In this study, the three types of quantitative research questions will be employed. Descriptive research questions will be utilized to seek answers pertaining to queries asking the frequency at which IT outsourcing and offshoring in businesses takes place. The question addresses the issues arising from quantity, that is, ‘how much’ or ‘how often’ do businesses outsource or offshore their services to other companies. These particular sets of questions therefore attempt to understand what could be the changes that occur over time in the course of IT outsourcing and off shoring. Conversely, descriptive questions may perhaps endeavor to unravel the extent of relationships existing between two variables, that is, the amount of time spent in the process and the availability of resources (Bugajska, 2007). Descriptive questions could therefore consist of the following:
- How often do firms participate in IT outsourcing and off shoring in the international market?
- What could be the relationship existing between the amount of time spent by companies in IT sourcing as well as off-shoring in relation to the profits of the company?
Predictive quantitative research questions could strive to determine whether a variable can be utilized in foreseeing some future results. The questions posed at this stage are intended to check whether one variable influences the other. Questions may include:
- Does IT outsourcing and offshoring foretells the propensity of firms to excel in the global market?
- How do the management in the IT outsourcing and off-shoring firms influence the performance of their clients in the global market.
Causal queries are meant to evaluate the various fluctuations of particular phenomenon, especially in endeavoring to classify causes of something. The questions repeatedly comprise the exploitation of independent variable, which are the IT outsourcing and off shoring and the contrasting of the result of the exploited variable. The research questions in this category could consist of the following:
- Does firm’s variation in IT outsourcing and off shoring produce a change in the company’s performance internationally.
Research questions should be reasonable, meaning that they should be situated within the study. Unreasonable questions are avoided at all costs since the cause ambiguity and other unnecessary inconveniences.
In hypothesis testing, the researcher in this study will particularly have two hypotheses, that is, the null hypothesis that frequently shows no alteration or no impact. Another hypothesis will be an alternative that is now utilized for experimental hypotheses. The substantiation from the trial is taken to support both the null and the alternative premise. Because the researcher is interested in hypothesis testing, an experiment pertaining to data collection will be performed. One sample from the population of the IT outsourcing and off-shoring interviewees will be taken, that is the top managers in the IT outsourcing firms, and then they will be interrogated prior to data collection while the other samples would not be interrogated. By doing this, in the end, the group that does best in giving out accurate information is determined. Finally, the researcher in this study comes to note whether the collected data comprise of evidence that alludes to the alternative-experimental or null hypotheses. In testing hypothesis, the researcher will neither confirm nor invalidate a hypothesis. All that will be established is either a sample supporting a hypothesis or the one challenging it. Overall, the researcher will have to measure some concepts contained in the hypothesis, which include the following:
- transform concepts into calculable aspects
- acquire these computable features and take them as variables
- discover quantity scales to enumerate variables
The identified hypotheses could therefore be:
- IT outsourcing and offshoring improves the performance of a company in the international market.
- IT outsourcing and off shoring does not add any value to the company’s performance in the global market.
Operational Definition of Variables
Subsequent to inventing the study objectives and premises and recounting completely the study intercession, the subsequently step in the investigative procedure is to classify operationally the important variables and stipulations of the study. Operational definitions provide two indispensable rationales, one being to institute the regulations and events that the study canvasser will utilize in measuring the important variables of the research and secondly it offers explicit connotation to expressions that or else may be understood in unusual ways. This exploration will contain operational definitions of key variables and expressions.
The independent variable in this study is IT outsourcing and off shoring since it is affected by other variables such as the type of management in an organization, the age of the managers and the educational level or the number of trained personal in an organization. The staff with high skills or high educational levels is likely to apply the same skills in outsourcing or off shoring their business IT requirements. The operational definition of IT outsourcing knowledge in this study will be:
Knowledge about IT outsourcing and = the number of perfect responses an IT
Offshoring program Outsourcing and offshoring manager in a firm gives to twenty questions asked on IT Outsourcing
In addition to the above operation definition, the researcher in this study will also categorize the IT outsourcing and off shoring correspondence basing on their knowledge of the new technology. This is done by setting up classes of IT outsourcing knowledge, characterizing between the respondents who have IT outsourcing and off shoring acquaintance (Carmel & Agarwal, 2002). The groups are divided in terms of high knowledge, standard knowledge, little know how, without information pertaining to IT outsourcing and off shoring, and each grouping needs an operational decree that informs the researcher how to allocate any specified respondent to the class. Another technique of operationally defining the groupings may be:
- Sky-scraping familiarity = accurate replies to eighteen or more of the twenty questions asked.
- Standard Knowledge= Correct responses to between eight and fourteen of the twenty Questions posed.
- Little knowledge= Correct responses to between two and six of the twenty questions asked. This implies that a certain category of the sampled population is not well Conversant with the technology.
- Without knowledge= No accurate responses to any of the twenty questions posed. This means that some of the correspondences are not well conversant about outsourcing technology.
The researcher notes that the four classes of the variable are reciprocally exclusive meaning that they will never overlie. Consistent with the operational regulations recognized, IT outsourcing and off shoring respondent cannot be located in the group “sky-scraping familiarity” and simultaneously be sited in the “standard,” “little,” or “without knowledge” class. The classes are as well very inclusive. There are simply four classes.
In quantitative study, the researcher winds up with facts after accomplishing his or her investigation. These are to be studied in this exploration, and then construed in view of the study queries and other pertinent premises and study conclusions. To facilitate the development of figures for quantitative investigation (facts), a measurement procedure should take place. Alternatively, the canvasser in this study will translate a few human phenomenons precisely into statistical figures. The procedure of changing observable facts into data is referred to as measurement. In the social sciences, much of what is endeavored to be calculated is biased such as conceptions including physical health, which have furry descriptions (Grover, Cheon & Teng, 1996). Consequently, measurement turns out to be a tricky and multifaceted subject, and clamor is forever formed in the information because of imprecision in the procedure of measurement. Therefore, it is imperative to reduce clamor by utilizing consistent and suitable techniques of measurement.
The achievement of whichever study tool is typically measured in terms of consistency, soundness and sensitivity in addition to specificity. These ideas will also be useful in this study. The researcher employs them to determine the reliability of the researcher (Heeks, et al. 2001). Clarke suggests that consistency is the capability by which an investigation is competent enough to generate results that are reliable and firm over a specified period of time and given comparable state of affairs (Clarke, 1998). A variety of validities subsists, which consist of interior validity and external validity. Interior validity pertains to the relationship between objects when measured on a range. At whatever time that an investigation offers equivalent outcomes after the use of two diverse measures, the result is alleged to be corresponding.
Validity is the point at which a specified tool is calculated to measure. The validity of a research can fluctuate in dissimilar illustrations employed. In one state of affairs, an investigation can be convincing whereas in another state of affairs, it may possibly not. The validity of a research is calculated by what the research alleges to measure and the accessibility of coherent errors in the conclusions gotten from the exploration. Crotty (2003) argues that interior validity is the scope to which it is feasible to make self-regulating orientation from the conclusion of a study particularly if the independent variable controls the dependent variable. It is possible to measure variables in this study since IT outsourcing and off shoring, being a dependent variable is controlled by other variables such as the availability of finances to undertake it and the accessibility of skilled labor. Conversely, outside validity is the universal submission of the results of a study to other sceneries. The findings on IT outsourcing and offshoring are critical to the improvement of business performances in the global market (Jennex & Adelakun, 2003).
The measurement of the hypothetical construct of an exploration is calculated using construct validity while convergent validity makes judgment between the achievements that are attained from diverse apparatus that are utilized in the exploration. Unlike convergent validity, divergent validity compares the instruments used in the study that calculates conceptions, which are conflicting. Due to the above validity and reliability, the exploration is convincing and consistent for application by several individuals or calculated branches (Clarke, 1998).
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Jennex, M.E. & Adelakun, O. (2003). Success factors for offshore system development. Journal of Information Technology Cases and Applications. 5 (3), 12–31.