Undergraduates expressed their attitudes about a product after being exposed to a magazme ad under conditions of either high or low product involvement. The ad contained either strong or weak arguments for the product and featured either prominent sports celebrities or average citizens as endorsers. The manipulation of argument quality had a greater impact on attitudes under high than low involvement, but the manipulation of product endorser had a greater impact under low than high involvement. These results are consistent with the view that there are two relatively distinct routes to persuasion.

Two experiments examined the processes by which positive mood influences attitude change under high and low message elaboration conditions. To examine elaboration, Experiment 1 included individuals who differed in their need for cognition, and Experiment 2 manipulated the relevance of the message. In each study, Ss were exposed to a persuasive communication following a positive or neutral mood induction. In both studies, positive mood produced more positive attitudes toward the advocacy, but positive mood influenced the positivity of Ss' thoughts only under high-elaboration conditions. Path analyses showed that positive mood had a direct effect on attitudes in the low-elaboration conditions but influenced attitudes indirectly by modifying the positivity of thoughts in the high-elaboration conditions. Does a positive mood facilitate persuasion? Many of the initial studies addressing this question suggested that manipulations inducing a positive mood either just before or during a persuasive communication increased the likelihood of attitude change (e.g., Biggers & Pryor, 1982;Dribben & Brabender, 1979;Srull, 1983). These "main effect" findings were so consistent that in reviewing the work on positive affect and persuasion, McGuire (1985) concluded that Persuasive impact is greater if the person is in a happy, benevolent mood when the message comes, noshing on peanuts and soda (Janis, Kaye, and Kirschner, 1965), watching a good program (Krugman, 1983), and with pleasant musical background (Galizio and Hendrick, 1972), an appropriately scented other (Baron, 1983), a smile on one's face (Laird, 1974), nodding one's head (Wells and Petty, 1980), or relaxed in posture (Petty, Wells, Heesacker, Brock, and Cacioppo, 1983). (p. 285) Since 1985, however, the picture has become more complicated. In particular, recent studies have found that positive

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PurposeFor a new technology‐based product or service at an early stage of diffusion, it is likely that only a small subset of consumers have adopted it. When non‐adopters still comprise the majority of the target populations, describing all non‐adopters as a homogeneous population may be inaccurate and inappropriate. It is important to be able to identify differences, not only between adopters and non‐adopters, but also among non‐adopters, the latter providing a means of identifying the consumer segments likely to be profitable in the future. The first objective of this paper is to demonstrate the appropriateness of further segmenting the non‐adopter category based on motivations (intentions) to adopt internet‐banking by those who are “persistent non‐adopters” and those who are likely “prospective adopters”. The second objective of this paper is to further the understanding of the adoption process of internet banking by examining a range of diffusion factors that affect consumers’ adoption behavior.Design/methodology/approachThis study identifies three adopter segments, persistent non‐adopters, prospective adopters, and adopters, in the diffusion of internet banking services based on individuals’ intentions to adopt internet banking and their actual adoption behavior, using the data collected by the Graphic, Visualization, & Usability Center and Georgia Tech Research Corporation. Selective adoption factors (i.e. perceived attribute importance of internet banking, perceived risk, compatibility with existing banking services, and compatibility with the internet and computer technologies) were used to predict individuals’ adoption status through multinomial logit modeling.FindingsIt was found that further segmenting the non‐adopter category revealed meaningful differences between prospective adopters and persistent non‐adopters. This study reflects that the previous research practice of bifurcating adoption categories (adoption versus non‐adoption) is oversimplified. The two qualitatively different segments, prospective adopters and persistent non‐adopters, should not be lumped together in the diffusion of internet banking.Originality/valueThis study provides comparison profiles of the three adopter categories and discusses marketing implications for segmentation.

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