플로우 4경로모형의 마음상태와 플레이(play)

State of Mind in the Flow 4-Channel Model and Play

본 연구에서는 플로우 4경로모형에서 마음상태와 플레이(play)가 하는 역할과 그 결과를 분석하고자 하였다. 이를 위해서 선행요인인 도전감 및 숙련도의 조합에 따른 마음상태를 측정하고, 플레이를 플로우 4경로모형에 투입하여 플로우이론에서 가설적으로 제시하고 있는 마음상태와 플레이의 영향관계를 분석하였다. 또한 플로우와 플레이의 웹충성도에 대한 영향도 분석하였다. 가설검정 결과에서는 첫째, 도전감과 숙련도의 조합에 따라 플로우, 두려움, 지루함, 무관심의 마음상태가 형성되는 것이 확인되었다. 그러나 두려움의 수준은 도전감과 숙련도가 모두 가장 낮은 무관심집단에서 가장 높게 나타났다. 이런 결과는 플로우이론의 설명과 일치하지 않는데, 무관심집단은 두려움으로 인해 온라인 쇼핑을 회피하는 것으로 해석할 수 있다. 둘째, 도전감과 숙련도에 따라 구분된 집단 간에 플레이 수준에서 유의적인 차이가 있는 것으로 나타났다. 셋째, 플레이는 플로우에 대해서는 정(+)의 영향을 미쳤고, 지루함에 대해서는 부(-)의 영향을 미쳤다. 그러나 두려움과 무관심에 대해서는 부(-)의 영향효과가 유의적이지 않았다. 넷째, 플레이와 플로우는 웹충성도에 유의적인 정(+)의 영향을 미치는 것으로 나타났고, 부정적 마음상태인 두려움, 지루함, 무관심은 웹충성도에 부(-)의 영향을 미쳤다. 플레이의 웹충성도에 대한 영향은 부정적 마음상태에서 강화되는 것으로 나타났다. 본 연구에서는 플로우 4경로모형에서의 마음상태를 확인하기 위해 이를 측정할 수 있는 척도를 개발하여 사용하였다. 마음상태별로 수립한 4개의 구조방정식 모형을 통해 플로우 뿐만 아니라 두려움, 지루함, 무관심의 부정적 마음상태에서 발생하는 영향관계를 종합적으로 입증하였다. 이런 결과는 부정적 마음상태의 영향을 확인하였다는 점에서 이론발전에 기여하였다고 본다. 또한, 플로우모형에서 플레이의 역할을 규명하였다는 점에서도 의미가 있다. 본 연구는 실무적으로도 인터넷 소비자들의 마음상태에 따른 시장세분화와 플레이를 활용한 마케팅전략수립에 시사점을 제공한다.

The flow theory becomes one of the most important frameworks in the internet research arena. Hoffman and Novak proposed a hierarchical flow model showing the antecedents and outcomes of flow and the relationship among these variables in the hyper-media computer circumstances (Hoffman and Novak 1996). This model was further tested after their initial research (Novak, Hoffman, and Yung 2000). At their paper, Hoffman and Novak explained that the balance of challenge and skill leads to flow which means the positive optimal state of mind (Hoffman and Novak 1996). An imbalance between challenge and skill, leads to negative states of mind like anxiety, boredom, apathy (Csikszentmihalyi and Csikszentmihalyi 1988). Almost all research on the flow 4-channel model have been focusingon flow, the positive state of mind (Ellis, Voelkl, and Morris 1994 Mathwick and Rigdon 2004). However, it also needs to examine the formation of the negative states of minds and their outcomes. Flow researchers explain play or playfulness as antecedents or the early state of flow. However, play has been regarded as a distinct concept from flow in the flow literatures (Hoffman and Novak 1996; Novak, Hoffman, and Yung 2000). Mathwick and Rigdon discovered the influences of challenge and skill on play; they also observed the influence of play on web-loyalty and brand loyalty (Mathwick and Rigdon 2004). Unfortunately, they did not go so far as to test the influences of play on state of mind. This study focuses on the relationships between state of mind in the flow 4-channel model and play. Early research has attempted to hypothetically explain state of mind in flow theory, but has not been tested except flow until now. Also the importance of play has been emphasized in the flow theory, but has not been tested in the flow 4-channel model context. This researcher attempts to analyze the relationships among state of mind, skill of play, challenge, state of mind and web loyalty. For this objective, I developed a measure for state of mind and defined the concept of play as a trait. Then, the influences of challenge and skill on the state of mind and play under on-line shopping conditions were tested. Also the influences of play on state of mind were tested and those of flow and play on web loyalty were highlighted. 294 undergraduate students participated in this research survey. They were asked to respond about their perceptions of challenge, skill, state of mind, play, and web-loyalty to on-line shopping mall. Respondents were restricted to students who bought products on-line in a month. In case of buying products at two or more on-line shopping malls, they asked to respond about the shopping mall where they bought the most important one. Construct validity, discriminant validity, and convergent validity were used to check the measurement validations. Also, Cronbach's alpha was used to check scale reliability. A series of exploratory factor analyses was conducted. This researcher conducted confirmatory factor analyses to assess the validity of measurements. All items loaded significantly on their respective constructs. Also, all reliabilities were greater than.70. Chi-square difference tests and goodness of fit tests supported discriminant and convergent validity. The results of clustering and ANOVA showed that high challenge and high skill leaded to flow, low challenge and high skill leaded to boredom, and low challenge and low skill leaded to apathy. But, it was different from my expectation that high challenge and low skill didnot lead to anxiety but leaded to apathy. The results also showed that high challenge and high skill, and high challenge and low skill leaded to the highest play. Low challenge leaded to low play. 4 Structural Equation Models were built by flow, anxiety, boredom, apathy for analyzing not only the impact of play on state of mind and web-loyalty, but also that of state of mind on web-loyalty. According the analyses results of these models, play impacted flow and web-loyalty positively, but impacted anxiety, boredom, and apathy negatively. Results also showed that flow impacted web-loyalty positively, but anxiety, boredom, and apathy impacted web-loyalty negatively. The interpretations and implications of the test results of the hypotheses are as follows. First, respondents belonging to different clusters based on challenge and skill level experienced different states of mind such as flow, anxiety, boredom, apathy. The low challenge and low skill group felt the highest anxiety and apathy. It could be interpreted that this group feeling high anxiety or fear, then avoided attempts to shop on-line. Second, it was found that higher challenge leads to higher levels of play. Test results show that the play level of the high challenge and low skill group (anxiety group) was higher than that of the high challenge and high skill group (flow group). However, this was not significant. Third, play positively impacted flow and negatively impacted boredom. The negative impacts on anxiety and apathy were not significant. This means that the combination of challenge and skill creates different results. Forth, play and flow positively impacted web-loyalty, but anxiety, boredom, apathy had negative impacts. The effect of play on web-loyalty was stronger in case of anxiety, boredom, apathy group than fl ow group. These results show that challenge and skill influences state of mind and play. Results also demonstrate how play and flow influence web-loyalty. It implies that state of mind and play should be the core marketing variables in internet marketing. The flow theory has been focusing on flow and on the positive outcomes of flow experiences. But, this research shows that lots of consumers experience the negative state of mind rather than flow state in the internet shopping circumstance. Results show that the negative state of mind leads to low or negative web-loyalty. Play can have an important role with the web-loyalty when consumers have the negative state of mind. Results of structural equation model analyses show that play influences web-loyalty positively, even though consumers may be in the negative state of mind. This research found the impacts of challenge and skill on state of mind in the flow 4-channel model, not only flow but also anxiety, boredom, apathy. Also, it highlighted the role of play in the flow 4-channel model context and impacts on web-loyalty. However, tests show a few different results from hypothetical expectations such as the highest anxiety level of apathy group and insignificant impacts of play on anxiety and apathy. Further research needs to replicate this research and/or to compare 3-channel model with 4-channel model.