Applying E-Commerce in business Essay Example | Topics and Well Written Essays - 3000 words

Applying E-Commerce in business - Essay Example They can likewise utilize an assortment of stages to get to these administrations model: mobiles, workstations and PCs. This foundation contains all inclusive measures and is internationally reachable. It is wealthy in decent variety and suits recordings, sounds and text informing. They empower firms to have live gatherings, texting (IM), document moving and text based live talk over the globe. Online business foundation empowers successful interfacing between customers in multi stratums including purchasers, makers and merchants. It diminishes the expense and builds the nature of business exchanges. It permits customers to modify and customize the administrations as indicated by the prerequisites of various gatherings (Turban, et. al, 2012). Web based business creates various monetary advantages to the organizations because of the above attributes, for example, diminishing cost, expanding extravagance in correspondence, growing business sector and upgrading client relations. The exp ense of making a trip to commercial center is spared and the physical nearness is not, at this point required for haggling in virtual markets. Recordings, sounds, pictures and designs utilized in virtual markets can recreate the genuine shopping experience to purchasers. Market of the cutting edge business has extended generally over the globe. It isn't compelled by nation fringes, racial, strict, language and ethnicity based contrasts. It doesn't include unbending time regions. Billions of expected customers and different makers can meet in virtual commercial centers. Customer turns into a co-member in advertising process. Consequently the items can be redone as per singular inclinations not at all like in conventional promoting techniques (Turban, et. al, 2012). It tends to be combined with contemporary patterns in Information Technologies, for example, distributed computing. Advantages of Cloud registering for E-Commerce Based on the foreseeing examinations by Nelson (2010) it is determined that 80% extent of all out E-business on the planet will be taken over by distributed computing inside 10 years. Handling of installments utilizing the web and the networks include recognizable proof of exchange ID, client name, dollar sum, date/time of exchange and status of the installment. All these can be joined in â€Å"cloud† framework at a nearly lower cost and a higher productivity (Knowledge Center, 2011). Consequently distributed computing portrays the expected development of E-business too. As per the meaning of Mell, and Grance (2011), â€Å"cloud processing is a model for empowering pervasive, advantageous, on-request arrange access to a mutual pool of configurable figuring assets (e.g., systems, servers, stockpiling, applications, and administrations) that can be quickly provisioned and discharged with negligible administration exertion or specialist co-op interaction† (p.2). The above definition contains various qualities of E-business founda tion. Monetary attributes of distributed computing are as per the following. On-request self-administration includes utility based access to the cloud without including of human segment for offering the assistance each time a client needs it. Wide system gives heterogeneous stages. Asset pooling permits amassing the capacity, memory, preparing limit and system transfer speed of inert PCs over the globe and various clients who are unconscious of the particular area where the administration is given from. Fast flexibility includes quickly and boundlessly offering the types of assistance while guaranteeing its accessibility, availability and

Unit 4 Scenario Essay Example | Topics and Well Written Essays - 500 words

Unit 4 Scenario - Essay Example The term of the parcel begins from 6pm to 12 PM, state, 6 hours of the day. Additionally, this parking garage will be guided by a staff part at an expense of $10 every hour. It implies that the staff should be paid a measure of $60 every day, and $1800 every month. Other than this, an extra cost will be charged for the advantageous utilization of parking area. In this manner, so as to assess and receive the most suitable choice in regard of the stopping and support cost, it is important to break down the negligible income and peripheral costing method. Under peripheral costing, just factor costs are charged to cost units. â€Å"Comparable to any benefit augmenting firm, a consummately serious firm delivers the amount of yield in the short run that produces the most extreme distinction between complete income with all out cost, which is financial benefit. This benefit boosting level of creation is likewise accomplished by the correspondence between peripheral income and negligible expense. At this creation level, the firm can't expand benefit by changing the degree of creation. The examination of negligible income and minimal expense can be accomplished through a table of numbers or with peripheral income and minor expense curves† (Marginal Analysis, 2007). The arrangement is proposed by the bank was to charge a greater expense for parking garage. This is on the grounds that, through higher charging for stopping, it is conceivable to lessen the issue related with the stopping in a specific way. The fundamental purpose behind the higher charge set by the bank is that to make it conceivable the accessibility of appropriate stopping offices. At whatever point the charge for stopping builds, at that point the time spent for stopping will naturally be diminished. Because of this, it is conceivable to make the parking area helpfully whenever adequately. What's more, so as to conquer this particular issue, it is important to think about the idea of immaculate rivalry and

My Grandfather Essay Sample - Writing a Dear Letter

My Grandfather Essay Sample - Writing a Dear LetterA sample letter from your grandfather can be a great tool in crafting a perfect my grandfather essay sample. This is a letter that you can write yourself, and will help you to focus on the importance of the essay and how it relates to your writing experience. This kind of letter will be a helpful tool in learning how to write an effective letter.Writing a letter is a skill that will need to be mastered. People, including your grandparents, have different styles, but if you're going to be writing a letter, you want to make sure that it is a good one. It is a good idea to take notes about your grandfather's letter to understand his style, if you can. In this article, we will share with you some tips on writing your grandfather's letter.A my grandfather's letter should contain specific information about you and what you are doing at the moment. The content is very important in making your letter good. Your grandfather wants to know the details of your employment, and if he could meet with you for coffee, he wants to know where you are. You want to focus on giving him specific details that can only be given when he is willing to meet with you. He would like to know all the details of your work situation, and you want to give him the ones that he needs.Your grandfather will also like the information you provide about yourself. Be sure to answer his questions, and don't leave anything out. He wants to know how you got into your current job. If you are the best worker, he may give you a raise. You need to provide him with enough details to give him a good reason to give you more money.He may also want to know how your grandfather became rich. He may not know exactly what it was, but he wants to know that you did get a raise because of the amount of work you have done. He does not want to hire someone who has no work ethic, but he would like to know that you have. So if you've become a better employee, your grandfather may be more willing to hire you.There are many samples of my grandfather letters that you can find online. It is important to remember to make them look good. Do not use anything less professional than you would want to, especially if you are just starting to learn about writing an essay for your grandfather. Your grandfather may be very busy, so you want to make it look professionally done so that he can easily read it.Next, you want to start looking for samples of my grandfather essays that you can use. You want to make sure that the letters that you do create are clear and concise. A good sample will be a list of questions that will be asked by your grandfather, but at the same time, you want to make sure that the questions are related to what your grandfather is asking about.Lastly, and most importantly, is your grandfather's age. Make sure that you tell him your grandfather's age, and to keep the date the letter was written. By doing this, you will avoid some complications in your grandfather's life, and he will remember your humble story.

Gender College Study

Sample details Pages: 14 Words: 4194 Downloads: 7 Date added: 2017/06/26 Category Statistics Essay Tags: Gender Essay Did you like this example? This chapter presents the results of the study. Included are an analysis of the five research questions and the six hypotheses of the study. This chapter concludes with a summary of the information presented in this chapter concerning the quantitative statistical findings of this study. As previously indicated, job satisfaction is a term that is difficult to describe as a single construct, and the definition of job satisfaction varies between studies (Morice Murray, 2003; Protheroe, Lewis Paik, 2002; and Singer, 1995). In higher education, a number of researchers have discussed the importance of continuous research on job satisfaction among community college faculty (Bright, 2002; Green, 2000; McBride, Munday, Tunnell, 1992; Milosheff, 1990; Hutton Jobe, 1985; and Benoit Smith 1980). A reason suggested for the continuous study of community college faculty, is the value of data received from such studies in developing and improving community college faculty and their practices (Truell, Price, Joyner, 1998). The purpose of this study was to examine job satisfaction of community college instructional faculty in regards to their role as teachers. Don’t waste time! Our writers will create an original "Gender College Study | Education Dissertations" essay for you Create order Analysis of Research Questions Research question one sort to describe the sociodemographic characteristics of community college instructional faculty. This research question included three variables (gender, age, and race/ethnicity). Sociodemographic Characteristics Gender There were 371 participants in the sample, of which 188 were male and 183 were female. In regards to gender, the analysis showed that 51% of the sample size included males and 49% of the sample size were female. Table 1 identifies the frequency and percentage results as they relate to gender of community college faculty. Table 1. Gender Distribution of Community College Instructional Faculty Gender Percent Frequency Male 51% 188 Female 49% 183 Total 100% 371 Age The sample size consisted of 371 participants. For age, the analysis displayed that 16% of the faculty were both under 30 and between ages 30 and 34 while17% were between ages 35 and 39. 15% of community college instructional faculty were between 40 and 44, while 14% were in the age range of 45 to 50. The last age range consisted of participants who were 50 or over, which was 21%. Even though the largest percentage of faculty members are 50 or over, faculty members who are 34 or under total 32% which indicates that the majority of faculty are under the age of 34. Table 2 identifies the frequency and percentage results as they relate to the variable of age of community college faculty. Table 2. Age Distribution of Community College Instructional Faculty Age Percent Frequency Under 30 16% 60 30-34 16% 60 35-39 17% 65 40-44 15% 57 45-49 14% 51 50 and over 21% 79 Total 100% 371 Race and Ethnicity The sample size consisted of 371 participants. The variable race/ethnicity showed that 83% of the participants were White, Non-Hispanic; 7% were Black, Non-Hispanics; 3% were Asian, Non-Hispanics; 1% were both American Indian, Non-Hispanics and Pacific Islanders Non-Hispanics; 2% were More than one race, Non-Hispanic; and 5% were Hispanics. Over 80% of the participants (308) were White, Non-Hispanic. Table 3 identifies the frequencies and percentages for the variable of race/ethnicity. Table 3. Race/Ethnicity of Community College Instructional Faculty Race/Ethnicity Percent Frequency White, Non-Hispanic 83% 308 Black, Non-Hispanic 7% 25 Asian, Non-Hispanic 3% 11 American Indian, Non-Hispanic 1% 1 Pacific Islanders, Non-Hispanic 1% 1 More than one race, Non-Hispanic 2% 7 Hispanics 5% 18 Total 100% 371 Research question two sort to describe the nature of employment characteristics of community college instructional faculty. This research question included three variables (rank, employment status, and tenure status). Nature of Employment Characteristics Employment Status There were 371 participants in the sample, of which 126 were employed full time and 245 were employed part time. In regards to employment status, the analysis showed that 34% of the sample size was employed full time and 66% of the sample size were employed part time. Table 4 identifies the frequency and percentage results as it relates to employment status of community college faculty. Table 4. Employment Status Distribution of Community College Instructional Faculty Employment Status Percent Frequency Full time 34% 126 Part time 66% 245 Total 100% 371 Rank The sample size consisted of 371 participants. In regards to rank, the analysis displayed that 9% of the sample size was identified as professors. Associate professors were identified at 5% of the sample size while Assistant professors were identified at 4%. Instructors were identified as 45% of the participants and lecturers were identified at 2%. Faculty with other titles were identified at 30% and 5% of the participants answered the question as not applicable. More than 40% of the participants (167) were identified as instructors. Table 5 identifies the frequency and percentage results as they relate to the ranking of community college faculty. Table 5. Rank Distribution of Community College Instructional Faculty Rank Percent Frequency Professor 9% 30 Associate professor 5% 19 Assistant professor 4% 15 Instructor 45% 167 Lecturer 2% 7 Other titles 30% 111 Not applicable 5% 22 Total 100% 371 Tenure Status The sample size consisted of 371 participants. In regards to tenure status, the analysis showed that 18% of the faculty were tenured; 6% of faculty were on a tenure track, but are not tenured; and 76% of faculty are not on a tenure track. More than 70% of the participants (282) were identified as faculty not on a tenure track. Table 6 identifies the frequency and percentage results as they relate to the tenure status of community college faculty. Table 6. Tenure Status of Community College Instructional Faculty Tenure Status Percent Frequency Tenured 18% 67 On tenure track, but not tenured 6% 22 Not on tenure track 76% 282 Total 100% 371 Job Satisfaction of Community College Instructional Faculty Research question three was designed to describe the job satisfaction of community college instructional faculty based on the eight components (Authority to make decisions; Benefits; Equipment/facilities; Instructional support; Overall; Salary; Technology-based activities; and Workload) of job satisfaction from the National Study of Postsecondary Faculty Survey NSOPF: 04. The sample size consisted of 366 participants. In regards to job satisfaction, the analysis showed that 73% of the faculty were very satisfied with authority to make decision; 34% of faculty were somewhat satisfied with benefits; 44% of faculty were very satisfied with equipment and facilities; 40% were somewhat satisfied with instructional support; 55% were very satisfied with overall job satisfaction; 42% were somewhat satisfied with salary; 53% were very satisfied with technology-based activities; and 50% of faculty were very satisfied with workload. Table 6 identifies the frequency and percentage results as they relate to the job satisfaction of community college faculty. Table 7. Job Satisfaction of Community College Instructional Faculty Satisfaction Percent Frequency Authority to Make Decisions Very satisfied 73% 268 Somewhat satisfied 22% 81 Somewhat dissatisfied 4% 14 Very dissatisfied 1% 4 Total 100 366 Benefits Very satisfied 27% 106 Somewhat satisfied 34% 127 Somewhat dissatisfied 19% 70 Very dissatisfied 18% 67 Total 100 371 Equipment/facilities Very satisfied 44% 161 Somewhat satisfied 38% 140 Somewhat dissatisfied 14% 51 Very dissatisfied 4% 15 Total 100 366 Instructional support Very satisfied 37% 134 Somewhat satisfied 40% 147 Somewhat dissatisfied 17% 62 Very dissatisfied 6% 23 Total 100 366 Job overall Very satisfied 55% 203 Somewhat satisfied 38% 141 Somewhat dissatisfied 6% 22 Very dissatisfied 1% 5 Total 100 371 Salary Very satisfied 29% 106 Somewhat satisfied 42% 157 Somewhat dissatisfied 18% 67 Very dissatisfied 11% 41 Total 100 371 Technology-based activities Very satisfied 53% 195 Somewhat satisfied 35% 129 Somewhat dissatisfied 9% 32 Very dissatisfied 3% 10 Total 100 366 Workload Very satisfied 50% 187 Somewhat satisfied 34% 127 Somewhat dissatisfied 11% 41 Very dissatisfied 4% 17 Total 100 371 Predictive Relationship between Sociodemographic Characteristics, Nature of Employment Characteristics and Job Satisfaction Research questions four and five examined the predictive relationship between gender, nature of employment, (rank, employment status, and tenure status) and job satisfaction of community college instructional faculty. Associated with this research question were six hypotheses. The hypotheses were tested using a multiple linear regression model that included two independent variables (gender and rank, gender and employment status, and gender and tenure status) and the eight components of the dependent variable, job satisfaction (Authority to make decisions regarding instructional practice, Benefits, Equipment/facilities for instructional use, Instructional support, Overall satisfaction, Salary, Technology-based activities, and Workload). The findings for each of the hypotheses are discussed below. Gender, Rank, and Job Satisfaction H01:There is no statistical difference in job satisfaction of community college instructional faculty based upon gender and rank. Ha1:There is a statistical difference in job satisfaction of community college instructional faculty based upon gender and rank. The regression model was not significant between the independent variables (gender and rank) and the dependent variable job satisfaction (Authority to make decisions regarding instructional practice), F (2, 363), = 0.280, p = .756 (See Table 8). A non-significant relationship was found between gender, rank, and component one. The coefficients were: t = -.321 (gender) and -.670 (rank), df = 363, and p .05 for both gender (.748) and rank (.504). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. Table 8. Summary Regression Results for Authority to Make Decisions Model Sum of Squares df Mean Square F p Regression .234 2 .117 .280 .756 Residual 151.878 363 .418 Corrected Total 152.112 365 The regression model was not significant between the independent variables (gender and rank) and the dependent variable job satisfaction (Benefits), F (2, 363), = 4.203, p = .016. The total model produced an r-square value of 0.023 (See Table 9). The r-square value indicated that approximately 1% of the variation in benefits was accounted for by the combined variation of the 2 independent variables (gender and rank). The coefficients were: t = .050 (gender) and 2.897 (rank), df = 363, and p .05 for gender (.960) and p.05 for rank (.004). Therefore, the null hypothesis was rejected because p .05 and p .05 with alpha= .05. Table 9. Summary Regression Results for Benefits Model Sum of Squares df Mean Square F p Regression 9.431 2 4.716 4.203 .016 Residual 407.247 363 1.122 Corrected Total 416.678 365 R-Square = 0.023 The regression model was not significant between the independent variables (gender and rank) and the dependent variable job satisfaction (Equipment/facilities for instructional use), F (2, 363), = 1.045, p = .353. The total model produced an r-square value of 0.006 (See Table 10). The r-square value indicated that approximately 1% of the variation in equipment/facilities for instructional use was accounted for by the combined variation of the 2 independent variables (gender and rank). The coefficients were: t = .793 (gender) and -1.225 (rank), df = 363, and p .05 for both gender (.428) and rank (.221). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. The regression model was not significant between the independent variables (gender and rank) and the dependent variable job satisfaction (Instructional support), F (2, 363), = .370, p = .691. The total model produced an r-square value of 0.002 (See Table 11). Table 10. Summary Regression Results for Equipment/facilities for Instructional Use Model Sum of Squares df Mean Square F p Regression 1.441 2 .721 1.045 .353 Residual 250.187 363 .689 Corrected Total 251.628 365 R-Square = 0.006 The r-square value indicated that approximately 1% of the variation in instructional support was accounted for by the combined variation of the 2 independent variables (gender and rank). The coefficients were: t = .392 (gender) and -.773 (rank), df = 363, and p .05 for both gender (.695) and rank (.440). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. Table 11. Summary Regression Results for Instructional Support Model Sum of Squares df Mean Square F p Regression .570 2 .285 .370 .691 Residual 279.804 363 .771 Corrected Total 280.374 365 R-Square = 0.002 The regression model was not significant between the independent variables (gender and rank) and the dependent variable job satisfaction (Overall satisfaction), F (2, 363), = 13.505, p = .000. The total model produced an r-square value of 0.069 (See Table 12). The r-square value indicated that approximately 1% of the variation in overall satisfaction was accounted for by the combined variation of the 2 independent variables (gender and rank). The coefficients were: t = -5.169 (gender) and -.436 (rank), df = 363, and p .05 for gender (.000) and p .05 for rank (.663). Therefore, the null hypothesis was rejected because p .05 and p .05 with alpha= .05. Table 12. Summary Regression Results for Overall Satisfaction Model Sum of Squares df Mean Square F p Regression 19.269 2 9.634 13.505 .000 Residual 258.950 363 .713 Corrected Total 278.219 365 R-Square = 0.069 The regression model was not significant between the independent variables (gender and rank) and the dependent variable job satisfaction (Salary), F (2, 363), = .050, p = .951. The total model produced an r-square value of 0.000 (See Table 13). The r-square value indicated that approximately 0% of the variation in salary was accounted for by the combined variation of the 2 independent variables (gender and rank). The coefficients were: t = .220 (gender) and -.230 (rank), df = 363, and p .05 for gender (.826) and for rank (.818). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. The regression model was not significant between the independent variables (gender and rank) and the dependent variable job satisfaction (Technology-based activities), F (2, 363), = .050, p = .819. Table 13. Summary Regression Results for Salary Model Sum of Squares df Mean Square F p Regression .091 2 .045 .050 .951 Residual 331.857 363 .914 Corrected Total 331.948 365 R-Square = 0.000 The total model produced an r-square value of .001 (See Table 14). The r-square value indicated that approximately 0% of the variation in technology based activities was accounted for by the combined variation of the 2 independent variables (gender and rank). The coefficients were: t = .081 (gender) and -.628 (rank), df = 363, and p .05 for both gender (.936) and rank (.531). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. Table 14. Summary Regression Results for Technology-based activities Model Sum of Squares df Mean Square F p Regression .245 2 .123 .199 .819 Residual 223.219 363 .615 Corrected Total 223.464 365 R-Square = 0.001 The regression model was not significant between the independent variables (gender and rank) and the dependent variable job satisfaction (Workload), F (2, 363), = .557, p = .573. The total model produced an r-square value of 0.003 (See Table 15). The r-square value indicated that approximately 0% of the variation in workload was accounted for by the combined variation of the 2 independent variables (gender and rank). The coefficients were: t = .312 (gender) and -1.015 (rank), df = 363, and p .05 for both gender (.756) and rank (.311). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. Table 15. Summary Regression Results for Workload Model Sum of Squares df Mean Square F p Regression 1.218 2 .609 .557 .573 Residual 396.607 363 1.093 Corrected Total 397.825 365 R-Square = 0.003 Gender, Employment Status, and Job Satisfaction H02:There is no statistical difference in job satisfaction of community college instructional faculty based upon gender and employment status. Ha2:There is a statistical difference in job satisfaction of community college instructional faculty based upon gender and employment status. The regression model was not significant between the independent variables (gender and employment status) and the dependent variable job satisfaction (Authority to make decisions regarding instructional practice), F (2, 363), = .070, p = .932 (See Table 16). A non-significant relationship was found between gender, employment status, and component one. The coefficients were: t = -.355 (gender) and .120 (employment status), df = 363, and p .05 for both gender (.723) and employment status (.904). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. Table 16. Summary Regression Results for Authority to Make Decisions Model Sum of Squares df Mean Square F p Regression .040 2 .020 .070 .932 Residual 104.091 363 .287 Corrected Total 104.131 365 The regression model was not significant between the independent variables (gender and employment status) and the dependent variable job satisfaction (Benefits), F (2, 363), = 26.952, p = .000. The total model produced an r-square value of 0.129 (See Table 17). The r-square value indicated that approximately 1% of the variation in benefits was accounted for by the combined variation of the 2 independent variables (gender and employment status). The coefficients were: t = -.140 (gender) and 7.340 (employment status), df = 363, and p .05 for gender (.889) and p.05 for employment status (.000). Therefore, the null hypothesis was rejected because p .05 and p .05 with alpha= .05. The regression model was not significant between the independent variables (gender and employment status) and the dependent variable job satisfaction (Equipment/facilities for instructional use), F (2, 363), = 2.754, p = .065 (See Table 18). Table 17. Summary Regression Results for Benefits Model Sum of Squares df Mean Square F P Regression 51.741 2 25.870 26.952 .000 Residual 348.437 363 .960 Corrected Total 400.178 365 R-Square = 0.129 The coefficients were: t = -.016 (gender) and -2.347 (employment status), df = 363, and p .05 for gender (.987) and p .05 for employment status (.019). Therefore, the null hypothesis was rejected because p .05 and p .05 with alpha= .05. Table 18. Summary Regression Results for Equipment/facilities for Instructional Use Model Sum of Squares df Mean Square F p Regression 3.331 2 1.665 2.754 .065 Residual 219.489 363 .605 Corrected Total 222.820 365 The regression model was not significant between the independent variables (gender and employment status) and the dependent variable job satisfaction (Instructional support), F (2, 363), = 1.844, p = .160 (See Table 19). The coefficients were: t = -.308 (gender) and -1.897 (employment status), df = 363, and p .05 for gender (.758) and p .05 for employment status (.059). Therefore, the null hypothesis was rejected because p .05 and p .05 with alpha= .05. Table 19. Summary Regression Results for Instructional Support Model Sum of Squares df Mean Square F p Regression 2.651 2 1.326 1.844 .160 Residual 260.977 363 .719 Corrected Total 263.628 365 The regression model was not significant between the independent variables (gender and employment status) and the dependent variable job satisfaction (Overall satisfaction), F (2, 363), = .637, p = .529. The total model produced an r-square value of 0.003 (See Table 20). The r-square value indicated that approximately 0% of the variation in overall satisfaction was accounted for by the combined variation of the 2 independent variables (gender and employment status). The coefficients were: t = -.652 (gender) and -.924 (employment status), df = 363, and p .05 for both gender (.515) and employment status (.356). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. The regression model was not significant between the independent variables (gender and employment status) and the dependent variable job satisfaction (Salary), F (2, 363), = .058, p = .944 (See Table 21). The coefficients were: t = .260 (gender) and -.216 (employment status), df = 363, and p .05 for gender (.795) and for employment status (.829). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. Table 20. Summary Regression Results for Overall Satisfaction Model Sum of Squares df Mean Square F p Regression .516 2 .258 .637 .529 Residual 146.916 363 .405 Corrected Total 147.432 365 R-Square = 0.003 Table 21. Summary Regression Results for Salary Model Sum of Squares df Mean Square F p Regression .100 2 .050 .058 .944 Residual 315.441 363 .869 Corrected Total 315.541 365 The regression model was not significant between the independent variables (gender and employment status) and the dependent variable job satisfaction (Technology-based activities), F (2, 363), = .529, p = .589 (See Table 22). The coefficients were: t = -.334 (gender) and -.975 (employment status), df = 363, and p .05 for both gender (.739) and employment status (.330). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. The regression model was not significant between the independent variables (gender and employment status) and the dependent variable job satisfaction (Workload), F (2, 363), = 13.418, p = .000. Table 22. Summary Regression Results for Technology-based activities Model Sum of Squares df Mean Square F p Regression .523 2 .261 .529 .589 Residual 179.130 363 .493 Corrected Total 179.653 365 The total model produced an r-square value of 0.069 (See Table 23). The r-square value indicated that approximately 1% of the variation in workload was accounted for by the combined variation of the 2 independent variables (gender and employment status). The coefficients were: t = 1.351 (gender) and -4.995 (employment status), df = 363, and p .05 for gender (.178) and p .05 for employment status (.000). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. Table 23. Summary Regression Results for Workload Model Sum of Squares df Mean Square F p Regression 17.895 2 8.947 13.418 .000 Residual 242.062 363 .667 Corrected Total 259.956 365 R-Square = 0.069 Gender, Tenure Status, and Job Satisfaction H03:There is no statistical difference in job satisfaction of community college instructional faculty based upon gender and tenure status. Ha3:There is a statistical difference in job satisfaction of community college instructional faculty based upon gender and tenure status. The regression model was not significant between the independent variables (gender and tenure status) and the dependent variable job satisfaction (Authority to make decisions regarding instructional practice), F (2, 363), = 0.120, p = .887 (See Table 24). A non-significant relationship was found between gender, tenure status, and component one. The coefficients were: t = -.442 (gender) and .222 (tenure status), df = 363, and p .05 for both gender (.659) and tenure status (.825). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. Table 24. Summary Regression Results for Authority to Make Decisions Model Sum of Squares df Mean Square F p Regression .073 2 .037 .120 .887 Residual 110.465 363 .304 Corrected Total 110.538 365 The regression model was not significant between the independent variables (gender and tenure status) and the dependent variable job satisfaction (Benefits), F (2, 363), = 9.706, p = .000. The total model produced an r-square value of 0.051 (See Table 25). The r-square value indicated that approximately 1% of the variation in benefits was accounted for by the combined variation of the 2 independent variables (gender and tenure status). The coefficients were: t = .015 (gender) and 4.405 (tenure status), df = 363, and p .05 for gender (.988) and p.05 for tenure status (.000). Therefore, the null hypothesis was rejected because p .05 and p .05 with alpha= .05. Table 25. Summary Regression Results for Benefits Model Sum of Squares df Mean Square F p Regression 20.959 2 10.479 9.706 .000 Residual 391.916 363 1.080 Corrected Total 412.874 365 R-Square = 0.051 The regression model was not significant between the independent variables (gender and tenure status) and the dependent variable job satisfaction (Equipment/facilities for instructional use), F (2, 363), = 3.790, p = .024. The total model produced an r-square value of 0.020 (See Table 26). The r-square value indicated that approximately 1% of the variation in equipment/facilities for instructional use was accounted for by the combined variation of the 2 independent variables (gender and tenure status). The coefficients were: t = .247 (gender) and -2.746 (tenure status), df = 363, and p .05 for gender (.805) and p .05 tenure status (.006). Therefore, the null hypothesis was rejected because p .05 p.05 with alpha= .05. The regression model was not significant between the independent variables (gender and tenure status) and the dependent variable job satisfaction (Instructional support), F (2, 363), = 2.705, p = .068. Table 26. Summary Regression Results for Equipment/facilities for Instructional Use Model Sum of Squares df Mean Square F p Regression 4.463 2 2.232 3.790 .024 Residual 213.758 363 .589 Corrected Total 218.221 365 R-Square = 0.020 The total model produced an r-square value of 0.015 (See Table 27). The r-square value indicated that approximately 1% of the variation in instructional support was accounted for by the combined variation of the 2 independent variables (gender and tenure status). The coefficients were: t = -.201 (gender) and -2.313 (tenure status), df = 363, and p .05 for both gender (.841) and p .05 tenure status (.021). Therefore, the null hypothesis was rejected because p .05 and p .05 with alpha= .05. Table 27. Summary Regression Results for Instructional Support Model Sum of Squares df Mean Square F p Regression 3.868 2 1.934 2.705 .068 Residual 259.599 363 .715 Corrected Total 263.467 365 R-Square = 0.015 The regression model was not significant between the independent variables (gender and tenure status) and the dependent variable job satisfaction (Overall satisfaction), F (2, 363), = .511, p = .600. The total model produced an r-square value of 0.003 (See Table 28). The r-square value indicated that approximately 0% of the variation in overall satisfaction was accounted for by the combined variation of the 2 independent variables (gender and tenure status). The coefficients were: t = -.484 (gender) and -.878 (tenure status), df = 363, and p .05 for both gender (.629) and for tenure status (.381). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. Table 28. Summary Regression Results for Overall Satisfaction Model Sum of Squares df Mean Square F p Regression .391 2 .196 .511 .600 Residual 139.084 363 .383 Corrected Total 139.475 365 R-Square = 0.003 The regression model was not significant between the independent variables (gender and tenure status) and the dependent variable job satisfaction (Salary), F (2, 363), = .164, p = .849. The total model produced an r-square value of 0.001 (See Table 29). The r-square value indicated that approximately 0% of the variation in salary was accounted for by the combined variation of the 2 independent variables (gender and tenure status). The coefficients were: t = -.485 (gender) and -.296 (tenure status), df = 363, and p .05 for gender (.628) and for tenure status (.767). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. Table 29. Summary Regression Results for Salary Model Sum of Squares df Mean Square F p Regression .269 2 .135 .164 .849 Residual 297.286 363 .819 Corrected Total 297.555 365 R-Square = 0.001 The regression model was not significant between the independent variables (gender and tenure status) and the dependent variable job satisfaction (Technology-based activities), F (2, 363), = 13.722, p = .000. The total model produced an r-square value of .070 (See Table 30). The r-square value indicated that approximately 1% of the variation in technology based activities was accounted for by the combined variation of the 2 independent variables (gender and tenure status). The coefficients were: t = 2.061 (gender) and -4.855 (tenure status), df = 363, and p .05 for both gender (.040) and tenure status (.000). Therefore, the null hypothesis was rejected because p .05 with alpha= .05. The regression model was not significant between the independent variables (gender and tenure status) and the dependent variable job satisfaction (Workload), F (2, 363), = 6.544, p = .002. The total model produced an r-square value of 0.035 (See Table 31). The r-square value indicated that approximately 1% of the variation in workload was accounted for by the combined variation of the 2 independent variables (gender and tenure status). The coefficients were: t = 1.140 (gender) and -3.455 (tenure status), df = 363, and p .05 for gender (.255) and p .05 for tenure status (.001). Therefore, the null hypothesis was rejected because p .05 and p .05 with alpha= .05. Table 30. Summary Regression Results for Technology-based activities Model Sum of Squares df Mean Square F p Regression 16.535 2 8.267 13.722 .000 Residual 218.700 363 .602 Corrected Total 235.235 365 R-Square = 0.070 Table 31. Summary Regression Results for Workload Model Sum of Squares df Mean Square F p Regression 8.363 2 4.182 6.544 .002 Residual 231.946 363 .639 Corrected Total 240.309 365 R-Square = 0.035 Summary The finding of this study showed that the gender of community college instructional faculty was almost equally distributed. In that, 51% were male and 49% were female. Apparently, community colleges are providing instructional opportunities not only for men, but also for women. The findings also showed that the majority of community college instructional faculty were below the age of thirty-four making a combined percentage of 32% for the age ranges of 34-30 and 30 and under, although 21% of community college instructional faculty are fifty years of age or over. Assuming a retirement age of 65, these data indicate the approximately 130 out 371 community college instructional faculty will have to be replaced in the next 15 years. This study also found that the community college instructional faculty ethnic make-up is White, Non-Hispanic at 83%. This indicates that the race of community college instructional faculty has a limited minority presence. Other findings from this study, such as employment status, showed that 66% of community college instructional faculty were employed in part-time status. This is consistent with findings in the literature regarding employment status. The findings also showed that 75% of community college instructional faculty were identified as instructors or had other titles. Since this study was examining the job satisfaction of community college instructional faculty regarding their role as teachers, the finding are not surprising that faculty viewed themselves as instructors. Finally, the finding for research question one, as it relates to tenure status showed that 76% of community college instructional faculty were not on a tenure track. The finding for research question three yielded that community college instructional faculty were either somewhat or very satisfied with all eight components (Authority to make decisions; Benefits; Equipment/facilities; Instructional support; Overall; Salary; Technology-based activities; and Workload) of job satisfaction ranging from 61% to 95%, with Benefits fairing the least at 61%. The results of the regression analysis conducted in this study showed that no significant relationship existed between gender and nature of employment (rank, employment status, and tenure status), and job satisfaction. All three hypotheses were tested at the .05 level of significance. The findings of this study revealed that none of the independent variables are predictive of job satisfaction of community college instructional faculty. The next chapter will present discussion, conclusions, implications, and recommendations of this study.

High-Performance Police Essay - 849 Words

High Performance Police High-performance police organizations involve different approaches to the way law enforcement handle emergency situations. Law enforcement officer’s duties include focusing on the concerns and safety of average citizens. They must embrace the daunting challenge of radically transforming their organizations and their organizational cultures (Henry Bratton, 2003). Policing can deal with the communities, organizations, budgets, and strategies to most effective actions to engage in challenging situations (Henry Bratton, 2003). Many police agencies have adopted the Compstat Paradigm which is a hybrid management style that combines the best and most effective elements of several organizational models as well as†¦show more content†¦If the method is not planned properly the high-performance tactic can fail as a whole. High-Performance police include Problem-Oriented Policing which identifies crime trends based on incidents and searched underlying problems that are responsible for criminal activity. This allows police to work in collaboration with community leaders finding ways to fight crime. For example, there is an annual Police-Oriented Policing conference in the Westin Providence Hotel in Providence, RI, involving police officers, police leaders, crime consultants, and crime researchers discussing their approaches to reducing crime and safety issues. They choose which workshop they want to focus on, at that moment (The Office of Community Oriented Policing Services, U.S. Department of Justice , 2012). Another example, the Berkeley Police Department have implemented the proven crime control model known as the COMPSTAT, short for computer statistics, to reduce crime. 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Types Of Methods For Cheating - 883 Words

There are many forms of cheating and it does not only have to be when a person fabricates someone else’s idea, paper, or answers. The types of methods for cheating have increased throughout the years and it has become very common to take credit for something that you have worked for. Another method that has become increasingly popular through the years is people cheating the government. The people who have been cheating the government believe that they deserve more benefits than what they receive, but the truth is that most of those people do not want to work for what they need which promotes languid behavior (Gabriel). This in turn causes those who actually need those benefits to be rejected. Though some people may disagree and say that they do work for their money, there are a select few who will do whatever is necessary to get what they think they deserve. For instance, a friend of the family confessed to be living with her husband but has claimed to be separated so that sh e and her family can receive benefits. She is able to get food stamps and Medicaid for her children because of her claims. If she did not claim that she was separated, she would not be able to qualify for these benefits. Even though she can afford it, she gets away with cheating the government system. This is obviously unfair to those who actually have to pay for their own insurance and medical care or to those who simply can’t afford it. The people who can’t afford the assistances provided by theShow MoreRelatedPlagiarism : What Is Plagiarism?1583 Words   |  7 Pageshimself. Many reasons and factors are attributed for the use of plagiarism and could be cultural, historical, linguistic, environmental and educational background. Plagiarism is a form of an academic dishonesty, academic misconduct, and a digital cheating. 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Flight Centre Travel Group Ltd †Free Samples to Students

Question: Discuss about the Flight Centre Travel Group Ltd. Answer: Introduction: Investors are the key part of an organization. These stakeholders assist an organization to raise the funds for betterment of the company. Investor must go through the entire related details before making a decision about investing in a particular company or the industry/ In this report, FLIGHT CENTRE TRAVEL GROUP LTD has been taken into the consideration to understand that how a company could be analyzed through analyzing various technique and methods. For identify the worth of the business, debt valuation management, share valuation technique, intrinsic price of the shares, WACC, share analysis, cost of capital etc studies have been done. Through these calculations, it has been identified that whether the investors must invest into the company or not and if the investors would invest into the company then how much return could be got by the investors. FLIGHT CENTRE TRAVEL GROUP LTD is an Australian company. This company is the biggest retail travel outlet in the Australian market. Head quarter of the company is in Brisbane, Australia. This company has listed itself in the Australian stock exchange in 1982. The turnover of the company is around $ 20 billion (Home, 2017). This company is currently employing 20,000 people. Around 2,800 stores have been owned by the company in various countries and the performance of the company is stunning. FLIGHT CENTRE TRAVEL GROUP LTDs annual reports have been analyzed to identify that how the company raises the funds. In this study, the short term and long term debt of the company has been analyzed initially. The short term and long term debt management of the company is as follows: FLIGHT CENTRE TRAVEL GROUP LTD 2017 2016 2015 2014 2013 Long term debt 0 0 0 2 3 Short term debt 56 77 33 43 44 Further, the industrys data has been analyzed. The following are the short term and long term debt of the competitive company of FLIGHT CENTRE TRAVEL GROUP LIMITEED. HELLOWORLD TRAVEL LTD 2017 2016 2015 2014 2013 Long term debt 63 58 28 27 33 Short term debt 0 0 0 1 2 Through this analysis, it has been found that the debt structure of FLIGHT CENTRE TRAVEL GROUP LIMITED is not at all consistent according to the industry standards. Through the analysis over company and industrys debt, it has been found that the other companies in the industry are focusing more on the long term debt while the company is focusing on short term debt. At the same time, the increment rate of concerned company of debt is less than the industry rate. The cost of debt of FLIGHT CENTRE TRAVEL GROUP LIMITED is 0.0263 which express that the company have to pay total 0.0350 parts to the debt holders in terms of interest. Calculation of cost of debt Outstanding debt 0 interest rate 5% Tax rate 0.3 Kd 0.0350 Thus, through the debt valuation study of the company, it has been analyzed that the performance of the companys debt is different from the industry. But the performance of the company is depicting positive influences. It has also been analyzed that the total cost of debt of the company is 3.50%. Share valuation: Further, share valuation study has been performed over the company to analyze the performance of the company in terms of total expenses and the capital structure. The current cost of equity of the company is 4.7041% which depict that the company has to pay total 4.7041% to the shareholders in terms of dividend. Dividend Discount Model Dividend expected 0.017505805 Growth rate 5% Price per share 28.919622 cost of equity 4.7041% In addition, it has been analyzed that the total revenue of the company has been enhanced from last 4 years. The company is performing well in the market so as the turnover of the company is also enhancing rapidly. The earnings and the revenue of the company are as follows: 2017-06 2016-06 2015-06 2014-06 2013-06 Revenue 2544 2625 2363 2207 1945 2017-06 2016-06 2015-06 2014-06 2013-06 Earnings 2.29 2.42 2.55 2.05 2.45 This depict that the performance of the company is quite stable. Currently, the total earnings of the company are 2.29 which are lower than last year but according to the industry performance, companys performance is stunning. Further, the value of shares has been analyzed through the P/E model and the constant dividend growth rate model. The calculations of both the techniques are as follows: Dividend Constant growth Model Dividend expected 0.02 Growth rate 5% Discount rate 3.46% Intrinsic Value (1.48) Share Price 28.919622 Overvalued (Morningstar, 2017) PE Multiple Model Industry PE ratio 9.76 EPS 2.29 Intrinsic Value 22.35 Share Price 28.919622 Overvalued Through both the evaluation, it has been found that the current share price of the company is overvalued. According to the dividend constant growth mode, the intrinsic value of the company is -1.48 and according to the P/E model, the intrinsic value of the shares is $ 22.35. There are various factors which have influenced the shares prices of the company such as discount rate, growth rate, expected dividend, industry P/E ratio etc. The influence of these factors could be shown above (Google finance, 2017). According to the analysis over both the methods, P/E approach is more reliable as this depends over the external sources management and factors and according to this approach, the intrinsic value of the company is $22.35. Still the share price of the company is overvalued which depict that when the share price of the company get down to $ 22.35 then the investors must buy the share of the company (AFR, 2017). Lastly, according to various studies, it has been analyzed that for valuing the share price of the company, various other data could also be considered by the investors such as the revenue of the company, dividend approach of the company, policy and strategy of the company, new projects, diversification, new products etc which directly impact over the share price and the performance of the company. Cost of capital: Cost of capital is an opportunity cost which refers to the specific investment of a company. This is the cost of the company which has been occurred through investing some amount into various projects with equal risk. The current cost of capital of the company is as follows: Calculation of WACC Price Cost Weight WACC Debt 0 0.035 0 0 Equity 1429 0.04704 1 0.04704 1429 Kd 0.04704 This depict that the total cost of the company is 0.04704. The tax rate of the company is 30% according to the government regulations of Australia. In this calculation the cost of debt of the company has been analyzed through using 30% tax rate. Through the calculations, it has been found that the cost of equity of the company is 4.704% and the cost of debt of the company is 3.5% which depict that there is some difference between both the cost, the main reason behind these differences are the tax rate, debt interest rate, growth rate of the share price and the expected dividend by the company (Morning star, 2017). Current liabilities of a company must not be included in the cost of capital as the main reason behind calculating the cost of capital is to analyze the long term cost of the company. If the current liabilities would be included in that then there would be no meaning to calculate the cost of capital of the company. Further, it the current liabilities would be included in that then it would be easy for the company to calculate the entire cost of the company. According to the analysis, the major value of the WACC is cost of equity and this could be used in investment decision making by overlooking the capital structure of the company and enhance the part of debt more than the equity so that a proper equation could be set and cost of the company could also be reduced (Google finance, 2017). Currently, company has diversifies its market into various new countries as well as the new techniques have been adopted by the company to run the business smoothly. Through this, it has been found that the company has enhanced the equity to invest into these projects rather than the debt of the company. Due to which, the risk level of the company has been reduced but at the same time, the cost of the company has been enhanced. Lastly, the capital structure of the company has been analyzed in context with the industry capital structure and it has also been analyzed that what would be the optimal capital structure of the company. The capital structure of the company is as follows: Through this analysis, it has been found that both the company and the industry are enahncing the level of equity rather than the debt to enhance and manage the funds of the company. so, the capital strcuture of the company is quite consistent with the industry standards. The optimal capiatl structure of a company must be accoridng to the industry and economical sitaution of the company, at the same time, the turnover and the perfrmance of the company also matters. Accroidng to this case, the debt and equity ratio of the company must be 4:6 so that the risk level of the company could be in the favor and the cost of the company could also be reduced. According to the above evaluation, it has been found that the performance of the company is enhancing rapidly and there are more chances of the company to offer high return to its employees in near future. The news articles and various journals depict that the performance of this company would be enhanced in future and as this company is the largest company in the Australian market so it would lead the another companies in the market (Glajnaric, 2016). According to FT (2017), the performance of the company is becoming better and the company is required to manage the capitals structure in such a manner that the optimal capital structure level could be got by the company (Voelkl and Fritz, 2017). According to (Bui et al, 2016), this company is offering the great return to the investors even in the phase of financial crisis. AFR, (2017) depict that the performance of the company would be better in near future. Company is just required to manage over few financial products to enhance the worth of the company (Oliver and Schoff, 2017). Yahoo finance (2017) depict that share price of the company is overvalued. Conclusion: Thus through this analysis, it has been found that the performance of the company is better than other companies in the industry. Still the company is suggested to make some changes into its capital structure and strategies to manage the optimal capitals structure and the share price of the company could be according to the intrinsic value f the company. References: AFR. 2017. FLIGHT CENTRE TRAVEL GROUP LTD. Retrieved from https://www.afr.com/research-tools/FLT/company-profile/operational-history available on 3rd Oct 2017. AFR. 2017. FLIGHT CENTRE TRAVEL GROUP LTD. Retrieved from https://www.afr.com/markets/buy-hold-sell-flight-centre-harvey-norman-ccamatil-qantas-treasury-wine-20170903-gya397 available on 3rd Oct 2017. Bloomberg. 2017. FLIGHT CENTRE TRAVEL GROUP LTD. Retrieved from https://www.bloomberg.com/quote/FLT:AU available on 3rd Oct 2017. Bui, S.B.D., Petersen, T., Poulsen, J.N. and Gazerani, P., 2016. Headaches attributed to airplane travel: a Danish survey. The journal of headache and pain, 17(1), p.33. Glajnaric, M., 2016. The importance of dividend paying stocks. Equity, 30(2), p.6. Google finance. 2017. FLIGHT CENTRE TRAVEL GROUP LTD. Retrieved from https://finance.google.com/finance?q=ASX:FLT available on 3rd Oct 2017. Home. 2017. FLIGHT CENTRE TRAVEL GROUP LTD. Retrieved from https://www.fctgl.com/ available on 3rd Oct 2017. Morningstar. 2017. FLIGHT CENTRE TRAVEL GROUP LTD. Reterived from https://financials.morningstar.com/company-profile/c.action?t=FGETFregion=usaculture=en-US available on 3rd Oct 2017. Morningstar. 2017. Hello world travel limited. Retrieved from https://financials.morningstar.com/balance-sheet/bs.html?t=HLOregion=ausculture=en-US available on 3rd Oct 2017. Oliver, J. and Schoff, P., 2017. Agency and Competition Law in Australia Following ACCC v Flight Centre Travel Group. Journal of European Competition Law Practice, 8(5), pp.321-328. Voelkl, B. and Fritz, J., 2017. Relation between travel strategy and social organization of migrating birds with special consideration of formation flight in the northern bald ibis. Phil. Trans. R. Soc. B, 372(1727), p.20160235. Yahoo finance. 2017. FLIGHT CENTRE TRAVEL GROUP LTD. Retrieved from https://finance.yahoo.com/quote/flt.ax?ltr=1 available on 3rd Oct 2017.