2001); empirical research has not applied the framework in the context of technology product use. By demonstrating the applicability of the S–O–R paradigm to this new consumer behavior context

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    The authors would like to thank the editor and reviewers for their careful review and constructive comments.

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