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Organizational Conflicts: Causes, Negative Impacts, Benefits, and Solutions
Introduction
Organizational conflicts are some of the major disturbances that occurs in corporate companies and these conflicts leads to some of major and drastic looses to the company it might be profit or sometime it might be reputation of the company or it might be some form of personal problems. Conflicts in organizations always results a negative effect but some time these conflicts also acts as motivation for the other employees to work hard for the company and it might eventually increase the company’s productivity. This paper is gong to elaborate some of the major cause of these conflicts and the negative impacts that these conflicts bring upon the individuals in the company and to the company itself. All these causes and the negative impacts are going to be collected from the data that this paper is going to collect by using the quantitative research method. This paper also provides some of the solutions those can be used against these conflicts and the solutions that can be adopted to solve the conflicts by conducting various quantitative research among several people in the corporate companies.
Data collection and presentation
Imai, (2018) Research data is gathered using a variety of methodologies, research data takes many different forms like videos, images, artefacts and diaries that are later presented in a research project so that they are effectively analyzed quantitatively using statistical tools through inferential and descriptive data analysis techniques. This information is collected, stored, and processed to produce and validate the original research results. Primary data sources like observations, surveys and interview and focus groups could be more advantageous in the research, collected data in research projects is obtained from different sources. For this these primary sources are very important because these data collected will help this research to identify what are the major causes of the organizational conflicts and what negative impact it caused on them can be learned effectively. The collected data is later converted into a readable- machine that is inform of a numeric format used for appropriate analysis by the computer programs. Stored data should be presented in a spreadsheet or database that can always be recognized when in demand. The stored data has to be frequently checked for accuracy via the occasional in spot checks on a given set of items on observations during the time of entry. During data entry, the researcher has to check the coder for bad data's common evidence adequately. Missing data has always been an inevitable part of any form of the empirical data set. In the field, respondents may not be up to answer some questions as they may appear ambiguously worded or too sensitive.
The participants used for this research are the people that work in the organizations because this paper is mainly focused on the organizational conflicts. By using participants that works in organizations will helps us to get as much information as possible. All of the participants are sampled from different departments of an organization so that the information can be collected broader and it helps to understand the study effectively. The required number of participants required to take part in any research would depend on the researcher's profile towards convincing an elite person to accept to get interviewed. To secure access to senior managers at the beginning of processes does not necessarily mean that the research would not be collected data from them. Research participants include vulnerable and elite participants. Vulnerable participants are specifically human beings considered to be susceptible to endure a given influence in a research setting. At times, this group entails persons who may be considered incapable of getting to understand what it means to participate in a given research. Vulnerable individuals have a demised capacity to anticipate, cope with, or become resistible to be in a position of recovering from the impact from a human -made hazard or a natural cause. The vulnerable groups may be up consisting of individuals who cannot care for themselves and have an increased chance of self-harm or the likelihood of harming others.
Chu, Lin, Gao, Xin, Zhang, & Wang, (2016) states that research that entails vulnerability occurs when the participants are not in a position to protect a person's interests and thus increase the probability of being intentionally or unintentionally harmed. The vulnerability can result from the inability of a person to understand and provide informed consent related to an unequal power relationship that is always up to hindering the basic rights. During research studies, an experienced researcher needs to interview the elite status people by making appropriate preparations, establishing appropriate trust, and facing interpersonal challenges that may occur during the interview. This means that interviewing individuals in senior positions in the organizations should show up unique methodological challenges that had not been there before, especially for the less experienced researchers, due to the power imbalance between the interviewee and interviewer. The target audience for this study are the people that works in organizations, entrepreneurs, and corporate employees. Because most of this study focus on the conflicts in organizations it helps the employees and business entrepreneurs to get to know what type of conflicts will arise and how to handle such conflict after reading this paper.
Ethical Issues and Biases Consideration
Merino-Saum, Clement, Wyss, & Baldi, (2020) stated that research ethics involves the daily requirements at work, protecting subject dignity, and publication in the research. There are several ethical issues that will rise in this research. At first is the identity of the participants, because we are interviewing several employees from an organization. If their identity gets reveled that will lead to the loose of employment or it might lead to rise of conflicts. Next is reveal of the research data is also major concern because if the data is revealed to unknown sources it might lead to the damage of the company reputation and it might also leads to some serious lawsuits. Research ethics should be established in the dissection process. Therefore, when tackling ethical issues and challenges in the dissertation, it's important to consider the research strategies that one can easily adopt and impact it through ethical issues. To eliminate the biases in the research all the collected data is taken into consideration from all the collected samples. Because some of the collected data is reliable as most of the participants are working employees of an organizations. Since they are all working in the same organization everyone will or should have faced some form of the conflicts. So, there is no chance of intentional bias but sometimes there will be a chance of unintentional bias.
Analysis of stationary data
Rabbani, Ayatollahi, & Jivkov, (2017) stated that stationary data analysis entails statistical properties, such as the mean, autocorrection, and variance within a given time, that are all are all constant. The statistical forecasting methods are mostly based on the assumption that the time series can appropriately be rendered approximately stationary through mathematical transformations. At times stationaries series predictions can appropriately be untransformed by reversing the existing mathematical transformations that had previously been in use in obtaining effective predictions for the original series. A stationaries series in most times it's relatively easy for an individual to predict. A researcher should be able to predict that the study's statistical properties will have to be in the same future just the way they have been in the past. Stationing a time series is always of great importance as a researcher may obtain meaningful sample statistics like the mean, variance, variables, and corrections; these statistics usually serve as future descriptors only if the series is stationary.
Stationary time series is important because the type of tools used in the time series analysis and forecasting is that they both assume stationarity. At times they help researchers identify the driving factors in their research studies such that when a change in time is detected, there is an equal change in some other time series, which may be able to infer a correlation. In cases where a time series is necessary, this clearly defines a lack of a broad trend in the provided data. This is always of great importance, mainly because there is always a close consideration in the time series forecasting. After detrending and deseasonalizing a given time series, the researcher tests the residual time series to enhance stationarity as a stationary time series can be able to have an autoregressive behavior that tells about the short time trends in the time of series.
Descriptive data analysis entails statistically presenting and describing the constructs of interest in between the constructs (Galindo-Martín, Castaño-Martínez, & Méndez-Picazo, 2019). The inferential analysis only involves the testing of hypotheses. Data coding is the process of converting data into a numeric format. This is achieved through a codebook that guides the coding process; thus, a codebook should be more comprehensive. It contains a well detailed descriptive data of variables in research studies. This includes the measures of each of the items in response to the ratio scale. Data coding is most important in large complex studies that may involve several variables and measurable items. Coded data must be fed in the database or spreadsheets into statistical programs like the SPSS. Most of these programs provide a data editor that is more effective in entering data; these programs can store data in their native format. As a result, it becomes more complex to share the stored data with the other statistical programs in a particular institution.
During data, some of the statistical programs can automatically treat blank entries as missing values, while others need numeric values to be denoted as a missing value, at times the missing values have to be just one of the items on a multi-item scale (Ibrahim, Hanna, Russell, Aboutaleb, & El-adaway, 2020). Still, if the missing value belongs to a single-item scale, researchers should use the average of other respondents to the scale's remaining items. These imputations seem to be biased if the missing value is in a systematic nature rather than a random nature.
According to Xu, He, Zhang, (2020), data transformation is important mainly because data values have to be effectively transformed before meaningfully interpreted. Reverse coded items should convey the coded opposite meaning of data. The other meaning of data entails creating scale measures by adding the individual scale items. Observational data can be effectively captured through the observation of a behavior or a particular activity. When data becomes collected by using human beings through observation, open-ended surveys, or an instrument or sensor to monitor and record an observation, this is so because the type of observational data provided has to be captured in real-time. The most advantage noted in observational research is that it enables businesses to observe potential customers in a natural setting, which can be up to revealing penetrating insights that become unavailable through several records like surveys and focus groups. The observational research studies help researchers to be in a position of modifying their vantage point that is based on real-time variables. Experimental data is collected through active intervention by the researcher to produce and measure change into creating a difference when a particular variable becomes altered. Experimental data typically allows the researcher to determine a causal relationship and is projected to a larger population. Simulation data is generated by limiting the operation of a real-world process over a specified time using computer-based models.
Longest (2019). Data simulation is used to determine what would happen under certain conditions. Simulation data enables experimentation on a valid digital representation of a particular system. Simulation provides a researcher with the ability to analyze the models as it runs set in simulation modeling that is apart compared with other methods. Compiled data involves using the existing data points, often from different data sources, to create new data through some data transformation. Quantifiable information can be used in calculations and statistical analysis in that the real-life decisions can be made based on the derived mathematical derivations. Quantitative data makes measurement more controllable. Quantitative data is usually collected for statistical analysis using surveys and questionnaires sent to a specific section of a population. Qualitative data collection checks out several factors that are up to establishing an understanding depth of raw information. For these, researchers should develop a more systematic empirical study to effectively comprehend data analysis of the empirical experiences needed to develop an in-depth understanding of the arising issues and the resultant strategy to be adopted. Sampling is a technique that allows researchers to infer information of a particular population that is based on the results from a subset of a population. This can be done without getting into the investigations of every individual.
Mishra, Pandey, Singh, Gupta, Sahu, & Keshri, (2019). When a researcher decides to reduce the population number of individuals in a study, this greatly reduces cost and workload. At times reducing the workload and costs may be easier towards obtaining high-quality information. Still, the procedure has to be balanced in a manner that one is capable of establishing a true association. In cases where samples are used, it’s nice that the selected individuals represent the whole population. This measure may involve specifically targeting people who are so hard to get
Taking hypothesis
Matviychuk, Steamers Harbou, & Holland, (2020). This is the method used in selecting samples to help a researcher learn more about particular characteristics in a given population. This is achieved using data measured in samples to determine the likelihood that a particular sample could have been selected, only if the hypothesis regarding a certain parameter is true. Being clear and specific, a research hypothesis provides an outcome of effective scientific research based on a population's particular property. For a researcher to adequately specify the research hypothesis serves as the best step towards planning a scientific research study. At most times, the quantitative research usually states a priori expectation about the research study results in the research hypotheses before conducting any study. The stated hypotheses have ever designed the planned research study and the research study; thus, the research hypotheses mainly require the researcher to think fully. Probability sampling entails starting with a fully complete sampling frame of all eligible individuals from which a sample is selected. Through this technique, all eligible individuals have the opportunity of being chosen for the sample. One is more able to generalize the assembled data from the study. At most times, the probability sampling methods tend to bring more time consuming and expensive than in non-probability sampling. A simple random sampling procedure involves an individual being chosen entirely by chance, and each member of the population has an equal opportunity to get selected. The best method of obtaining a random sample number in research is finding out the appropriate individual to include. Simple random sampling allows the sampling error to be calculated. It reduces selection bias, and its main advantage this is the most straightforward method of probability sampling while the other side of this sampling method is that a researcher may not be in an appropriate position to select enough individuals with characteristics of the same interest, especially in cases where the characteristics are termed as uncommon.
Agrawal, & Gupta, (2020). Systematic sampling, this is where the individuals become selected at regular intervals from the sampling frame. The researcher chooses intervals to ensure an appropriate adequate sample size. This method is more often and convenient than any other sampling method because it is easy to administer; however, it may sometimes lead to bias. The stratified sampling method is applicable when the population is divided into relevant groups who all share the same characteristic. Researchers chose these methods to determine measurements of interests to vary between the different subgroups. Stratified sampling is up to improving the accuracy and representativeness of the provided result by minimizing sampling bias. Clustered sampling procedure entails subgroups of a particular being used by the researcher as a sampling unit rather than individuals. The population becomes divided into specified subgroups that are randomly sampled into the research study groups. Cluster sampling is more efficient than simple random sampling because it has a large geographical space for the research to take place. The convenient sampling method is the easiest method used in sampling mainly because the participants are mostly selected based on their availability and willingness to participate. These useful results can effectively be obtained. Still, the provided kind of data can be prone to significant bias because the individuals who volunteer to take part may appear to be different from those who decide not to participate. Snowball sampling is used in social sciences by a researcher when trying to investigate hard-to-reach groups. Those existing subjects are further requested to nominate subjects they have well-detailed information about effectively, this technique thus increases in its initial size as that of a snowball.
Analysis and forecasting of time series
Ngantweni & Zondo (2020). Forecast time series involves making predictions majorly about the future. A researcher has to make the models fit on the historical data and use them to predict the future. Its performance towards predicting the future fully determines the type of skills that are provided by the time series forecasting model; the researcher often does this at his own expense such that the researcher can be able to explain on why a specific prediction was made, the existing confidence interval towards that prediction and even be in a position to understand better the underlying causes behind every problem. The time-series data have a temporary ordering; this technique makes time series analysis unique from the cross-sectional studies. There has always never been any natural ordering of the observations. At most times, the researcher had to note that the time series analysis is more distinct from spatial data analysis. The observations were believed to relate to the geographical positions typically.
Saini, & Singhania, (2018). Time series analysis can effectively be applied to a real-value, discrete numeric data or discrete symbolic data that contains specific characters like letters and words. The purpose of the forecast from the research studies is mainly to determine the power and required of the techniques towards governing the selection. Suppose the forecast needs to be set in standard against which would help in evaluating the performance. In that case, it will be so unfortunate that the forecast method would not take into special account actions.
Conclusion
Danner, Barbosa (2018). The researchers have now been in a position of sketching on how the future will be like through forecast. If a company is not in a position to make a significant change in its strategies and tactics, it will probably have to die. A researcher can adequately develop a linear, exponential regression time series forecasting method's through creating a time index variable that starts with the first observation moving to the most recent; these type of model is more important if a researcher will be in an appropriate position of underlying an assumption that this trend is more appropriate and relevant to the decided period and if the chosen model is not capable of taking seasonality into account then this can be done more effectively with linear regression.
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