One of the biggest challenges facing managers today is learning how to lead a multigenerational workforce. Many organizations have four generations of employees working alongside one another, and every generation has different expectations of what they want from the workplace (Lester et al., 2012). A generation is the social construction where individuals born during a similar period and these are influenced by historical, social events, and their experiences making them different from one other generation. These differences continue to be molded as aging (Howe and Strauss, 2007), influencing people's thoughts of authority, money, institutions, family and career (Conger, 2002). There are three classifications of age-based generations: baby boomers (1943- 1964) generation X (1965-1977) and generation Y (1978- 1990) (Parry and Urwin, 2011; Westerman and Yamamura, 2007), and in recent years we had the inclusion of generation Z, formed by people born after the nineties (Collistochi et al., 2012; Shah, 2009). Several studies, especially those directed to managers of companies, are between Y and Z generations (Batat, 2008; Mcmillan and Morrison, 2006; Nurullah, 2009), from the perspective that these generations are less committed at work (Becton et al., 2014). Likewise, these same generations would be more likely to use mobile technologies. By extension, we hypothesize that the generation affectsboth: usage behavior and perceived work performance improvement.
PayWave cards are used to make contactless payment be faster. However, the issues on data theft has been questioned by the bank's union. This study has been developed to identify the level of usage of payWave cards among consumers in Kuala Lumpur, as well as identifying the relationship between data security and data privacy towards the usage behavior of the payWave cards. A total of 204 data were collected through questionnaires and Statistical Package for Social Science (SPSS) had been used to analyze the data. The level of usage of payWave cards among consumers in Kuala Lumpur was found moderate. Meanwhile, there was a significant relationship between the data security and privacy and the usage behavior towards payWave cards. The findings of this study enhance the knowledge regarding payWave cards and provide practical contributions to the banking institutions and policy makers in increasing the use of payWave cards among consumers.
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2008; Lucas, 1975; Robinson et al., 2005; Sheppard et al., 1988), information/knowledge sharing (Bock and Kim, 2002; Bock et al., 2005), marketing (Lutz, 1991; Shih, 2004; Hansen and Solgaard, 2004), health (Manstead, Proffitt and Smart, 1983; Baker et al., 1996; Fishbein, 2008), management (Wu, 2003), renewable energy (Bang et al., 2000) could be given as an example to these areas. TRA could be used to understand almost all kinds of behavior (Ajzen and Fishbein, 1980), and that is why it will also be beneficial to comprehend computer usage behavior. Accordance with Davis et al. (1989), many researchers such as Wu (2003), Kuo and Young (2008), Rehman et al. (2007) have used TRA as a starting point to their studies, which explore IT usage behavior.
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Internet connectivity and its relevant use on mobile through data can provide access to range of life enhancing services for Bottom of Pyramid Market (BoP) market. This paper is intended to provide a valuable insight for telecom players and policy makers to segmentrelatively unexplored BoP market. The recent phenomenon of mobile data usage behavior of telecom customers in the BoP market based on a large sample survey of telecom usersusing convenience random sampling is discussed in this study. Hypothesis of the study was mobile data consumption has a significant impact on various demographic factors. The questionnaire was administered to masons on construction sites, roadside vendors,and helpers of shop-keepers, office boys, Cycle Rickshaw pullers and house maids working in urban set up but falls in the category of BoP market segment. Data is analyzed using SPSS version 20. Descriptive statistical tools like frequency distribution and Cross Tabulations, Chi Square and ANOVA are used to interpret the data. Scope of the study will help the marketers to analyze the impact of mobile data usage by BoP customers thus helping them to provide data services relevant to improve their livelihood.
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The survey was conducted among 100 students from ten leading colleges around the state. The period of study is January-April 2016. The close ended questionnaire comprised of demographic details such as age, gender, educational qualification, Course of study and monthly family income of the respondent. It is followed by a set of questions to analyze the mobile internet usage behavior of the students. Part three comprised of an attitudinal scale to observe the perception of students towards M- Learning. A pilot study was conducted to determine internal consistency and avoid ambiguity in the questionnaire.
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A statistical tool called Factor Analysis was used to analyze data that was collected through questionnaire. Factor analysis includes cluster of strategies that are primarily used for data reduction and summarization. Among various variables in research, few variables gather to denote certain factors that influence social media usage behavior.After selecting a method for factor extraction, the next step is to decide how any factors should be retained for rotation. Rotation is done to simplify and clarify the data structure. Varimax, quartimax, and equimax are commonly available orthogonal methods of rotation. Factor analysis is a large-sample procedure. If the sample is not large then generalization and replicable results are likely to show.
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The purpose of this paper is to extend the understanding of the drivers of social media in higher education institutions (HEIs) in an emerging economy. This research adopts the Technology Acceptance Model but included subjective norm, perceived playfulness, Internet reliability and speed as additional constructs. With these inclusions, the model is appropriate and relevant in explaining users ’ adoption and usage behavior of social media. Data from 500 students from public and private HEIs in the Philippines were collected and analyzed. We used a combination of statistical analyses such as the Principal Component Analysis (PCA) and Structural Equation Modeling (SEM) in analysing the complex relationships between determinants of these technologies. The research demonstrated that perceived usefulness, perceived ease of use, subjective norm, and perceived playfulness (happiness) are robust predictors of usage behavior of students. However, Internet reliability and speed were only significant in (some) public HEIs. This evidence may be explained by the fact that information and communications technology (ICT) infrastructure in public HEIs is not a priority or underinvested in developing countries. On the other hand, the analysis between public and private HEIs undertaken here extends our understanding towards the different behaviors of users. The findings, though
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DOI: 10.4236/tel.2018.813176 2805 Theoretical Economics Letters regarding the spend on various commodities. The theory of planned behavior (TBP) (Ajzen 1991)  is an intention model that explains and predicts beha- vior in multiple contexts (Chang 1998) . Ajzen defines intention as the will to undertake a particular behavior. As per Ajzen, intention has three cognitive antecedents. The first one is the individual’s attitude (A), which could be favora- ble or otherwise for the target behavior. The second pertains to the subjective norms (SN) of the reference groups, which could include the opinion of family, friends, etc. on a behavior. Finally, the perceived behavior control (PBC) high- lights how easy or difficult it was for the individual to perform that behavior. It was observed that research on usage intention could not completely rely on the evidence from other domains to validate intention and establish an antecedent of big data usage behavior.
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This research’s objects are variety of citizens who have been using E-government services in Vietnam. The survey questionnaire was designed to collect data which consisted items related to respondent’s profile, possible factors affecting usage behavior in using E-government services in Vietnam. The scales used in the questionnaire were largely built upon the scope and structure of previous studies. Constructs were measured based on seven-point Likers- scales ranging from strongly disagree (1) to strongly agree (7). The questionnaires were sent directly to the participants and the online questionnaires. Out of 248 self- administered questionnaires distributed, only 232
In the literature, most of traditional security models are unfortunately static. They in fact respond to user requests just by yes or no. Recently, some interesting works on access control framework that model obligations was proposed    . The formalization of the obligation is different from one model to another. In XACML for example, obligations are all operations that must be filled in conjunction with the application of the authorization decision. In  and , there is a difference between provisions and obligations: provisions are actions or conditions that must be satisfied before an access decision is made, while obligations are actions that must be satisfied by the users or the system after the access decision is made. The specification language of obligations in  distinguishes between usages formulas and obligations formulas: usage formulas concern operations on the data that must be protected, while obligations formulas are conditions on the use of data. An obligation formulas become
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This research proposed to study corrosion behavior and evaluate lifetime of the metal sheet cladding and roofing when exposed to actual usage environment using electrochemical corrosion test method according to ASTM G5 , G59 , G96  and G106 . The reaction between galvanized steel and simulated acid rain (SAR) would be occurred through coating surface which was porous layers. The corrosion rate was predicted by EIS method obtained from potentiostat device according to ASTM G3  and G102 . The analyzed penetration and corrosion rate can be used to estimate lifetime of wall and roof panel and help developing standard for testing and designing of wall and roof panel system in the future work of power plant in Thailand.
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The Research work has mainly focused for teen students to analyze the stress level according to parental behavior and more usage of digital gadgets. In future we can also focus to find the stress level of Kids and also applicable to find the stress of Employers in industries. The same algorithm will also apply to find the anxiety level . by comparing with the new algorithm , Artificial Intelligence based algorithms will also be suitable to apply.
Internet is acting as a major source of data. As the number of web pages continues to grow the web provides the data miners with just the right ingredients for extracting information. In order to cater to this growing need a special term called Web mining was coined. Web mining makes use of data mining techniques and deciphers potentially useful information from web data. Web Usage mining deals with understanding the behavior of users by making use of Web Access Logs that are generated on the server while the user is accessing the website. A Web access log comprises of various entries like the name of the user, his IP address, number of bytes transferred timestamp etc. A variety of Log Analyzer tools exist which help in analyzing various things like users navigational pattern, the part of the website the users are mostly interested in etc. The present paper makes use of such log analyzer tool called Web Log Expert for ascertaining the behavior of users who access an astrology website. It also provides a comparative study between a few log analyzer tools available.
Though the authors proposed an online recommender system, the semantic information is not integrated into web usage mining. The authors in  used sequential mining for pattern generation by integrating semantic information however there was no technique to group the user. In this research paper the authors have advanced an architecture which adapts the idea of online and offline phase by integrating the semantic information in the offline phase. The list of recommendation is generated by using maximum subsequence problem. The system data flow of the recommender system is shown in Fig 1.
Prediction of logs is the main activity. The prediction is analyzed by using the attributes and categories in weblog, which is based on user preference. The two types of prediction process one is online and another is offline with respect to web server activity. Offline data analyze historical data, such as log file or weblogs, which are captured from server. Weblog used in online whenever that user comes online for next time. Based on previous user navigation we can identify user behavior that is taken from weblogs. 1.1 Objectives
• In 2012, Dr. A. R. Patel and Renata Ivancsy  Web usage mining predict the user navigation behavior based on the preferences in website, User navigation technique uses data preprocessing suggested that the weblog contains raw log format, so convert to unprocessed weblog to processed weblog using data preprocessing, the data preprocessing technique contains Data Cleaning, User identification, Session Identification, content retrieval and path completion to get user navigation pattern. After getting the processed log, the given log is converted in to sequence of patterns based on user’s pattern.
The purpose of this study is about the influencing the worker behavior when use the technology to make sure their job effectively and efficient. In order to reduce the risk do the job, various ways and idea to be applied to ensure more orderly manner or systematic working.
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the time limit control, extensive browsing and texting logs, and location features. (3) ESET, which filters websites, manages application, and trails whereabouts faster. In addition, it is only applied in the family scope, where prevention is based on rules that are possibly broken on instances where changes occur in the functional environment. Furthermore, there are no historical data on behavior while using smartphones, hence, implementing dynamic treatments are not possible . Conversely, several forms are dedicated in the prevention of smartphone addiction, e.g., Offtime, Moment, BreakFree, FlipD, and AppDetox. These are used and configured individually by the owner with the settings of limiting application usage time, filtering website and communication, as well as family tracking. In addition, BreakFree is specifically able to provide a score of addiction. . Advance applications for recognizing smartphone user behavior of students, proposed by  are capable of recording usage through the installation of mobile applications, which is, therefore, sent to a server, with attributes of its name, time and duration of use. Supported by pattern analysis tools, clinicians are able to make suggestions, which are subsequently sent back to the end user. Therefore, the despondence of this research is based on , in order for students' smartphone behavior to be mapped in the classification of addictive and non-addictive. Unfortunately, no conclusion about this pattern characterization exists, while this application is not easy to use, especially for people with no data science capability, including ordinary parents and teachers. This is way,  proposed the intelligent recommender system (IRS), which is able to provide recommendations, either
Internet usage and developments in technology growth around the world have grown rapidly and changed the lives of millions of people during the last decade  . In- ternationally, there have been many researches on the Internet usage and most of them found that the Internet usage is most prevalent among younger and more educated people for students and teachers; the Internet is becoming an increasingly important part of the educational process in a classroom and in a textbook .
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Studies like these, as well as this study might be useful when it comes to designing media or launching new products, e.g. When the motivations of people and factors that lead to media consumption are known, the product could be adapted to the needs. Also, insight would be given in peoples’ motivations and usage criteria that might explain why the usage of a certain medium declines, or another medium becomes more and more important for users. When understanding the peoples’ reasons of why they prefer a certain medium instead of another, the process of why and how media develop through certain phases would be better understood. This might be important for, among others, media psychologists and media designers, as well as for editors of (online) newspapers or other information media. Media designers and editors could further investigate what factors of their medium they themselves could influence that lead to stronger reading habits, which in turn, might lead to a more intensive consumption of their media. As results of this study suggest, habit strength is a significant predictor of both, reading an online newspaper as well as a traditional newspaper. However, readers of online newspapers seem to score higher on habit strength, although habit strength seems to stronger explain the usage of traditional newspapers. Media psychologists might study if this effect is shown in similar contexts and media designers and editors could use the results to make their media more
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