The conceptual frame work “USER SETISFECTION at university libraries of balochistan” developed to follow by a type of (SEM) structured equation modeling through PLS (Partial least squire). Since 1970 the development of structural equation modelling (SEM) methods and software has proceeded rapidly (Austin, 2000).Structural modelling technique is a general statically method which is being used in different behavioral sciences. It can be also used as the combination of factor analysis and regression analysis (path analysis). Many researchers give it the name of LISREL modal also called liner modal which is 1st used by joreskog in an introductory programs of SEM.
It is a convenient framework for statically analysis. For example regression analysis, factor analysis, discernment analysis in a special case. All these equation models often visualized by graphical path diagram. Thus we can say that ‘It is a comprehensive statistical approach to testing hypotheses about relations among observed and latent variables’ (Hoyle, 1995).
In a PLS model, the individual reliability of the indicator is assessed by examining the loadings (simple correlations) with their respective construct. A common rule of thumb, proposed by Carmines and Zeller (1979), sets a loading over 0.70 as a criterion for acceptance. Nevertheless, some authors (Barclay et al. 1995; Chin 1998a) state that this rule can be relaxed both in the early stages of the scale development and also in comparative studies using the same scale, accepting items with loadings greater than 0.5. The previous criterion is adopted in this study due to the lack of a sound theory based on structural equations to explain the user satisfaction level of smartphone technology.
The present study has been given in form of visual modeling technique of SEM through PLS model. Structural equation modeling (SEM) is a technique which combines factor analysis and regression. As compared to conventional statistical techniques such as regression, SEM is a more healthy approach to testing substantive theories. These techniques do not further assume any relationships among the independent variables. According to Schumacker and Lomax (2004), SEM is more compatible with what happens in real-life because (a) it takes into account the relationships among many variables simultaneously and (b) in contrast to techniques such as regression which assume the measurement of the variables is error-free, SEM takes measurement error into account. SEM can simultaneously examine relationships among observed variables and latent variables as well as among latent variables.
There are two types of SEMs: the conventional SEM which is referred to as covariance-based SEM (CBSEM) and the partial least squares SEM (PLS-SEM) which is variance based. Due to the estimation procedures employed in each of the two types of SEM, they make different distributional assumptions and aim at different objectives. The notion Partial Least Squares (PLS) estimation is closely connected with the name of Herman O.A. Wold who in his late years was interested in models that contain latent variables (Claes Cassel, Peter Hackl, and Anders H. Westlund, 2014).CB-SEM is more suited to well-researched domains where enough theoretical and substantive knowledge is available thus CB-SEM can be employed to test the postulated network of relationships among the variables (i.e., test theories).
One the other hand, PLS-SEM is more appropriate where theory is less developed. They are primarily used to develop theories in exploratory research. According to Hair, Hult, Ringle, and Starstedt (2014), PLS-SEM is advantageous over the conventional SEM in situations where sample sizes are small, the data are not normally distributed, and complex models with many observed variables and relationships are estimated. According to Chin (2010) CB-SEM is covariance based while PLS-SEM is prediction based. , PLS estimates are affected only by the paths and loadings in the immediate block where a given construct lies (i.e., the constructs immediately affecting or affected by the construct). Thus in the component based PLS process a distinction is made between whether one wishes to explain the covariance’s of items under neighboring constructs or those under constructs further away.
This chapter comprises the data analysis as a result of questionnaire and literature search in pursuit of required information pertaining to the adoption, impact and usability of smartphone technology on university libraries of balochistan, Pakistan. Results have been given clearly with two major steps descriptive and ‘quantitative analysis.in below table a sample of n= 360 respondents were participated in the present study. The total sample from each university was 60 per university but the data frequency researcher got was 17 percent from UOB, from SBKWU there were 16 percent respondents while respondents from BUITEMS were as well 16 percent, from BUTAKE response rate was 16, al-Ahmad University provide data number of 16 percent too as well researcher got 17 percent responses from UTHAL university. the table also shows the Ages of respondents using smartphone technology.
According to the data 12 percent of responses shows that they were 18-22 years old. 30 percent users were under 22-25same as 25-30 were in 45 percent of total population. While 13 percent were above then 30 years. Among 79 percent were male whereas the female respondents were 21 percent out of total sample. According to the frequency of respondent’s qualification with PHD is 06 percent of the samples and the frequency of respondent’s qualification with MPhil are 29 percent, same as despondence with Master’s program are 55 percent while 14 percent despondence belongs from Bachelor degree program. The frequency of respondent’s belongs faculty with Arts is 19 percent and 14 percent from Humanities, 51percent respondent who participated in study are from Social Sciences and 15 percent belongs from the faculty of Science. As well results discussed the operating systems which are in use of respondents.
According to the results 63 percent users are using android handsets .IPhone users are 22 percent. The frequency of respondent’s using blackberry are 1 percent while 13 percent uses other type of operating systems for getting information. Respondence were also asked about the time experience with smartphone usage. According to the data 21 percent response’s shows that they are using smartphone since last one year. While 52 percent response’s provide data that they have experience of smartphone of last 2 to 6 years.27 percent respondents are using smartphone 7 to 9 years while no one provide response of using smartphone over 9 years. 29 percent respondents who participated in the study use their smartphones for the purpose of study. 30 percent uses smartphone research. 6 percent use it for official tasks and 39 percent respondents use it for recreational activities. The below table also represents that which type of media respondents are using for sharing information. Many of respondents were familiar to social networking sites.
According to them 37 percent use SMS service as a tool for sharing information. 25 percent respondents find Email service easy for getting and sharing data. Respondents using YouTube for information access are 3 percent and 2 percent uses blogs.23 percent find face book as a good source while twitter users are 3 percent who use it for sharing information. 6 percent respondent’s response that they use phone call as tool to sharing and access data. Social media is the widely used term in 21st century which is use to define number of tools and technologies, channels for communication and coloration with social aspects of internet (Dabbagh & Reo, 2011a).
Such as SMS, email, YouTube, Facebook, blogs tweeter and much more. Facebook hold 94% internet users which is the 1st most used social media site around the world (satiate, 2014). According to the report of United States tweeter hold 48.2 million users which is the 2nd most popular social media tool in the world (Lin and Liser, 2013). The 2010 ECAR study showed that 33.1% of the participant undergraduate student sample reported using wikis; 29.4% used SNS; 24.3% used video-sharing websites; 17.4 used web-based calendars; 11.6% used blogs; 4.3% used micro-blogs; and 2.8% used social bookmarking tool. Table 1 shows the detailed sample demographics.