Interaction Between Two Anionic Dyes and Cationic Surfactant

Interaction Between Two Anionic Dyes and Cationic Surfactant The interaction of the triphenylmethane dye xylenol orange with cationic surfactants cetyltrimethylammonium bromide and cetylpyridinium chloride was studied by absorption spectrophotometry and principal component analysis of infrared spectra by A. Gojmerac IvÃ… ¡ic ´ et al. [24]. Upon the addition of surfactants the yellow color of the acidic xylenol orange solution changed to purple-red. Changes in the absorption spectra of the mixture indicate strong interactions between dye and surfactants. These interactions were also monitored using ATR spectroscopy and the most prominent changes were observed in the vibration of the carbonyl group. The comparison of principal component loadings obtained from spectral data matrices for xylenol orange and mixtures of xylenol orange with surfactants revealed differences in infrared spectra caused only by the interaction of the surfactants with the dye. The interactions of Acid Green 25 (AG), an anionic dye, with two cationic surfactants tetradecyltrimethylammonium bromide (TTAB), and hexadecyltrimethyl ammonium bromide (CTAB) in aqueous solutions far below the CMC are studied at different temperatures using the conductometric method by H. Dezhampanah et. al. [25]. Various thermodynamic functions and equilibrium constants for the process of dye-surfactant ion pair formation were calculated using the conductometric data. There results indicate that the longer hydrophobic chain surfactants had a greater tendency and higher equilibrium constant as compared to shorter hydrophobic chain surfactants and short range, non-electrostatic interactions along with long range electrostatic forces have a significant influence on dye surfactant ion pair formation. Interaction of a food dye, tartrazine, with some cationic conventional and gemini surfactants, tetradecyltrimethylammonium bromide (TTAB), N,N-ditetradecyl-N,N,N,N-tetramethyl-N,N-butanediyl-diammonium dibromide (14,4,14), and N,N-didodecyl-N,N,N,N-tetramethyl-N,N- butanediyl-diammonium dibromide (12,4,12), were first investigated comprehensively by A. Asadzadeh Shahir et al. [26], employing conductometry, tensiometry, and UV-visible spectroscopy. Tartrazine was found to behave in the same manner as aromatic counterions. The formation of ion pairs reflected as a considerable increase of the surfactant efficiency in tensiometry plots and their stoichiometry were determined by Job’s method of continuous variations. For the tartrazine/TTAB system, nonionic DS3, ionic DS2-, and/or DS2 ion pairs, their small premicelles, and tartrazine rich micelles were constituted as well as dye-containing TTAB-rich micelles. Insoluble J-aggregates of DS ion pairs and cylindrical surfactant-rich micelles were also formed in tartrazine/gemini surfactant systems and recognized by transmission electron microscopy. The zeta potential and the size of the aggregates were determined using dynamic light scattering and confirmed the suggested models for the processes happening in each system. Cyclic voltammetry was applied successfully to track all of these species using tartrazine’s own reduction peak current for the first time. Reza Hosseinzadeh et al. [27] investigated the solubilization and interaction of azo-dye light yellow (X6G) at/with cationic surfactants cetyltrimethylammonium bromide (CTAB) and cetylpyridinium chloride (CPC) spectrophotometricaly. The effect of cationic micelles on solubilization of anionic azo dye in aqueous micellar solutions of cationic surfactants was studied at pH 7 and 25 à ¢- ¦C. The binding of dye to micelles implied a bathochromic shift in dye absorption spectra that indicates dye–surfactant interaction. The results showed that the solubility of dye increased with increasing surfactant concentration, as a consequence of the association between the dye and the micelles. The binding constants, Kb, were obtained from experimental absorption spectra. By using pseudo-phase model, the partition coefficients between the bulk water and surfactant micelles, Kx, were calculated. Gibbs energies of binding and distribution of dye between the bulk water and surfactant micelle s were estimated. The results show favorable solubilization of dye in CTAB micelles. The aggregation induced by Alizarin Yellow R (AYR) in the cationic surfactant, cetyltrimethylammonium bromide (CTAB), was investigated by measuring their UV–visible absorption spectra by M. F. Nazar et al. [28]. Conductance measurements as a function of surfactant concentration below and above the critical micelle concentration (CMC) were studied. CTAB aggregation takes place at the concentration far below its normal CMC in the presence of AYR. Both hydrophobic and electrostatic interactions affect the aggregation process in aqueous solution. Different parameters obtained from spectroscopic measurements and conductance data indicate an enhanced solubility of AYR dye in the micellar region. Intramolecular hydrogen bonding within the dye molecule effectively reduces intermolecular attraction, thereby increasing solubility in non-polar solvents (micelles). Medium effects on the position of the long wavelength absorption band of the azo dye characterize it as a pH chromic reporter molecule. A partitioning study of the solubilized system provides useful insight into the process of solubilization that is applicable to the general problem of membrane solubilization properties and in drug delivery to quantify the degree of drug-micelle interaction. The partition coefficient value obtained is important in micellar electro–kinetic capillary chromatography and high pressure liquid chromatography (HPLC) for drug quality control. Thus, interaction with micellar aggregates induces significant pKa shifts of Alizarin Yellow R that can be rationalized in terms of the partitioning of species and electrostatic contribution. Likewise, knowledge of the effects of organic additives on the CMC of surfactants is used both for theoretical and practical purposes because some additives are likely to be present as impurities or byproducts in the manufacturing of surfactants and their presence may cause significant differences in supposedly similar commercial surfactants. Eosin B and eosin Y have been used to estimate micro- and submicrogram quantities of proteins respectively. A.A. Waheed et al. [29] describe the mechanism of eosin binding to proteins. At pH lower than 3.0 the absorbance of unbound dye is greatly reduced. After the dye binds to protein, the absorption maximum of the dye changes from 514 to 530  ± 5 nm. The absorbance and bathochromatic shift in absorption maximum of the protein–dye complex are proportional to the concentration of protein. The pH of the assay solution does not change due to protein. Arginine, histidine, and lysine (at both acidic and neutral pH) and tryptophan (at acidic pH) residues of a protein bind electrostatically to carboxylic and phenolic groups of the dye to produce a stable water-soluble protein–dye complex. The binding constants of eosin B with poly-L-arginine, poly-L-histidine, poly-L-lysine, and poly-L-tryptophan at pH 1.96 are 0.37, 0.32, 0.33 and 0.33 nmol/nmol of amino acid, respectively . The binding constants of eosin B and eosin Y with bovine serum albumin (BSA) at pH 1.96 are essentially the same, i.e., 0.82 nmol/nmol of reactive amino acid of BSA. The binding constant varies with solution pH so that a wide range of protein concentrations can be estimated. The reason for the higher absorbance of protein–eosin Y complex compared to that of protein–eosin B complex is discussed. Javadian et al. [30] studied the interaction of Congo Red (CR) with a series of Alkyl trimethyl ammonium bromide (CnTAB), N-hexadecyl pyridinium bromide (CPB) and N-hexadecyl pyridinium chloride (CPC) using conductometry and UV-Vis spectroscopy technique. Job’s method of continuous variations demonstrated that only DS complexes are formed in the studied mixtures. The formation of DS complexes results in decreasing the systems order so it is an enthalpy-driven reaction under the influence of both hydrophobic and electrostatic interactions. The amount of ion pairs forming through the process is highly dependent on physical conditions and structure of the dyes and the surfactants such as chain length, head group and counterion. By increasing the length in the hydrophobic chain or the charge density of headgroup of a cationic surfactant, the strength of dye-surfactant interaction increases. In addition, the results show that the counter ion has no significant effect on dye-surfact ant interaction. The tendency to form DS complexes decreases with increasing temperature in the range of 298.0-313.0 K. K. Fujio et al. [31] examined the effect of added salt on micelle size, shape, and structure the solubilization of Orange OT in aqueous NaBr solutions of decylpyridinium bromide (DePB), dodecylpyridinium bromide (DPB), tetradecylpyridinium bromide (TPB), and hexadecylpyridinium bromide (CPB). The solubilization powers of DePB and DPB micelles increase with increasing NaBr concentration up to 2.86 and 3.07 mol dm–3, respectively, but above these concentrations remain unaltered. This suggests that spherical micelles of DePB and DPB can have a maximum and constant size at NaBr concentrations higher than these threshold concentrations. On the other hand, the solubilization powers of TPB and CPB micelles increase in the whole range of NaBr concentration studied. The dependencies of the solubilization powers of their micelles on the counterion concentration change at 0.10 and 0.03 mol dm–3 NaBr, respectively, as suggests that TPB and CPB micelles undergo the sphere–rod transition at those concentrations. Orange OT is a more suitable probe for detecting the presence of the maximum- and constant-size spherical micelle than Sudan Red B. The interactions between two anionic dyes and a cationic surfactant were studied by conductometric technique by A. A. Rafati et al. [32]. The conductance of aqueous solutions of methyl orange (MO) and methyl red (MR) was measured in the presence of a cationic surfactant, cetylpyridinium bromide (CPB) at different temperatures in water–ethanol, water–propanol and water–butanol mixed solvents, containing different concentration of alcohols. The equilibrium constants and other thermodynamic functions for the process of dye–surfactant ion pair formation were calculated on the basis of a theoretical model. The results showed that the presence of alcohol, as well as increasing the length of the alcohol chain, decreases the tendency for ion pair formation. The results have shown that an increase in temperature lowers the tendency for ion pair formation as the equilibrium constants decrease with increasing temperature. According to the results, long range as well as short range interactions are responsible for the formation of the ion pair. The importance of long range electrical forces is basically to bring the dye anion and the surfactant cation close enough to enable the action of short range interactions whose contribution represents the major part of the standard free energy change for the formation of the anionic dye–cationic surfactant ion pair. By using the association constant (K1) for the first step of the association [D+S+→(DS) °], the standard free energy change, standard enthalpy change, and standard entropy change of the association were calculated at low surfactant concentrations.

Management Influences on Turnover Intention of Software Developers Essay

Introduction The Information Technology (IT) Age has created many opportunities for employment in the IT and IT services industry.   IT professionals are in demand all over the world.   Organizations worldwide invest money that go not only into salaries but for further training of IT professionals they hire. However, around the world, the demand, supply, selection, recruitment and particularly retention of IT professionals has threatened organizations that use, manage or deal in IT or IT services for the past few years (Parà © and Tremblay 2000; Ermel and Bohl 1997; Morello 1998; Guptill et al. 1999). This is why the departure of an IT professional from a company usually comes with disastrous effects to the organization.   When an IT professional resigns, the organization suffers loss of business process knowledge and acquired technical skills (Dorà © 2004). Since late 1996, the turnover for IT professionals has jumped from 15% to 20% annually, with only 8 of 10 IT positions being filled with qualified candidates (McNee et al. 1998).   With the annual turnover rate estimated at 20% or more (Alexander 1999; Kosseff 1999), job-hopping of IT professionals has been one of the biggest problems among managers and human resources (HR) experts (Parà © and Tremblay 2000). IT professionals seem to have a tendency to change their jobs faster than other employees when they feel dissatisfied with their current employer (Hacker 2003).   The estimated cost of replacing IT professionals range from 1.5 to 2.5 times of their annual salaries for the companies they resigned from (Kosseff 1999).   On the other hand, the cost of losing a qualified IT professional is actually 3 to 6 times more expensive than the cost of losing a manager (Kochanski and Ledford 2001). IT professionals, as also mentioned previously in this study, also tend to change jobs more quickly than other employees when they feel dissatisfied with in their current employment (Hacker 2003).   However, rational models of voluntary turnover cannot be used to explain the high turnover rates for IT professionals (Rouse 2001) since many IT professionals remain dissatisfied with their jobs even though they enjoy high financial rewards yet their creativity and expertise do not receive high respect from their peers, supervisors and companies as a whole (Fisher 2000). Furthermore, another explanation why IT professionals may resign more quickly when dissatisfied with their current employment is that â€Å"much of IT work is project oriented, the technical employee’s loyalty may be more to the project, and not necessarily to the employer† (Hacker, 2003, p. 15). These trends place intense pressure on both IT executives and HR managers.   High IT professional turnover translates to a threat not only to an organization’s IT department but to the business as a whole. Most importantly, high IT turnover poses a threat to the growth, competitive positioning and strength of the global economy (Parà © and Tremblay 2000). A dissertation by Dr. Timothy Lee Dorà © (2004) studied the relationships between job characteristics, job satisfaction and turnover intention among software developers.   These two factors – job characteristics and job satisfaction – are deemed to play crucial roles in understanding turnover intention not only among software developers but IT professionals as a whole. The current study aims to investigate the management influences on employee retention of IT professionals, focusing on job characteristics and job satisfaction, and their impact on turnover and retention.   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   1.1.1  Ã‚  Ã‚   Scope and Limitations of the Study This research will study the impact of job characteristics and job satisfaction on the turnover intention of IT professionals.   Although this paper intends to replicate some of Dorà ©Ã¢â‚¬â„¢s findings, the study will not be limited to software developers only as this sector only constitutes a small sample of IT professionals as a whole. Specifically, the research study will focus on the turnover intention of IT professionals in___________. In studying the relationships between job characteristics, job satisfaction and turnover intention, this study is limited to the use of the following theoretical models and theories to support its conclusions: For the discussion on job characteristics, the research study will make use of the Job Characteristics Model developed by JR Hackman and GR Oldham (1975/1980) and the analysis on Model Employers by Minda Zetlin (2001). For the discussion on job satisfaction, as well as motivation, the paper will use the Motivator-Hygiene Theory by F. Herzberg (1968/2003) and the Synergistic Model by T.M. Amabile (1997). For the discussion on turnover, the study will use the Voluntary Turnover Model by R.M. Steers and R.T. Mowday (1987); the Rational Turnover Model by P.D. Rouse (2001); the Instinctual or â€Å"Unfolding† Model of Turnover by T.W. Lee, T.R. Mitchell, L. Wise and S. Fireman (1996); and the Conceptual Model for Investigating Turnover in IT, developed by J.B. Thatcher, L.P. Stepna and R.J. Boyle (2002-03) These models will be discussed in detail later in this chapter, as well as in Chapter 2 on Review of Related Literature. Chapter 2 Review of Related Literature This chapter will analyze the various literature which are related to this research paper. It will discuss the works of other analysts and researchers on theories/models that will be used to support this study, as well as pertinent literature on IT professionals’ turnover intentions. The chapter begins with a general discussion on motivational theories, cutlure, and leadership which are all critical factors that affect an employee’s intent to leave. The discussion them dovetails into a more specific presentation of the framework used in the current study. This chapter will also include a definition of terms incorporated into the discussion of related literature. 2.1  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Relationships between Job Characteristics, Job Satisfaction, and Turnover Intention In 2004, Timothy Lee Dorà © submitted a dissertation titled â€Å"The Relationships Between Job Characteristics, Job Satisfaction, and Turnover Intention Among Software Developers†.  Ã‚   According to Dorà ©, the factors leading to the turnover intention of software developers have been poorly understood.   His study was designed to further understand the relationships between job characteristics, job satisfaction, and turnover intention among software developers.   His study involved the use of 326 web surveys that contained questions relating to job characteristics, job satisfaction, turnover intention and demographic information. The results of Dorà ©Ã¢â‚¬â„¢s study showed that several factors can influence turnover intention, most significantly, job characteristics that may be influenced by management, such as training, autonomy, feedback, number of developers, task significance, and skill variety (Dorà © 2004).   In his study, Dorà © made use of two research questions and sixteen hypotheses to understand the job characteristics variables which contribute to the various dimensions of job satisfaction, and which of these job satisfaction dimensions, in turn, contribute to turnover intention. Dorà © made use of indirect effect tests, to determine if certain job characteristics could be linked to turnover intention through the job satisfaction scales he provided.   The results of his study indicated that ten of the indirect effects were statistically significant.   All ten of the statistically significant indirect effects were associated with only three of the seven job satisfaction scales: internal work motivation, general job satisfaction, and satisfaction with pay. The largest indirect effect, according to Dorà ©, was the effect of autonomy on turnover intention through general job satisfaction: higher levels of autonomy lead to lower levels of turnover intention by increasing general job satisfaction.  Ã‚   The next largest indirect effect was the effect of organizational training on turnover intention through general job satisfaction: organizational training decreased turnover intention through an increase in general job satisfaction.   The next three highest indirect effects in Dorà ©Ã¢â‚¬â„¢s findings were also between a job characteristic (feedback, skill, variety, and number of developers) and turnover intention through general job satisfaction (Dorà ©, 2004, p. 130). 2.2  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Measuring Turnover Intentions Among IT Professionals Guy Parà © and Michel Tremblay, in contrast to Dorà ©Ã¢â‚¬â„¢s study, completed a research covering the turnover intention of not just software developers but IT professionals as a whole.   Their study, â€Å"The Measurement and Antecedents of Turnover Intentions among IT Professionals† (2000), submitted to Cirano research center, aimed to present and test an integrated model of turnover intentions that address the unique nature of the IT profession (Parà © and Tremblay, 2000, p. 3).   The authors identified a multidimensional set of HR practices that will most likely increase retention among IT employees.  Ã‚   They emphasized citizenship behaviors as well as two distinct types of organizational commitment as key antecedents of turnover intentions. The study involved the sending of questionnaires to 394 Quebec members of the Canadian Information Processing Society.  Ã‚   The study addressed four research questions: 1) What are the essential HR practices necessary to create an effective plan for retaining IT professionals? 2) What is the impact of compensation and negotiation conditions on the turnover intentions of IT personnel? 3) What is the effect of employee demographic characteristics on the turnover intentions of IT personnel? 4) Do organizational commitment and citizenship behaviors mediate the effects of HR practices, compensation and negotiation conditions as well as demographic characteristics on the turnover intentions of IT personnel? (Parà © and Tremblay, 2000, p. 4) Parà © and Tremblay provide that IT employees who are highly committed to their organization are less likely to leave than those who are relatively uncommitted.   They attach three distinct dimensions to organizational commitment: affective, continuance and normative commitment (Meyer and Allen 1997). 1)  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Affective commitment – means an employee’s personal attachment and identification to the organization.   This results in a strong belief in an acceptance of the organization’s goals and values.   â€Å"Employees with a strong affective commitment continue employment with the organization because they want to do so† (Parà © and Tremblay, 2000, p. 5) 2)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Continuance commitment – is a tendency to engage in consistent lines of activity based on the individual’s recognition of the â€Å"costs† associated with discontinuing the activity.  Ã‚   â€Å"Employees whose primary link to the organization is based on continuance commitment remain because they need to do so.† (Parà © and Tremblay, 2000, p. 5) 3)  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Normative commitment – provides that employees exhibit behaviors solely because they believe it is the right and moral thing to do. â€Å"Employees with a high level of normative commitment feel that they ought to remain with the organization.† (Parà © and Tremblay, 2000, p. 5) In their findings, Parà © and Tremblay provide that affective commitment and continuance commitment are negatively related to turnover intentions (Parà © and Tremblay, 2000, p. 6).   In addition to these two distinct types of commitment affecting turnover intention, their studies also points to the factor they call Organizational Citizenship Behavior or OCB. OCB is considered as a key element in organizational effectiveness.   OCB is defined as â€Å"an employee’s willingness to go above and beyond the prescribed roles which they have been assigned† (Parà © and Tremblay, 2000, p. 6, quoting from Organ 1990). Based on Parà © and Tremblay’s findings, the stronger the citizenship behavior of an IT employee, the more likely they are to stay in their company.   The IT professional’s affective commitment, or attachment to his or her organization, also decreases turnover intention. 2.3  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Job Characteristics Model Hackman and Oldham’s Job Characteristics Model, as earlier introduced in Chapter 1 of this research study, predicts what aspects of jobs reflect the level of job enrichment for employees, and how these relate to employees’ individual differences and to the work outcomes required. The model includes five core job characteristics that can be applied to any job: skill variety, task identity, task significance, task autonomy and feedback. Skill variety is defined as â€Å"the number of different skills required in the job† (Hackman and Oldham 1980; Pilon 1998). Task identity means â€Å"the completeness of the tasks done in the job† (Hackman and Oldham 1980; Pilon 1998). Task significance on the other hand is defined as â€Å"the importance of the job to the served population.† (Mohamed 2004). Autonomy means â€Å"the vertical expansion of responsibility, the amount of decision-making and independence allowed for employees.† (Mohamed 2004). And lastly, feedback means â€Å"the extent that the job itself provides information about employees’ performance† (Huber 2000). These characteristics – skill variety, task identity, task significance, autonomy, and feedback – are combined into a single predictive index which is called the Motivating Potential Score (Hackman and Oldham 1980). Figure 1. Job Characteristics Model Source: A.H. Mohamed (2004)   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The five core job characteristics enumerated in the previous paragraph are in continuous interaction with individual differences that evoke three critical psychological states in an employee.   These three states are: 1) when the job is structured by skill variety, task identity and task significance this could lead employees to experience meaningfulness in their work. 2) The second state, task autonomy, which leads to feelings of responsibility for the outcomes of work. 3) The third and last state is feedback, which leads employees towards knowledge of the results of their work (Douthit 2000; Huber 2000).   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   These three critical psychological states lead to a set of affective and personal outcomes:   high internal work motivation, high growth satisfaction, high general satisfaction, high work effectiveness, and low rate of absenteeism (Mohamed 2004; Donovan and Radosevich 1998).   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   These affective and personal outcomes are the results of en employee’s job characteristics.   They are defined as follows: High internal work motivation – this is the degree to which an employee is willing to work and to consider the organizational objectives as part of his or her own goals (Mohamed 2004). High growth satisfaction – this is the achievement of the employee in overcoming challenges, succeeding and growing (Steers and Black 1994) High general satisfaction – this the feeling derived from the overall satisfaction with the work itself. â€Å"This type of satisfaction is reflected mainly in decreased rates of absenteeism among employees† (Steers and Black 1994; Omachonu et al 1999). High work effectiveness – this refers to both the quality and quantity aspects of work performance (Hackman and Oldham 1980). Low rate of absenteeism. The Job Characteristics Model, also includes three attributes that are identified as Moderators: knowledge and skills, context job satisfaction, and employee growth-need strength.   These attributes indicate which employee will respond positively to the Motivating Potential Score of their job and its outcomes (Hackman and Oldham 1980). An employee’s knowledge and skills are dependent on their educational qualifications which in turn will reflect their perceptions toward their work outcomes (Sabiston and Laschinger 1995).   On the other hand, an employee’s perception of his or her context job satisfaction involves factors like pay, supervision, colleagues, and job security.   All these affect the employee’s outcomes as well (Mohamed 2004).  Ã‚   Lastly, growth-need strength is the degree in which an employee seeks opportunities in his or her job for self-direction, learning and personal accomplishment.   These elements in turn affect the employee’s level of work internal motivation (Mohamed 2004). An example of a study which made effective use of Hackman and Oldham’s Job Characteristics Model is the one conducted by A.H. Mohamed (2004) called â€Å"Using the job characteristics model to compare patient care assignment methods of nurses† for the Faculty of Nursing, University of Alexandria in Egypt.  Ã‚   The population used were the nurses in the Alexandria Main University Hospital.   Mohamed made use of a Job Diagnostic Survey (also developed by Hackman and Oldham) to determine nurses’ perceptions towards the components of the Job Characteristics Model in relation to their performance in utilizing the case and functional methods of patient care assignment (Mohamed 2004). In his study, Mohamed concludes that the jobs of intensive care unit nurses result in different expectations based also on the different categories of nurses, based on skills and challenges inherent in the work they perform (Mohamed 2004). Generally speaking thus, studies like Mohamed shows that an employee’s personal and affective outcomes are a result of the employee’s job characteristics. 2.4  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Model Employers However, management also plays a crucial role in the retention and conversely turnover of IT professionals.  Ã‚   Since IT professionals still enjoy a wide selection of employers to choose from, employers constantly compete to attract the best IT professionals by becoming â€Å"model employers†.   In her 2001 article for Computer World, called â€Å"Model employers†, Minda Zetlin outlines the strategies that make certain companies â€Å"model employers†. By compiling its eight annual list of 100 Best Places to Work in IT, Computer World roughly sums up the model employers as offering not just top compensation, but also â€Å"opportunities for career growth, investment in training, diversity in the work place, work flexibility, and, ideally, a comfortable and fun place to spend their daytime hours† (Zetlin 2001).   Zetlin in her article outlines three common themes behind the success of these model IT employers: IT is central to the best employers’ success According to Zetlin, excellence in IT is a top corporate strategy.  Ã‚   Prioritizing IT should not be limited to companies that strictly provide IT or IT services.   Companies such as Avon, for instance, which ranks 4th in Computer World’s list of 100 best employers, may be perceived to operate on a relationship-based environment.  Ã‚   Yet to process its more than 60 million custom orders every year, the company relies heavily on IT to process its complex supply chain.   The fact that is it is actually a very transactional business, dependent on technology, makes IT one of its priorities (Zetlin 2001). Management takes an active interest in employers’ careers from the day they arrive This includes having development plan for employees as soon as they join the organization.   Employees meet with their managers on a periodic basis for a formal review to assess their development plan and to evaluate its progress.  Ã‚   Orientation programs at the start of the employment are also part of this strategy.   Apart from orientation, Harley-Davidson, Inc. (ranked as No. 11) also provides for a yearly self-assessment for its employees against the established competencies for their jobs, with their supervisors doing the same (Zetlin 2001).   Such focus on career development per employee makes the employee feel that management takes an active interest in aligning its objectives with the employee’s personal goals. Model employers also provide for continuous interest on their employees’ careers throughout their employment with the company.   Knowledge mentoring programs and career mentoring programs, used by the State Farm Mutual Automobile Insurance Co. (ranked No. 13), for instance, allow employees to learn more skills and career guidance from their more experience colleagues, and help management to identify employees to fill leaderships positions in the short and long term (Zetlin 2001).   State Farm’s mentoring program is in fact so successful that it has extended the program to employees who haven’t even arrived yet – such as assigning mentors to college students who plan to join State Farm after they graduate. There are no walls between business and IT Unlike other organizations, model employers ensure that IT people and business people work side by side.   There is no division or competition.  Ã‚   IT professionals are given a better understanding that what they do helps the business succeed.   This understanding leads to career satisfaction for IT professionals.   Technology people know exactly how they contribute to the revenues of their business and how important they are in the business plan.   One advantage here is that a close relationship between IT and business allows people to switch between the two fields (Zetlin 2001).   Another strategies such as cross-functional work teams gives career development not just to IT professionals but to business people in the organization as well.  Ã‚   There are continuously different career tracks available.   An IT professional may opt to advance by taking on management roles within technology, or they may shift to business management positions (Zetlin 2001). 2.5  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Voluntary Intention Model   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   R.M. Steers and R.T. Mowday, in their study â€Å"Employee turnover and post-decision accommodation processes† (1981) analyzed turnover as rooted in voluntary intention.   Steers and Mowday viewed the interaction of intention to leave, and alternative job opportunities (ease of movement) as direct antecedents to turnover (Steers and Mowday 1981; Rouse 2001).   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   As earlier discussed in Chapter 1 of this study, the direction of the process in Steers’ and Mowday’s Voluntary Intention Model starts with Job Expectations, then Affective Responses, then Turnover Intention, then finally,   Actual Turnover (see Section 1.2.1.1 of this paper).   However, these four elements were actually grouped together by Steers and Mowday under three steps. As can be seen in the Figure 3: Each step in Figure 3 contains two constructs.   The second construct (Job Attitudes) of Step 1 becomes the first construct of Step 2.   The second construct (Intent To Leave) of Step 2 becomes the first construct of Step 3. Step 1 of the Voluntary Intention Model involves the manner in which job expectations influence an employee’s attitudes regarding his or her job.   Attitudes are composed of job satisfaction, organizational commitment, and job involvement.   Job expectations in turn are influenced by three stimuli. The first stimuli focuses on individual characteristics such as occupation, age, tenure, family concerns, and personality form (Steers and Mowday 1981; Rouse 2001). The second stimuli involves information obtained during the recruitment process and at various assessments points throughout the employee’s career (Steers and Mowday 1981; Rouse 2001). For instance, studies have shown that job expectation levels are often high when the employee first accepts a new job (Porter and Steers 1973). At these particular periods, expectations are developed from both the employee and employer’s ends. In other words, a sort of unwritten social contract is deemed to be adopted by the two parties (Prouse 2001). Lastly, the third stimuli affecting job expectations are alternative job opportunities.   Studies have shown that the more alternatives there are confronting an employee, then the more negative the employee’s attitudes becomes concerning his or her current job (Pfeffer and Lawler 1979). Step 2 in the Voluntary Intention Model involves the Affective Responses that are elicited from Step 1.   These responses include the construct of job satisfaction, and how those responses influence the employee’s desire to leave the organization.   Factors that affect the employee’s decision to leave include non-work factors such as family, hobbies, religion and political influences (Cohen 1995). Steers and Mowday also identified the potential of employees to alter their actual job, in terms of pay, working hours, environment, etc., and thus change their attitudes regarding their jobs (Prouse 2001). Chapter 3 Methodology The aim of the research is to examine the relationships between job characteristic, job satisfaction and turnover intention among IT professionals in ______________.   The proposition is that job satisfaction and job characteristics as management influences have indirect impact to the levels of turnover intention among IT professionals.   The literature review indicates that there are different factors affecting IT professionals’ turnover intention.   This research is going to study the turnover intention of IT professionals in _____________. 3.1  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Research Questions The study will answer the following two research questions: Which job characteristic variable(s) causes the job satisfaction among IT professionals in ____________? What job satisfaction variable(s) cause the turnover intention among IT professionals in ____________?   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   In answering these two primary questions, the thesis will make use of the following framework:    Hypotheses Research Question 1   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   â€Å"Which job characteristic variable(s) causes the job satisfaction among IT professionals in _______________?†   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The first research question will analyze the standardized effect of job characteristics to job satisfiers.  Ã‚   The null hypotheses tested were: Job Characteristics à   Job Satisfactions H1: The level of IT training does not affect the various measures of job satisfaction. H2: The level of user contact does not affect the various measures of job satisfaction.   Ã‚  Ã‚  Ã‚  Ã‚   H3: The job-required skills do not affect the various measures of job satisfaction.   Ã‚  Ã‚  Ã‚  Ã‚   H4: The level of task significance does not affect job satisfaction.   Ã‚  Ã‚  Ã‚  Ã‚   H5: The amount of workload does not affect job satisfaction.   Ã‚  Ã‚  Ã‚  Ã‚   H6: The amount of feedback does not affect job satisfaction. Research Question 2   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   â€Å"What job satisfaction variable(s) cause the turnover intention among IT professionals in ________________?†   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The first research question will analyze the standardized effect of the job satisfaction scales to turnover intention.  Ã‚   The null hypotheses tested were:   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Job Satisfactions à   Turnover Intention   Ã‚  Ã‚  Ã‚  Ã‚   H7: The level of internal work motivation does not affect turnover intention.   Ã‚  Ã‚  Ã‚  Ã‚   H8: The level of job security satisfaction does not affect turnover intention.   Ã‚  Ã‚  Ã‚  Ã‚   H9: The level of social job satisfaction does not affect turnover intention.   Ã‚  Ã‚  Ã‚  Ã‚   H10: The level of job growth satisfaction does not affect turnover intention.   Ã‚  Ã‚  Ã‚  Ã‚   H11: The level of satisfaction with pay does not affect turnover intention.   Ã‚  Ã‚  Ã‚  Ã‚   H12: The level of satisfaction with supervision does not affect turnover intention. Research Procedures   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   3.3.1  Ã‚  Ã‚   Data Collection   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Research is a process of studying and analyzing situational factors of a specific problem or issue in order to determine solutions of it (Cavana, Delahaye and Sekaran 2001). According to Cavana, Delahaye and Sekaran (2001), there are three research paradigms: positivist, interpretivist and critical research.   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   As the research hypotheses of this study try to explore the relationships between job characteristic, job satisfaction and turnover intention among the IT professionals in __________________, the positivist approach will be adopted and it will provide the framework upon which the methodology of this study can be used.   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   In this study, the research problem requires primary data to specifically address the twelve hypotheses. An Internet questionnaire will be used as it is the most effective and appropriate data collection method. â€Å"Questionnaire† is defined as a â€Å"pre-formulated written set of questions to which respondents recorded their answers within closely defined alternatives† (Cavana, Delahaye and Sekaran, 2001). A well-designed questionnaire provides accurate and useable data for analysis in order to make a conclusion of accepting / rejecting a research hypothesis.  Ã‚   A copy of the questionnaire to be used is attached as Appendix A of this study.   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   After gathering the data from questionnaires, the analysis of the data (including frequency distribution, correlation analysis and regression analysis) will be performed by a quantitative data analysis tool called SPSS (Statistical Package for the Social Sciences). SPSS predictive analytics advances in usability and data access, drawing reliable conclusions from the collected quantitative data (SPSS, Inc. 2002). In depth quantitative analysis of the data will be undertaken. Frequency Distribution, Correlation Analysis, and Regression Analysis will be used to analyze the collected data.   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The population of this research is the _________ professionals in the country. The research is expected to have a 10% response rate (i.e. ____ questionnaires).   A reminder email will be sent to the students to ensure reaching the planned response rate. Participants are not inconvenienced or exposed unnecessarily to potential harm by recruiting more than is required. The research conducted by Dorà © in 2004 (which this paper intends to compare itself to) only received 326 responses which is less than 0.1% of the population.   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   An invitation email   will be sent to the administration managers of the participating institutions. Then the manager will forward the invitation email to all qualified IT professionals and invite them to fill in the Internet anonymous questionnaire within 10 business days. A reminder email will be sent by the manager on the 6th business day. The invitation email only contains a consent form   and a URL to the Internet anonymous questionnaire. Participation is entirely voluntary. The participant can withdraw at any time and there will be no disadvantage if the participant decides not to complete the survey.   At no time will any individual be identified in any reports resulting from this study. A copy of the consent form is attached with this application. Variables   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The variables which will used in this study can be categorized into two categories: job characteristics and job satisfaction.   The factors within each category are discussed as follows:   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The following job characteristics for IT professionals were selected for this study, based also on previous usage in similar studies as indicated in the literature review: IT Training User Contact Job-required Skills Task Significance Workload Feedback   Ã‚  Ã‚  Ã‚  Ã‚   On the other hand, the job satisfaction scales include the following: Internal Work Motivation Job Security Satisfaction Social Job Satisfaction Job Growth Satisfaction Satisfaction with Pay Satisfaction with Supervision Data Analysis   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The study will make use of descriptive and inferential analysis were used throughout the study.  Ã‚   Descriptive statistics (percentages, means, standard deviations, frequencies, and item means) were computed using the SPSS (SPSS, Inc., 2002).   This general-purpose analysis program will be used to characterize the sample in terms of demographic characteristics pertaining to gender, income, education, age, years as an IT professional, years in the current organization, and years in the current position.   SPSS will likewise used to analyze the correlation among job characteristics, the correlation between job satisfaction scales, the correlation between job satisfaction and job characteristics, and the correlation between job characteristics, job satisfaction, and turnover intention.   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   The primary inferential technique that will be used is bivariate correlation.   SPSS will   also be used to analyze the regression analysis for the data.   A 0.01 level of significance was adopted for testing significance.   The standardized effects of all the job characteristics for each job satisfier will also be computed.   The same method will be used to analyze the standardized effect of all the job satisfaction scales to turnover intention.   From these standardized effect analyses, the prediction of turnover intention by job satisfaction scales will be computed.    The job satisfaction scales which had a 0.60 level were considered significant to turnover intention.   The reliability coefficients ranging between 0.60 and 0.70 are deemed adequate for research purposes (Aiken, 2000, p.88).   For purposes of this study, the job satisfiers and job characteristics which have indirect effects of 0.60 above significance to turnover intention will be used.   The standardized effect of the significant job characteristic will be multiplied to the standardized effect of the particular job satisfier.   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Each of the twelve hypotheses of this study will be tested in essentially six multiple regression analyses – one for each job satisfier as the constant, independent variable and its relation to each dependent variable represented by the job characteristics.   Otherwise stated, each job satisfier will represent a criterion variable and the six job characteristics will be considered predictors in each of the six regression analyses. References Alexander, S. M. (1999, November 1). The tricks for retaining talent. Crain’s Cleveland Business, T2-T3. Amabile, T. M. (1997). Motivating creativity in organizations: On doing what you love and loving what you do. California Management Review, 40, 39-58. Cavana, R. Y., Delahaye, B. L., and U. Sekaran. (2001). Applied Business Research: Qualitative and Quantitative Methods, John Wiley & Sons Australia, Ltd., Milton. Cohen, A. (1995). An examination of the relationships between work commitment and nonwork domains. Human Relations, 48, 239-263. Donovan, J.J., and D.J. Radosevich, (1998). The moderating role of goal commitment on the goal difficulty–performance relationship: a meta-analytic review and critical re-analysis. Journal of applied psychology, 1998, 83:308–15. Dorà ©, Timothy Lee. (2004).   The Relationships Between Job Characteristics, Job Satisfaction, and Turnover Intention Among Software Developers. Graduate School of Argosy University-Orange County. Douthit, M.W., (2000). Job satisfaction returns to human and social capital. Journal of behavioral and applied management, 2000, 1(1):67. Fisher, A. (2000, December 18). Inspiring the burned-out computer programmer. Fortune, 334. Guptill, B. Stewart, B. Maroccio, L., Potter, K. and C. Claps (1999). 1998 IT Spending and Staffing Survey Results. Gartner Group Strategic Analysis Report, April 1st. Hacker, C.A. (2003, Spring). Turnover: A silent profit killer. Information Systems Management, 14-18. Hackman JR, and GR Oldham, (1980). Work design. Reading, Massachusetts, Addison–Wesley, 1980:114–21. Herzberg, F. (1968). Work and the nature of man. Cleveland: World. Herzberg, F. (2003, January). One more time: How do you motivate employees? Harvard Business Review, 87-96. Kochanski, J., and G. Ledford. (2001). ‘How to keep me’—Retaining technical professionals. Research Technology Management, 44(3), 31-38. Kosseff, J. (1999, September 6). Info-tech firms increase efforts to keep workers. Crain’s Detroit Business, p. 21. McNee, B. Morello T. Zidar, E. and C. Smith (1998). IT Staff Retention and Recruitment: Addressing a Critical Problem for the IS Organization. Gartner Group Strategic Analysis Report, September 28. Meyer, J.P., and N.J. Allen, (1997). Commitment in the Workplace, Sage Publications. Mohamed, A.H. (2004, May) Using the job characteristics model to compare patient care assignment methods of nurses, Vol. 10, No. 3, May 2004, p. 389-405.   Department of Nursing Administration, Faculty of Nursing, University of Alexandria, Alexandria, Egypt. Morello, D.T. (1998). IT retention and recruitment: What has a year brought? Gartner Group Monthly Research Review, September 01. Omachonu V.K., Swift, J.A., Ross, J.E., eds. Principles of total quality, 3rd ed. Florida, St. Lucie Press, 1999:335. Organ D.W., (1990). The motivational Basis of Organizational Citizenship Behavior  » In B.M. Parà ©, G. and Tremblay, M. (2000).   The Measurement and Antecedents of Turnover Intentions among IT Professionals.   Scientific Series.   Ciprano.   September 2000. Pfeffer, J., & Lawler, J. (1979). The effects of jb alternatives, extrinsic rewards, and commitment on satisfaction with the organization: A field example of the insufficient justification paradigm . Berkeley: University of California. Pilon BA. Outcomes and surprises of work redesigning: how other leaders are coping with changes. Nursing Management, 1998, 29(8):44–5. Porter, L. W., and R.M. Steers. (1973). Organizational, work and personal factors in employee turnover and absenteeism. Psychological Bulletin, 80, 151-176. Rouse, P. D. (2001). Voluntary turnover related to information technology professionals: A review of rational and instinctual models. International Journal of Organizational Analysis, 9, 281-290. Sabiston J.A., and H.K. Laschinger (1995). Staff nurse work empowerment and perceived autonomy. Journal of nursing administration, 1995, 25(9):42–50. SPSS, Inc. (2002).   Statistical Package for the Social Sciences (Version 11.5) [Computer software].   Chicago, IL: SPSS, Inc. Steers, R. M., and R.T. Mowday. (1981). Employee turnover and post-decision accommodation processes. In L. Cummings & B. Staw (Eds.), Research in Organizational Behavior (Vol. 3, pp. 235-281). Steers, R.M., and J.C. Black. Organizational behavior, 5th ed. New York, Harper Collins, 1994:95–101. Thatcher, J.B., Stepna, L.P. and R.J. Boyle (2002-03).   Turnover of information technology workers: Examining empirically the influence of attitudes, job characteristics, and external markets.   Journal of Management Information Systems, 19, 231-261. Zetlin, M. (2001, June 4). Model employers. Computerworld, 40-45.

Project-Proposal-Template

Project Proposal Template Project Proposal Template Company Name Company Name Write Company Address Here City, State, Zip Code Phone, Fax Email: www. proposaltemplatestips. com Website: www. abc. com Write Company Address Here City, State, Zip Code Phone, Fax Email: www. proposaltemplatestips. com Website: www. abc. com Project Proposal Project Proposal 2011/12 2011/12 Project Name Project Name Business Area Program Name Business Area Program Name Project Sponsor:| | Author (Business):| | Author (MIS):| | Project Sponsor:| | Author (Business):| |Author (MIS):| | DD/MM/YYYY DD/MM/YYYY a). Sample text —————————————————————————————————————- 1 b). Sample text —————————â⠂¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€- 4 c). Sample text —————————————————————————————————————- 6 b). Sample text —————————————————————————————————————- 8 d).Sample text ———————————————†”—————————————————————- 10 e). Sample text —————————————————————————————————————- 14 f). Sample text —————————————————————————————————————- 15 g). Sample text —————————————————————â⠂¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€- 19 h).Sample text —————————————————————————————————————- 20 i). Sample text —————————————————————————————————————- 24 j). Sample text ———————————————————————————â €”—————————- 27 k). Sample text —————————————————————————————————————- 28 l).Sample text —————————————————————————————————————- 29 m). Sample text —————————————————————————– ——————— ————- 31 n). Sample text —————————————————————————————————————- 32 o). Sample text —————————————————————————————————————- 33 p).Sample text —————————————————————————————————————- 35 q). Sample text —————————————————————————————————————- 39 r). Sample text —————————————————————————————————————- 41 s). Sample text —————————————————————————————————————- 43 t).Sample text —————à ¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€- 45 u). Sample text —————————————————————————————————————- 45 v). Sample text —————————————————————————————————————- 48 w). Sample text —————————— Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€- 49 x).Sample text —————————————————————————————————————- 52 y). Sample text —————————————————————————————————————- 53 z). Sample text ————————————————â€⠀Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€- 59 a). Sample text —————————————————————————————————————- 1 b).Sample text —————————————————————————————————————- 4 c). Sample text —————————————————————— Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€- 6 b). Sample text —————————————————————————————————————- 8 d). Sample text —————————————————————————————————————- 10 e).Sample text ————————————————————————————— ————————- 14 f). Sample text —————————————————————————————————————- 15 g). Sample text —————————————————————————————————————- 19 h). Sample text ——————————————————————————————————â€⠀Ã¢â‚¬â€Ã¢â‚¬â€- 20 i).Sample text —————————————————————————————————————- 24 j). Sample text —————————————————————————————————————- 27 k). Sample text —————————————————————————————————————- 28 l). Sample text — ————————————————————————————————————- 29 m).Sample text ————————————————————————————————————— 31 n). Sample text —————————————————————————————————————- 32 o). Sample text ——————— ——————————————————————————————- 33 p). Sample text —————————————————————————————————————- 35 q).Sample text —————————————————————————————————————- 39 r). Sample text ———————————— Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€Ã¢â‚¬â€- 41 s). Sample text —————————————————————————————————————- 43 t). Sample text —————————————————————————————————————- 45 u). Sample text ——————————————————†”——————– ———————————- 45 v). Sample text —————————————————————————————————————- 48 w). Sample text —————————————————————————————————————- 49 x). Sample text ————————————————————————â €”————————————- 52 y). Sample text —————————————————————————————————————- 53 z).Sample text —————————————————————————————————————- 59 Make a list of all sections of proposal along with applicable page numbers. Cover this section on one page. Make a list of all sections of proposal along with applicable page numbers. Cover this section on one page. Table of Contents Table of Contents Proje ct Details Project Details Summary Summary Provide a brief executive summary of your project proposal here in this section including background, current situation, objectives, time scales and expected benefits.Provide a brief executive summary of your project proposal here in this section including background, current situation, objectives, time scales and expected benefits. Provide a brief executive summary of your project proposal here in this section including background, current situation, objectives, time scales and expected benefits. Provide a brief executive summary of your project proposal here in this section including background, current situation, objectives, time scales and expected benefits.Provide a brief executive summary of your project proposal here in this section including background, current situation, objectives, time scales and expected benefits. Provide a brief executive summary of your project proposal here in this section including background, current situat ion, objectives, time scales and expected benefits. Provide a brief executive summary of your project proposal here in this section including background, current situation, objectives, time scales and expected benefits.Provide a brief executive summary of your project proposal here in this section including background, current situation, objectives, time scales and expected benefits. Provide a brief executive summary of your project proposal here in this section including background, current situation, objectives, time scales and expected benefits. Provide a brief executive summary of your project proposal here in this section including background, current situation, objectives, time scales and expected benefits. Business Objectives Business ObjectivesPlease identify main objectives of your project in this section by giving a concrete statement describing your project and what you want to achieve with this project. You have to show here that your project is SMART that is Specific, M easureable, Attainable, Realistic and Time bound. Please identify main objectives of your project in this section by giving a concrete statement describing your project and what you want to achieve with this project. You have to show here that your project is SMART that is Specific, Measureable, Attainable, Realistic and Time bound. Objective No| Objective Description| | | | | | | | |Please identify main objectives of your project in this section by giving a concrete statement describing your project and what you want to achieve with this project. You have to show here that your project is SMART that is Specific, Measureable, Attainable, Realistic and Time bound. Please identify main objectives of your project in this section by giving a concrete statement describing your project and what you want to achieve with this project. You have to show here that your project is SMART that is Specific, Measureable, Attainable, Realistic and Time bound. Objective No| Objective Description| | | | | | | | |Deliverables and Limits Deliverables and Limits The deliverables are the changes that the project has to make in order to achieve the business objectives. Typically these will be system implementation or change to achieve the business process changes described in objectives. In many projects mis-understandings often arise because there are limits to the scope of the project that have not been clearly stated e. g. the system will only be available to a certain group of users, the deliverable is only intended to capture a proportion of the transactions, the system will only be delivered on University supported browsers.Typical deliverables for IT projects are listed below. * New IT Service * New MyEd channel * New Software system * Suite of Reports * Implementation service * Infrastructure Upgrade * Business Process Change The deliverables are the changes that the project has to make in order to achieve the business objectives. Typically these will be system implementation or change to achieve the business process changes described in objectives. In many projects mis-understandings often arise because there

Religion and Its Effects on Children Essay - 1657 Words

Children are free spirits sensitive to many outside influences, especially religious influence. Religions have begun to strengthen their child involvement; this is why they have begun placing more emphasis on children ministries. If religion is a major part of a child’s life, it will shape who a child becomes and how the child is raised. Religion directly affects how a child grows up and how they perceive people and situations in their life after their childhood. In order to understand how religion affects a child, it is important to view the various situations through a variety of religious perspectives and approaches. As suggested by Don Browning, in Children and Childhood in American Religions, â€Å"A careful investigation of religions in a†¦show more content†¦Browning argues that, â€Å"even though childhood has distinct biological parameters, societies and social groups construct the meaning and nature of childhood to a considerable extent around powerfulâ₠¬ ¦ religious ideas† (4). These societies and social groups are affecting all parts of children’s lives, particularly their education. Education is an important part of many religions; some apply more emphasis than others do. Either way children are subjected to the pressures of completing high levels of education. Children subjected to the stress of academic achievement are the products of religious influence. The pressures of doing well in school are a major characteristic of religion. Religions are resolute and controlling when it comes to their prospective followers getting the best education possible. Wanting to control all aspects of a child’s life is common in many religions. They go as far as creating their own private schools to ensure that their children are properly educated. Browning confirms, stating, â€Å"Religions with detailed prescriptions, laws, and regulations for how to live are more likely to want to control the educational formation of their children by establishing alternative school systems† (16). Gaining the children’s attention at very early ages, with the schools grade levels ranging from pre-k to college level, benefits the religion at work. The educationalShow MoreRelatedEffects of Religion on Children, Positive or Negative?1296 Words   |  6 PagesRunning head: EFFECTS OF RELIGION 1 Effects of Religion on Children, Positive or Negative? Anthony C. Antonelli Everglades University EFFECTS OF RELIGION 2 Abstract This work will discuss the positive as well as negative effects of religion on children. 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From what I have experienced, some religious groups often preach for them to stay with fellow believers however, our countryRead MoreAssess the Reasons Why Young People Seem to Participate in Religious Activity Less Than Older Generations923 Words   |  4 PagesShown in recent statistics, is that younger people, who are over 15 (as children under this age are usually forced by parents), do not participate in religious activity as much as the older generation – excluding the over 65s group, this is usually because they cannot get to church easily due to ill-health and disabilities. Brierley supports the idea of an older generation by showing that the average age of church goers in 1979 was 37 which increased to 49 in 2005, clearly showing a trend towardsRead MoreSame Sex Marriage754 Words   |  4 Pagesof its effects on children, its unnaturalness, and its religious immorality. 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