• Journal of Global Scholars of Marketing Science
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• v.17 no.2
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• pp.1-29
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• 2007

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.

• Korean Journal of Mineralogy and Petrology
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• v.35 no.2
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• pp.83-100
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• 2022

The Zhenzigou Pb-Zn deposit, which is one of the largest Pb-Zn deposit in the northeast of China, is located at the Qingchengzi mineral field in Jiao Liao Ji belt. The geology of this deposit consists of Archean granulite, Paleoproterozoinc migmatitic granite, Paleo-Mesoproterozoic sodic granite, Paleoproterozoic Liaohe group, Mesozoic diorite and Mesozoic monzoritic granite. The Zhenzigou deposit which is a strata bound SEDEX or SEDEX type deposit occurs as layer ore and vein ore in Langzishan formation and Dashiqiao formation of the Paleoproterozoic Liaohe group. White mica from this deposit are occured only in layer ore and are classified four type (Type I : weak alteration (clastic dolomitic marble), Type II : strong alteration (dolomitic clastic rock), Type III : layer ore (dolomitic clastic rock), Type IV : layer ore (clastic dolomitic marble)). Type I white mica in weak alteration zone is associated with dolomite that is formed by dolomitization of hydrothermal metasomatism. Type II white mica in strong alteration zone is associated with dolomite, ankerite, quartz and alteration of K-feldspar by hydrothermal metasomatism. Type III white mica in layer ore is associated with dolomite, ankerite, calcite, quartz and alteration of K-feldspar by hydrothermal metasomatism. And type IV white mica in layer ore is associated with dolomite, quartz and alteration of K-feldspar by hydrothermal metasomatism. The structural formulars of white micas are determined to be (K_0.92-0.80Na_0.01-0.00Ca_0.02-0.01Ba_0.00Sr_0.01-0.00)_0.95-0.83(Al_1.72-1.57Mg_0.33-0.20Fe_0.01-0.00Mn_0.00Ti_0.02-0.00Cr_0.01-0.00V_0.00Sb_0.02-0.00Ni_0.00Co_0.02-0.00)_1.99-1.90(Si_3.40-3.29Al_0.71-0.60)_4.00O₁₀(OH_2.00-1.83F_0.17-0.00)_2.00, (K_1.03-0.84Na_0.03-0.00Ca_0.08-0.00Ba_0.00Sr_0.01-0.00)_1.08-0.85(Al_1.85-1.65Mg_0.20-0.06Fe_0.10-0.03Mn_0.00Ti_0.05-0.00Cr_0.03-0.00V_0.01-0.00Sb_0.02-0.00Ni_0.00Co_0.03-0.00)_1.99-1.93(Si_3.28-2.99Al_1.01-0.72)_4.00O₁₀(OH_1.96-1.90F_0.10-0.04)_2.00, (K_1.06-0.90Na_0.01-0.00Ca_0.01-0.00Ba_0.00Sr_0.02-0.01)_1.10-0.93(Al_1.93-1.64Mg_0.19-0.00Fe_0.12-0.01Mn_0.00Ti_0.01-0.00Cr_0.01-0.00V_0.00Sb_0.00Ni_0.00Co_0.05-0.01)_2.01-1.94(Si_3.32-2.96Al_1.04-0.68)_4.00O₁₀(OH_2.00-1.91F_0.09-0.00)_2.00 and (K_0.91-0.83Na_0.02-0.01Ca_0.02-0.00Ba_0.01-0.00Sr_0.00)_0.93-0.83(Al_1.84-1.67Mg_0.15-0.08Fe_0.07-0.02Mn_0.00Ti_0.04-0.00Cr_0.06-0.00V_0.02-0.00Sb_0.02-0.01Ni_0.00Co_0.00)_2.00-1.92(Si_3.27-3.16Al_0.84-0.73)_4.00O₁₀(OH_1.97-1.88F_0.12-0.03)_2.00, respectively. It indicated that white mica of from the Zhenzigou deposit has less K, Na and Ca, and more Si than theoretical dioctahedral mica. Compositional variations in white mica from the Zhenzigou deposit are caused by phengitic or Tschermark substitution [(Al³⁺)^VI+(Al³⁺)^IV <-> (Fe²⁺ or Mg²⁺)^VI+(Si⁴⁺)^IV] substitution. It means that the Fe in white mica exists as Fe²⁺ and Fe³⁺, but mainly as Fe²⁺. Therefore, white mica from layer ore of the Zhenzigou deposit was formed in the process of remelting and re-precipitation of pre-existed minerals by hydrothermal metasomatism origined metamorphism (greenschist facies) associated with Paleoproterozoic intrusion. And compositional variations in white mica from the Zhenzigou deposit are caused by phengitic or Tschermark substitution [(Al³⁺)^VI+(Al³⁺)^IV <-> (Fe²⁺ or Mg²⁺)^VI+(Si⁴⁺)^IV] substitution during hydrothermal metasomatism depending on wallrock type, alteration degree and ore/gangue mineral occurrence frequency.