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\title{Identification of the Key Factors Affecting the Adoption of Mobile Phone Gaming: A Study in the Context of Bangladesh}
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             \author[1]{Md. Mostafizur  Rahman}

             \author[2]{Md.Farijul  Islam}

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\date{\small \em Received: 10 December 2013 Accepted: 31 December 2013 Published: 15 January 2014}

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\begin{abstract}
        


In recent years the use of mobile phones and smart phones have become more widespread and continue to grow significantly and almost half of the population of the earth now use mobile communications. The mobile telecommunications industry is a key driver of both economic and social development across the globe and it is one of the most transformational industries in the world today, enriching the daily lives of almost half of the world?s population and mobile device is becoming part and parcel in our daily life and plays a pivotal role in passing time and recreation device for people specially young generation. This study investigates the identification of the key factors affecting the adoption of mobile phone gaming in the context of Bangladesh. The nature of the research is conclusive in form of descriptive with the help of collecting primary data via personal face to face interview where non probability convenience sampling were used and primary data were analyzed by descriptive statistics, correlation, ANOVA, multiple regression, factor analysis (principal component analysis). The result of the study shows that all the variables are correlated with and they have significant influence on the adoption of mobile phone gaming. The main findings of the study reveals that first three factors easiness, usefulness, enjoyment are most influencing factors for mobile phone gaming adoption and subsequent factors are attitude, intention, behavior, and concentration. The contribution made by the study is give suggestions for the operators of mobile phone to consider the above mentioned factors while designing their market offerings for customers to get the maximum benefit by satisfying mobile phone gaming users in Bangladesh. Marketers should, however, carefully consider the key factors that influence the adoption and performance the operations of mobile phone gaming.

\end{abstract}


\keywords{mobile phone gaming, adoption, usefulness, personal attitude, concentration, telecommunication, 3G, smart phones.}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
ittle research has been carried out in the area of mobile phone industry in the context of Bangladesh. Although some researchers have paid attention in the area, it is an endeavor to investigate the factors that influence the adoption of mobile phone gaming. Recent years the use of mobile phones and smart phones has become more widespread and continues to grow significantly. The rapid growth of third generation (3G) mobile technology has radically changed the lifestyle of the young generations. 3G mobile technology incorporates voice data and Internet access, making smart phones similar to personal computers. The mobile telecommunications industry is a key driver of both economic and social development across the globe. It advances technology, enables new business models that could not have existed without it and improves the efficiency of existing ones, creating jobs in the process and providing an important source of funding for governments. It extends the reach of healthcare, education and finance in developing countries. In doing so, it connects us all more closely, allows us greater access to information and the world around us and empowers the vulnerable. The mobile industry is one of the most transformational industries in the world today, enriching the daily lives of almost half the world's population. Almost half the population of the earth now uses mobile communications. There are still many adults and young people who would appreciate the social and economic benefits of mobile technology but are unable to access it, highlighting a huge opportunities for future growth and a challenge to all players in the industry ecosystem to expand the scope of products and services to tap this demand. It is obvious that the next few years will see continued growth with a further 700 million subscribers expected to be added by 2017 and the 4 billion mark to be passed in 2018. This growth is mirrored by strong mobile connections growth, to almost 7 billion connections in 2012, as many consumers have multiple devices or use multiple SIMs to access the best tariffs, while firms in many industry sectors roll out M2M applications to boost their own productivity and tap into new markets. Despite challenging economic headwinds in many regions, the market is expected to grow even more strongly on the dimension of connections over the next five years, with 3 billion additional connections expected to be added between 2012 and 2017, a growth rate of 7.6\% p.a. Mark Page (2013) also suggested that 3.2 billion people of the 7 billion on earth benefit from having a mobile phone. A further 700 million subscribers are expected by 2017, but this still leaves a huge potential for further growth and the headline growth in number of connections hides an important shift taking place in the types of connections that make up the total. In terms of numbers of connections, 2G technologywhich provides digital quality voice, messaging and low bandwidth data connections -still accounts for the majority of global connections in 2012, 5 billion of the 6.8 billion totals. However, the growth in connections will be driven by increased uptake of 3G and 4G connections, which are forecast to increase from 26\% to 53\% of global connections over the period 2012 to 2017. In Bangladesh, there is a growing number of wireless technology users in the area of M-commerce. There are more consumers who have a mobile phone than those who have a personal computer. Despite the rapidly growing number of mobile phone users in Bangladesh, M-commerce is a relatively new phenomenon in Bangladesh compared to other markets in Europe, the U.S., and the Asia Pacific. Currently, there are six mobile phone operators offering their services to customers in Bangladesh. GrameenPhone (GP) owned by Telenor (62\%) and Grameen Telecom (38\%) is the largest and the fastest growing cellular service provider in Bangladesh. BanglaLink is the second largest cellular service provider. It is a wholly owned subsidiary of Orascom Telecom. Robi is the 3rd largest cellular service provider in Bangladesh. It is a joint venture between Telecom Malaysia Sdn Bhd of Malaysia (70\%) and a local company A.K. Khan \& Company (30\%). The fourth largest cellular service provider in Bangladesh is Warid Telecom International Ltd. Warid Telecom is a wholly owned subsidiary of Warid Telecom International LLC. Abu Dhabi. The other two mobile phone operators in Bangladesh are Citycell and Teletalk Bangladesh Ltd. Before moving on, a clear understanding of what mobile gaming actually is needs to be established. Liang and Yeh (2011, p. 188) define mobile game as a video game played on mobile devices, including mobile phones, smart phones, PDA's or handheld computers. However, as handheld gaming has existed for such a long time in the form of Game boys, Tetrises and other small devices, the phenomenon is not very new anymore. Thus, the mobile gaming is understood as the form of gaming introduced by the new smart phone categories. Following this interpretation, mobile games are purchased or downloaded from online digital application stores, without ever taking any physical forms in the distribution chennel. \hyperref[b10]{Liu and Li (2011)} showed that use context and cognitive concentration which is essentially the same as flow are significant factors of mobile gaming adoption in the version of TAM they used. Liu from China focused on the adoption progress and performance at a minimum cost. The results of mobile learning in solving puzzles. These studies showed a significant improvement in terms of learning cconcentrated on the use of SMS and MMS in the learning outcomes and satisfaction compared to the traditional environment. However, the researcher ignored the important factors, such as affordability and pedagogy. As to more recent studies with an oriental focus, especially on mobile industry, we can mention \hyperref[b12]{Lu et al. (2007)}, who studied whether a TAM-based model is applicable in the Chinese context. Originally introduced by Fred Davis as early as in the 1980s, the technology aacceptance model sought to measure the willingness of people to accept and adopt new information technology innovations of that era, such as the electronic mail systems \hyperref[b2]{(Davis 1989}). As to more recent studies with an oriental focus, especially on mobile industry. According to Volery and Lord, student adoption of university has introduced mobile learning by sending technology is based on the satisfaction derived from messages through SMS. Using the device, flexibility, self-confidence, self-efficacy and participation. \hyperref[b6]{Ismail et al. (2009)} conducted learning in the United States; these factors proved to be action research on the adoption of SMS among long distance education students in University Sains Malaysia. The learning characteristics and design were examined based on communication, convenience, enjoyment, satisfaction, quality, pace and reliability. \hyperref[b18]{Mariga (2003)} posits that perceived usefulness, perceived ease of use, trust, and enjoyment is the significant determinant for the adoption of M-commerce services. \hyperref[b5]{Huei (2004)} suggests that perceived usefulness, perceived ease of use, perceived risk, and cost is the influencing factors for the adoption of M-commerce. Economic and infrastructure issues can influence the rate of m commerce adoption \hyperref[b3]{(Fraunholz \& Unnithan, 2004}). Cultural differences can potentially play a significant role in m-commerce adoption \hyperref[b4]{(Hards et al., 2005)}. The main obstacle toward personalization is privacy and security, which can negatively impact the prevalent use of such applications. So far, there has not been an approach focusing specifically on the wireless/mobile user \hyperref[b20]{(Panayiotou \& Samaras, 2004)}. Brunner and Kumar (2005) suggest that perceived enjoyment may help explain consumer acceptance of handheld Internet devices better than the original TAM constructs alone. Perceived enjoyment, usefulness, and expressiveness influence the intention to use mobile services \hyperref[b19]{(Nysveen et al., 2005)}. Lu, Yu, \hyperref[b13]{Liu and Yao (2003)}  determinants of usage intention. Furthermore, systems quality and social influence also contribute to the adoption of mobile services \hyperref[b9]{(Kleijnen \& Wetzels, 2004}). \hyperref[b15]{Lee and Benbasat (2003)} recommend the multi-tasking nature of device usage as well as users' limited attention as two of the many essential factors affecting mcommerce adoption. Other technical factors that can impact m-commerce adoption include user interface limitations, slow network connections, information security, or even the threat of government regulations \hyperref[b26]{(Wen \& Mahatanankoon, 2004)}. \hyperref[b21]{Sarker and Wells (2003)} find that users' positive experience with mobile usage (i.e., functional, psychological, and relational outcomes) influence adoption decisions and behaviors. They suggest that cultural origin influences individuals' patterns of mobile technology usage. \hyperref[b1]{Bouwman et al. (2007)} further suggest the importance of physical, cognitive, security, and economic factors when delivering bundled mobile services to consumers. Apart from the social uses outlined, earlier studies by \hyperref[b16]{Leung and Wei (2000)} indicate that utilitarian uses of the mobile phone are more frequent and instrumental motives are much stronger than social uses. Various types of business deals including cross country transactions are being conducted on mobile phones daily. The two fundamental attributes of the mobile phone which has lead to its flourished usage are mobility and immediate access. However, t is this characteristic which has extended its usage from a traditional voice communication to other value added services like games, internet, banking, payments and informational services, perceived ease of use, (PEOU) is the degree to which a person believes that using a particular system will be free of effort. Perceived ease of use and perceived usefulness are two key components that have made the technology acceptance model one of the most influential research models related to understanding information technology usage. PU in the adoption of mobile services is defined in a broader context to include how well consumers believes mobile services can be integrated into their daily activities. Various researchers have shown that perceived usefulness can also relate to the increased performance of a non-job related task that occurs outside of an organizational. Tseng, Hsu and Chuang (2012) found that PEOU and PU made a significant positive impact on attitudes towards website use. PEOU and PU have a positive impact on consumer online shopping: future plans to use online shopping \hyperref[b7]{(Koufaris, 2002)}, Enjoyment Studies have found that user entertainment plays a significant role in the success of certain technology. Research has shown that the adoption and satisfaction levels of information systems and products are associated with user perceptions of entertainment provided by the technology \hyperref[b8]{(Kim et al., 2009)}. The most popular reason for the adoption of e-books by university students was for enjoyment (reading pleasure and leisure) over academic purposes. Similarly, enjoyment has been shown to be a key to player usage of online gaming. Involvement user involvement has been shown to be a key determinant of technology usage. Research by \hyperref[b22]{Swanson (1974)} indicated that high user involvement ultimately increases frequency of use. The core research problem to be addressed in this study is whether the factors considered by literature review actually influence the adoption of mobile phone gaming among users in Bangladesh. The specific research objective is to investigate and identify the key factors and most influencing factors that users consider when they adopt the mobile phone gaming in the context of Bangladesh. 
\section[{II.}]{II.} 
\section[{Methodology a) Objectives of the study}]{Methodology a) Objectives of the study}\par
The objective of the study was to identify the various key factors that affect the adoption of mobile phone gaming. More specifically, the study aimed to achieve the following specific research objectives: ? 
\section[{Discussion and Analysis a) Factor analysis Model}]{Discussion and Analysis a) Factor analysis Model}\par
An econometric model is developed for the study in order to testify the relationship between the dependent and independent variables and to test the hypotheses relevant for the study. This is an integrated model, which is proposed on the basis of the Mobile phone gaming adoption. Mobile phone gaming adoption as a function of mobile adoption can be depicted in the form of the following equation.    \hyperref[tab_1]{2} shows that out of total samples about 80\% are male and 20\% are female. 10\% are below 20 years, 81\% are 20 to 30 years 2\% are 30-40 years and only 7 \% are more than 40 years old. Most of mobile phone games users are students (85\%), either employees or businessmen (7\%), 4\% are engaged in teaching. Educational status of the respondents shows that most of graduates and post graduate person play mobile phone gaming (78\% and 12\% respectively). This data indicates that higher educated people who have completed or about to be completed play mobile phone game. Data also shows that (75\%) users are expert and 25\% are new users in playing mobile phone game. 
\section[{Conceptual framework of the study}]{Conceptual framework of the study} 
\section[{c) Reliability Analysis, Means and Standard Deviations of the Study Variables}]{c) Reliability Analysis, Means and Standard Deviations of the Study Variables}\par
All items used a 5 (Five) points Likert scale as (1= Fully Disagree, 2= Disagree, 3= Undecided, 4= Agree, 5= Fully Agree. The Cronbach's alpha reliability test has been used to identify the validity of items used in survey. According to Hendrickson et al  {\ref (1993)} and  {\ref McGraw and Wong (1996)} the alpha of a scale should be greater than 0.70 for items to be used together as a scale. Finalize the item validity. As per shown in table 3 that all dimensions have appropriate reliability. Therefore minimum 0.700 coefficient alpha values accepted for the reliability of the variable used, all were found to show satisfactory Cronbach's value of between 0.668 and 0.762 (refer Table \hyperref[tab_2]{3}). Thus, all the Variables are considered reliable. In addition, the mean value for each of the ten components of Mobile phone gaming varied from 2.5833 to 3.6971. The standard deviation for these components ranges from 0.74627 to 1.16696. Mobile adoption had a mean value of 3.71 with a standard deviation of 1.10 whereas easiness had a mean of 3.70 and standard deviation of 0.75. 
\section[{d) Multiple Regression Analysis}]{d) Multiple Regression Analysis}\par
The first table of interest is the Model Summary table. This table provides the R, R 2 , adjusted R 2 , and the standard error of the estimate, which can be used to determine how well a regression model fits the data.\par
The R column represents the value of R, the multiple correlation coefficients. R can be considered to be one measure of the quality of the prediction of the dependent variable; in this case mobile game adoption. A value of 0.410 indicates a good level of prediction. The R Square column represents the R 2 value (also called the coefficient of determination), which is the proportion of variance in the dependent variable that can be explained by the independent variables. Technically, it is the proportion of variation accounted for by the regression model above and beyond the mean model. It is observed from table 4 that the value of R 2 is 0.176 that our independent variables explain 17.60 \% of the variability of our dependent variable. Thus, the regression equation of this study is: Y (MPG) = 1.985 + 0.255(Ea) + 0.012 (Us) -0.001(En) -0.233(SI) + 0.284 (Co) + 0.049 (PC) + 0.056 (PA) + 0.050(In) -0.084 (Be) + ?. 
\section[{Global Journal of Management and Business Research}]{Global Journal of Management and Business Research}\par
Volume XIV Issue VIII Version I Year ( ) The regression equation indicates that most of the independent variables and dependent variable (MPGA) mobile adoption are positively related. Moreover, in Table \hyperref[tab_5]{6}, the collinearity statistics reveal that the tolerances of all the independent variables are greater than 0.1 and that the Variance Inflation Factors (VIF) are all less than 10. The tolerance shows the amount of correlation between the predictor and all the other remaining predictors. Variance inflation factors reflect the degree to which the standard error of the predictor was increased due to the predictor's correlation with the other predictors in the model. As the tolerance value < 0.1 or the VIF value > 10 is an indication of collinearity, there is not collinearity in the model of this study as can be seen in the result shown in Table \hyperref[tab_5]{6}. Unstandardized coefficients indicate how much the dependent variable varies with an independent variable, when all other independent variables are held constant.\par
The F-ratio in the ANOVA table 5 tests whether the overall regression model is a good fit for the data. The table shows that the independent variables statistically significantly predict the dependent variable. As F (9, 92) =2.820, p<.05, this model is significant. In addition, Table \hyperref[tab_4]{5} is the result of ANOVA, which shows the value of F (9, 92) =2.820and it is significant at p value 0.006<0.05. As a result, overall, the regression model is deemed to accurately predict mobile adoption towards mobile phone gaming.   
\section[{) Analysis of correlation matrix}]{) Analysis of correlation matrix}\par
Correlation matrix is used to check the pattern of relationships among variables. It is seen that the majority of the values are greater than 0.05. so the relationships are correlated among variables. The value of the determinants of the correlation matrix is .086 which is greater than the necessary value of 0.00001.Therefore multi colinearity is not a problem for these data .To sum up, all questions in the survey are correlate fairly well and none of the correlation 
\section[{Global Journal of Management and Business Research}]{Global Journal of Management and Business Research}\par
Volume XIV Issue VIII Version I Year ( )\par
coefficients are particularly large . So there is no need to consider eliminating any questions at the research. 
\section[{f) KMO and Bartlett's Test}]{f) KMO and Bartlett's Test}\par
The table \hyperref[tab_7]{8} shows that the Kaiser-Meyer-Olkin Measure of Sampling Adequacy and Bartlett's Test of Sphericity. The KMO statistic varies between 0 and 1. A value of 0 indicates that the sum of partial correlation is large relative to the sum of correlations. Hence factor analysis is likely to be inappropriate. A value close to 1 indicates that patterns of correlations are relatively compact and so factor analysis yields distinct and reliable results.  {\ref Kaiser (1974)} recommends accepting values greater than 0.5. Furthermore values between 0.7 and 0.8 are good. So our Kaiser-Meyer-Olkin Measure of Sampling Adequacy test is 0.752 or 75\% is reliable and acceptable for further computation. So it is clear that factor analysis is appropriate for these data. Significance value also acceptable. Bartlett's Test of Sphericity tests the null hypothesis that the original correlation matrix is an identity matrix. For factor analysis we need some relationships between variables and if the R matrix were an identity matrix then all correlation coefficients would be zero. We see a significant value less than 0.05.A significant test tells that R matrix is not an identity matrix that means there are some relationships between variables. For these data Bartlett's test is highly significant (p<0.05), and therefore factor analysis is appropriate for this study. The correlation matrix of all 9 variables have been further subjected to principal component analysis. The factor matrix is obtained in the principal component analysis has also been further subjected to Varimax Rotation. The Eigenvalue is the total variance explained by each factor. The Eigen values, the percentage of total variance, and rotated sum of squared loadings have been shown in Table \hyperref[tab_8]{9}. Any factor that has an eigenvalue less than one does not have enough total variance explained to represent a unique factor and is therefore disregarded. The Eigenvalues associated with each factor represent the variance explained by that particular linear component and SPSS displays the eigenvalues in terms of the percentage of variance explained. An examination of Eigen values has led to the retention of three factors. These factors have accumulated for 37.\%, 14.32\%, and 11.82 \% of variation respectively. This implies that the total variance accumulated for by all three factors is 63.17\% and remaining variance is explained by other factors.. So factor 1 explains 37\% of total variance. It is clear that first few factors explain relatively large amounts of variance where as subsequent factors explain a small amount of variance.   
\section[{h) Analysis of Communalities and Component Matrix}]{h) Analysis of Communalities and Component Matrix}\par
The communality is the proportion of variance accounted for by the common factors of a variable. Communalities range from 0 to 1. Zero means that the common factors do not explain any variance and one means that the common factors explain all the variance. The communalities of the column leveled extraction reflect the common variance in the data structure. It is observed that 28.8 percent of the variance associated with question 1 is common or shared variance. It is seen from the result that relatively high numbers that is a good result.  
\section[{i) Scree Plot}]{i) Scree Plot}\par
A Scree plot is a graph that plots the total variance associated with each factor. It is a visual display of how many factors there are in the data. It is seen that although there are 9 principle components only three factors have eigenvalues over one. It is expected that three principle components in the data. The curve indicates the inflexion on the curve. 
\section[{j) Rotated Component Matrix}]{j) Rotated Component Matrix}\par
The Rotated component matrix indicates how each item correlates with each factor. The rotation of the factor structure clarified things considerably. There are three factors and variables load very highly onto only one factor. a. Rotation converged in 5 iterations.\par
IV. 
\section[{Conclusion}]{Conclusion}\par
Modern and innovative technology offer lots of blessings for human betterments, a smart phone is one of them that is a mobile phone device which offers more advanced computing ability and Internet connectivity than traditional mobile phones. The advanced functionalities of a smart phone enable access to the Internet and the use of various applications such as mobile gaming, as well as phone calling and text messaging. In This study tested the correlations among mobile phone gaming adoption and some influencing factors such as behavior, playing context, usefulness, easiness, concentration, enjoyment, social Influence, intention, personal attitude in of Bangladesh. The study also revealed that the first three factors are the most important factors these influence the most adoption of mobile gaming. We see correlation matrix to check the pattern of relationships. We see that the majority of the values are greater than 0.05. So the relationships are correlated among variables. The result of the study also highlights the pattern of relationship among variables that were proved by the analysis. The contribution made by the study is give suggestions for the operators of mobile phone to consider the above mentioned factors while designing their offerings for customers to get the maximum benefit by satisfying mobile phone gaming users in Bangladesh. Marketers should, however, carefully consider the key factors that influence the adoption and performance the operations of mobile phone gaming. Despite several meaningful findings, the study is not without limitations. Above all, the results are limited in terms of generalizability because the samples used in the current study did not cover a wide range of demographics in terms of age, ethnicity, employment, income, and education. Thus, future research is recommended to further investigate determining factors more closely by including extended demographics variables. 
\section[{Global Journal of Management and Business Research}]{Global Journal of Management and Business Research}\par
Volume XIV Issue VIII Version I Year ( )\begin{figure}[htbp]
\noindent\textbf{2}\includegraphics[]{image-2.png}
\caption{\label{fig_0}2 Global}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-3.png}
\caption{\label{fig_1}}\end{figure}
   \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.2842271293375394\textwidth}P{0.5657728706624605\textwidth}}
Research Instrument\tabcellsep Survey questionnaire.\\
Data Source\tabcellsep Primary data for statistical analysis and secondary data for literature review and the sources are Internet, journals, books, magazines etc.\\
Data collection mode\tabcellsep Survey in the form of Personal face to face interview.\\
Measurement\tabcellsep Non comparative scaling techniques. Itemized Ranting scale in the form of 5 point\\
Technique/Basic scale\tabcellsep likert scale. (1= Fully Disagree, 2= Disagree, 3= Undecided, 4= Agree, 5= Fully\\
design\tabcellsep Agree)\\
Sample Size\tabcellsep The target sample size 110 and realized sample size is 102 and the response rate is 102*100/110= 92\%.\\
Sampling Technique\tabcellsep Non probability and its specific form like convenience sampling.\\
Sample Location\tabcellsep Rangpur division specially Dinajpur and Rangpur district of Bangladesh.\\
Time\tabcellsep June-Nov, 2014.\\
\multicolumn{2}{l}{Reliability and Validity test Cronbach's Data analysis tools Descriptive statistics , correlation, One way ANOVA, multiple regression, factor analysis (principal component analysis).}\end{longtable} \par
 
\caption{\label{tab_0}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.5400804289544235\textwidth}P{0.11394101876675604\textwidth}P{0.19597855227882036\textwidth}}
Variable\tabcellsep Frequency\tabcellsep \%\\
Age( Years)\tabcellsep \tabcellsep \\
10-20\tabcellsep 10\tabcellsep 9.8\\
20-30\tabcellsep 83\tabcellsep 81.4\\
30-40\tabcellsep 2\tabcellsep 2.0\\
40-above\tabcellsep 7\tabcellsep 6.9\\
Total\tabcellsep 102\tabcellsep 100.0\\
Gender\tabcellsep \tabcellsep \\
Male\tabcellsep 81\tabcellsep 79.4\\
Female\tabcellsep 21\tabcellsep 20.6\\
Total\tabcellsep 102\tabcellsep 100.0\\
Profession\tabcellsep \tabcellsep \\
Student\tabcellsep 87\tabcellsep 85.3\\
Teacher\tabcellsep 4\tabcellsep 3.9\\
Employee\tabcellsep 7\tabcellsep 6.9\\
Other\tabcellsep 4\tabcellsep 3.9\\
Total\tabcellsep 102\tabcellsep 100.0\\
User\tabcellsep \tabcellsep \\
Category\tabcellsep \tabcellsep \\
Expert\tabcellsep 77\tabcellsep 75.5\\
Novice\tabcellsep 23\tabcellsep 22.5\\
Total\tabcellsep 102\tabcellsep 100.0\\
Education\tabcellsep \tabcellsep \\
Below SSC\tabcellsep 5\tabcellsep 4.9\\
SSC\tabcellsep 4\tabcellsep 3.9\\
HSC\tabcellsep 1\tabcellsep 1.0\\
Bachelor\tabcellsep 79\tabcellsep 77.5\\
Masters\& Above\tabcellsep 13\tabcellsep 12.7\\
Total\tabcellsep 102\tabcellsep 100.0\\
\multicolumn{3}{l}{b) Demographic Profile of the Respondents}\\
Table\tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_1}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.29129213483146066\textwidth}P{0.059691011235955056\textwidth}P{0.15758426966292136\textwidth}P{0.15280898876404495\textwidth}P{0.18862359550561797\textwidth}}
Variables\tabcellsep Number of items\tabcellsep Cronbach's Alpha\tabcellsep Mean\tabcellsep Std. Dev.\\
Social Influence\tabcellsep 3\tabcellsep 0.706\tabcellsep 2.5892\tabcellsep 0.85514\\
Concentration\tabcellsep 3\tabcellsep 0.707\tabcellsep 3.5667\tabcellsep 0.80325\\
Playing context\tabcellsep 2\tabcellsep 0.758\tabcellsep 3.5343\tabcellsep 0.97813\\
Personal attitude\tabcellsep 2\tabcellsep 0.668\tabcellsep 3.1716\tabcellsep 1.07283\\
Intention\tabcellsep 1\tabcellsep 0.686\tabcellsep 3.0980\tabcellsep 1.12144\\
Behavior\tabcellsep 2\tabcellsep 0.707\tabcellsep 2.5833\tabcellsep 1.16696\\
Adoption\tabcellsep 1\tabcellsep 0.762\tabcellsep 3.7157\tabcellsep 1.10245\\
Easiness\tabcellsep 3\tabcellsep 0.714\tabcellsep 3.6971\tabcellsep 0.74627\\
Usefulness\tabcellsep 2\tabcellsep 0.705\tabcellsep 3.4461\tabcellsep 0.96192\\
Enjoyment\tabcellsep 2\tabcellsep 0.675\tabcellsep 3.2696\tabcellsep 1.04992\end{longtable} \par
 
\caption{\label{tab_2}Table 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.49261363636363636\textwidth}P{0.014488636363636362\textwidth}P{0.028977272727272723\textwidth}P{0.04588068181818182\textwidth}P{0.002414772727272727\textwidth}P{0.0796875\textwidth}P{0.04588068181818182\textwidth}P{0.08451704545454544\textwidth}P{0.002414772727272727\textwidth}P{0.012073863636363636\textwidth}P{0.04105113636363636\textwidth}}
Model\tabcellsep R\tabcellsep R Square\tabcellsep Adjusted Square\tabcellsep R\tabcellsep Std. Error of the Estimate\tabcellsep R Square Change\tabcellsep \multicolumn{2}{l}{Change Statistics F Change df1}\tabcellsep df2\tabcellsep Sig. F Change\\
1\tabcellsep .419a\tabcellsep .176\tabcellsep .095\tabcellsep \tabcellsep 1.04882\tabcellsep .176\tabcellsep 2.177\tabcellsep 9\tabcellsep 92\tabcellsep .031\\
\multicolumn{11}{l}{a. Predictors: (Constant), Adoption, Usefulness, Playing Context, Intention, Easiness, Concentration, Enjoyment,}\\
\multicolumn{5}{l}{Social Influence, Behavior, Personal Attitude.}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{5}{l}{Dependent Variable: mobile game adoption}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_3}Table 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5} \par 
\begin{longtable}{P{0.20493150684931508\textwidth}P{0.523972602739726\textwidth}P{0.025616438356164384\textwidth}P{0.05356164383561644\textwidth}P{0.0163013698630137\textwidth}P{0.025616438356164384\textwidth}}
Year\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
6\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Volume XIV Issue VIII Version I\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
)\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
(\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Global Journal of Management and Business Research\tabcellsep ANOVA b Model Sum of Squares 1 Regression 26.541 Residual 96.214 Total 122.755\tabcellsep df 9 92 101\tabcellsep Mean Square 2.949 1.046\tabcellsep F 2.820\tabcellsep Sig. .006 a\\
\tabcellsep \multicolumn{5}{l}{a. Predictors: (Constant), Behavior, Playing Context, Usefulness, Easiness, Concentration, Enjoyment, Social}\\
\tabcellsep Influence, Intention, Personal Attitude\tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_4}Table 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{6} \par 
\begin{longtable}{P{0.23017944535073412\textwidth}P{0.11092985318107666\textwidth}P{0.08319738988580751\textwidth}P{0.10399673735725938\textwidth}P{0.07487765089722676\textwidth}P{0.07487765089722676\textwidth}P{0.10538336052202284\textwidth}P{0.06655791190864599\textwidth}}
\tabcellsep \multicolumn{2}{l}{Unstandardized}\tabcellsep Standardized\tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{Collinearity}\\
Model\tabcellsep \multicolumn{2}{l}{Coefficients}\tabcellsep Coefficients\tabcellsep t\tabcellsep Sig.\tabcellsep \multicolumn{2}{l}{Statistics}\\
\tabcellsep B\tabcellsep Std. Error\tabcellsep Beta\tabcellsep \tabcellsep \tabcellsep Tolerance\tabcellsep VIF\\
(Constant)\tabcellsep 1.985\tabcellsep 0.659\tabcellsep \tabcellsep 3.010\tabcellsep 0.003\tabcellsep \tabcellsep \\
Easy to use\tabcellsep 0.255\tabcellsep 0.110\tabcellsep 0.228\tabcellsep 2.317\tabcellsep 0.023\tabcellsep 0.883\tabcellsep 1.133\\
Usefulness\tabcellsep 0.012\tabcellsep 0.097\tabcellsep 0.013\tabcellsep 0.125\tabcellsep 0.901\tabcellsep 0.841\tabcellsep 1.189\\
Enjoyment\tabcellsep -0.001\tabcellsep 0.087\tabcellsep -0.002\tabcellsep -0.016\tabcellsep 0.987\tabcellsep 0.804\tabcellsep 1.243\\
Social Influence\tabcellsep -0.233\tabcellsep 0.090\tabcellsep -0.279\tabcellsep -2.582\tabcellsep 0.011\tabcellsep 0.729\tabcellsep 1.371\\
Concentration\tabcellsep 0.284\tabcellsep 0.102\tabcellsep 0.277\tabcellsep 2.789\tabcellsep 0.006\tabcellsep 0.863\tabcellsep 1.159\\
Playing Context\tabcellsep 0.049\tabcellsep 0.080\tabcellsep 0.059\tabcellsep 0.611\tabcellsep 0.543\tabcellsep 0.926\tabcellsep 1.080\\
Personal Attitude\tabcellsep 0.056\tabcellsep 0.119\tabcellsep 0.063\tabcellsep 0.476\tabcellsep 0.635\tabcellsep 0.481\tabcellsep 2.080\\
Intention\tabcellsep 0.050\tabcellsep 0.127\tabcellsep 0.051\tabcellsep 0.396\tabcellsep 0.693\tabcellsep 0.513\tabcellsep 1.948\\
Behavior\tabcellsep -0.084\tabcellsep 0.097\tabcellsep 0.104\tabcellsep -0.863\tabcellsep 0.390\tabcellsep 0.591\tabcellsep 1.692\\
\multicolumn{3}{l}{a. Dependent Variable: mobile game adoption}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_5}Table 6 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{7} \par 
\begin{longtable}{P{0.05210727969348659\textwidth}P{0.04559386973180077\textwidth}P{0.08358876117496807\textwidth}P{0.08358876117496807\textwidth}P{0.08250319284802043\textwidth}P{0.08358876117496807\textwidth}P{0.08250319284802043\textwidth}P{0.0846743295019157\textwidth}P{0.08358876117496807\textwidth}P{0.08358876117496807\textwidth}P{0.0846743295019157\textwidth}}
\tabcellsep \tabcellsep Ea\tabcellsep Us\tabcellsep En\tabcellsep SI\tabcellsep Co\tabcellsep PC\tabcellsep PA\tabcellsep In\tabcellsep Be\\
\tabcellsep Ea\tabcellsep 1.000\tabcellsep .290\tabcellsep .213\tabcellsep .182\tabcellsep .280\tabcellsep .005\tabcellsep .272\tabcellsep .322\tabcellsep .178\\
\tabcellsep Us\tabcellsep .290\tabcellsep 1.000\tabcellsep .328\tabcellsep .434\tabcellsep .313\tabcellsep .020\tabcellsep .342\tabcellsep .225\tabcellsep .200\\
Correlation\tabcellsep En SI Co PC\tabcellsep .213 .182 .280 .005\tabcellsep .328 .434 .313 .020\tabcellsep 1.000 .377 .405 .237\tabcellsep .377 1.000 .308 -.131\tabcellsep .405 .308 1.000 .141\tabcellsep .237 -.131 .141 1.000\tabcellsep .583 .454 .222 .056\tabcellsep .364 .246 .151 .010\tabcellsep .303 .442 .111 -.096\\
\tabcellsep PA\tabcellsep .272\tabcellsep .342\tabcellsep .583\tabcellsep .454\tabcellsep .222\tabcellsep .056\tabcellsep 1.000\tabcellsep .554\tabcellsep .435\\
\tabcellsep In\tabcellsep .322\tabcellsep .225\tabcellsep .364\tabcellsep .246\tabcellsep .151\tabcellsep .010\tabcellsep .554\tabcellsep 1.000\tabcellsep .565\\
\tabcellsep Be\tabcellsep .178\tabcellsep .200\tabcellsep .303\tabcellsep .442\tabcellsep .111\tabcellsep -.096\tabcellsep .435\tabcellsep .565\tabcellsep 1.000\\
\tabcellsep Ea\tabcellsep \tabcellsep .002\tabcellsep .016\tabcellsep .034\tabcellsep .002\tabcellsep .481\tabcellsep .003\tabcellsep .000\tabcellsep .037\\
\tabcellsep Us\tabcellsep .002\tabcellsep \tabcellsep .000\tabcellsep .000\tabcellsep .001\tabcellsep .419\tabcellsep .000\tabcellsep .011\tabcellsep .022\\
(1-tailed) Sig.\tabcellsep En SI Co PC\tabcellsep .016 .034 .002 .481\tabcellsep .000 .000 .001 .419\tabcellsep .000 .000 .008\tabcellsep .000 .001 .095\tabcellsep .000 .001 .079\tabcellsep .008 .095 .079\tabcellsep .000 .000 .013 .289\tabcellsep .000 .006 .065 .459\tabcellsep .001 .000 .133 .169\\
\tabcellsep PA\tabcellsep .003\tabcellsep .000\tabcellsep .000\tabcellsep .000\tabcellsep .013\tabcellsep .289\tabcellsep \tabcellsep .000\tabcellsep .000\\
\tabcellsep In\tabcellsep .000\tabcellsep .011\tabcellsep .000\tabcellsep .006\tabcellsep .065\tabcellsep .459\tabcellsep .000\tabcellsep \tabcellsep .000\\
\tabcellsep Be\tabcellsep .037\tabcellsep .022\tabcellsep .001\tabcellsep .000\tabcellsep .133\tabcellsep .169\tabcellsep .000\tabcellsep .000\tabcellsep \\
\multicolumn{3}{l}{a. Determinant = .086}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
e\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_6}Table 7 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{8} \par 
\begin{longtable}{}
\end{longtable} \par
 
\caption{\label{tab_7}Table 8 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{9} \par 
\begin{longtable}{P{0.5357142857142857\textwidth}P{0.22857142857142856\textwidth}P{0.08571428571428572\textwidth}}
\multicolumn{2}{l}{Kaiser-Meyer-Olkin Measure of Sampling}\tabcellsep 0.752\\
Adequacy.\tabcellsep \tabcellsep \\
Bartlett's Test of\tabcellsep \multicolumn{2}{l}{Approx. Chi-Square 238.008}\\
Sphericity\tabcellsep df\tabcellsep 36\\
\tabcellsep Sig.\tabcellsep 0.000\end{longtable} \par
 
\caption{\label{tab_8}Table 9 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{10} \par 
\begin{longtable}{}
\end{longtable} \par
 
\caption{\label{tab_9}Table 10 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{11} \par 
\begin{longtable}{}
\end{longtable} \par
 
\caption{\label{tab_10}Table 11 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.29162462159434915\textwidth}P{0.08491422805247224\textwidth}P{0.023158425832492432\textwidth}P{0.008577194752774976\textwidth}P{0.05060544904137235\textwidth}P{0.31478304742684154\textwidth}P{0.07633703329969727\textwidth}}
\tabcellsep \multicolumn{2}{l}{Initial}\tabcellsep \multicolumn{2}{l}{Extraction}\tabcellsep \\
Easiness\tabcellsep \multicolumn{2}{l}{1.000}\tabcellsep \tabcellsep .288\tabcellsep \tabcellsep Year\\
Usefulness\tabcellsep \multicolumn{2}{l}{1.000}\tabcellsep \tabcellsep .585\tabcellsep \tabcellsep 9\\
Enjoy Social influence Concentration Playing context Personal attitude Intention Behaviour\tabcellsep \multicolumn{2}{l}{1.000 1.000 1.000 1.000 1.000 1.000 1.000}\tabcellsep \tabcellsep .659 .622 .621 .813 .682 .720 .697\tabcellsep \tabcellsep Volume XIV Issue VIII Version I\\
\multicolumn{5}{l}{Extraction Method: Principal Component Analysis.}\tabcellsep \tabcellsep ( )\\
Easiness Usefulness Enjoy Social influence Concentration Playing context Personal attitude Intention Behaviour\tabcellsep 1 .788 .720 .685 .676 .644 .594 .489 .506\tabcellsep \multicolumn{2}{l}{Component 2 -.481 .734 .508}\tabcellsep 3 .406 -.454 .520\tabcellsep item of the analysis correlates with each of the three retained factors. Negative and positive correlations carry the same weight. The component matrix contains the loadings of each variable onto each factor. At this stage SPSS has extracted 3 factors .All loadings less than .40 is suppressed in the output that's why there are blank spaces for many of the loadings.\tabcellsep Global Journal of Management and Business Research\\
\multicolumn{5}{l}{Extraction Method: Principal Component Analysis.}\tabcellsep \\
\multicolumn{2}{l}{a. 3 components extracted.}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
  {\small\itshape [Note: © 2014 Global Journals Inc. (US)]} 
\caption{\label{tab_11}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{12} \par 
\begin{longtable}{P{0.6640625\textwidth}P{0.0564453125\textwidth}P{0.099609375\textwidth}P{0.029882812499999998\textwidth}}
\tabcellsep \tabcellsep Component\tabcellsep \\
\tabcellsep 1\tabcellsep 2\tabcellsep 3\\
Intention\tabcellsep .841\tabcellsep \tabcellsep \\
Behavior\tabcellsep .804\tabcellsep \tabcellsep \\
Personal attitude\tabcellsep .731\tabcellsep \tabcellsep \\
Usefulness\tabcellsep \tabcellsep .750\tabcellsep \\
Concentration\tabcellsep \tabcellsep .740\tabcellsep \\
Social influence\tabcellsep \tabcellsep .628\tabcellsep \\
Easiness\tabcellsep \tabcellsep .492\tabcellsep \\
Enjoy\tabcellsep .478\tabcellsep .482\tabcellsep .445\\
Playing context\tabcellsep \tabcellsep \tabcellsep .901\\
\multicolumn{4}{l}{Extraction Method: Principal Component Analysis.}\\
\multicolumn{4}{l}{Rotation Method: Varimax with Kaiser Normalization.}\end{longtable} \par
 
\caption{\label{tab_12}Table 12 :}\end{figure}
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