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Statistical Analysis Of Cronbach's Alpha Values

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Statistical Analysis Of Cronbach's Alpha Values
First of all, reliability of 7 indicator variables was calculated by SPSS (IBM Version 24). Table 3 presented the Cronbach’s Alpha which represent the reliability of survey results. All of Cronbach’s Alpha values are more than 0.7 which are in the recommended acceptable range (Nunnally and Bernstein, 2010). Values above 0.8 can be considered as very good values (Nunnally and Bernstein, 2010).

Indicator variables Cronbach’s Alpha
Subjective Norm 0.821
Image 0.717
Entertainment Relevance 0.846
Output Quality of Entertainment 0.862
Result Demonstrability 0.858
Perceived Ease of Use 0.772
Use Behaviour 0.881
Table 3: Reliability Test Results of Indicator Variables

Moreover, regression analyses of collected data were completed by SPSS software
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According to the theory of exploratory data analysis which published by John Tukey, for analysing unstructured data, statistical analysis should focus on exploring phenomena behind models (Tukey, 1992). Thus, apart from testing the model by software, factors will be analysed and discussed by the contrastive or cross-over analysis approaches to weight the importance of them.

Subjective Norm
As a theoretical foundation of TAM2, Subjective Norm can directly determine the Behavioural Intention (WU et al., 2011). The Impact of behaviours after received suggestions from key stakeholders was investigated to estimate the importance of this factor. Among 237 participants, around 72.2% people disagreed or strongly disagreed that suggestions from their friends, families or colleagues will stop them to use BI services in an online game. In addition, only 28% participants agreed or strongly agreed that they will use BI services. It can deduce that selection and abandonment of a game service are subjective behaviours which is not principally dependent on opinions of other individuals. In the online game, voluntariness can only provide a positive regulation to this
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Intelligent marketing, game modification, social media synchronisation and AI robot services were set into 4 questions by following the theory in last section. The AI robot service is an innovative interaction mode. After learning user behaviours, user can acquire game experience by interact with AI robots (Knight, 2017). According to Figure 15, the satisfaction of each question cannot significantly reflect the Output Quality of Entertainment is an important factor. However, in Figure 16, by cross-analysing these four questions, over 88% participants agreed or strongly agreed that if BI services can help them effectively achieve entertainments, they will accept it. Thus, it can deduce that users have different expectations and requirements of

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