Demystify Business Politics with Statistics

Technique is only reliable when applied by statisticians with experience in data gathering and analysis.

Introduction

The pursuit of business excellence requires a systematic method of measuring organizational performance. As a result, the field of corporate performance assessment and its use of data and analysis to influence business choices presents statisticians with a potentially lucrative chance to provide value. However, to be efficient in this field, statisticians should understand the varying needs for statistical information in various company management zones.

Risk-taking actions are impacted by two decision-making methods: a rational, deliberate, better impulsive approach (Albert, 2011). However, it is uncertain whether the analytical and reactive decision-making approaches are entirely distinct, partially integrated, or completely interconnected. In addition, most studies have focused on why some people participate in greater risk-taking actions than others rather than why person may involve in certain forms of risk-taking more frequently than others.

Individuals’ risk-taking action decisions are most effectively conceptualized from a multiple process framework that integrates rational and reactive processes (Osman, 2013). The Prototype Willingness Model (such as discussed in (Gerrard, 2008)) is an example of a dual process model that has been shown to predict risk-taking behavior accurately. On the other hand, in the Prototype-Willingness paradigm, the reasoned process is seen as a deliberate, rational decision-making process that produces behavioral intents or plans to involve in or refrain from engaging in a behavior. The paradigm suggests attitudes or sentiments regarding subjective norms. The framework assumes that attitudes and subjective standards impact behavioral intentions, yet these sentiments are the most immediate predictor of conduct in the logical process.

Meanwhile, the reactive process is regarded as a more spontaneous, impulsive process that leads to the tendency to engage in an action if the chance presents itself. The Prototype Willingness concept acknowledges that people may not intend to engage in risky activity. But, if a person is willing and given the opportunity, they may engage in risk-taking (Gibbons, 1998). However, willingness is the closest predictor of action in the reactive phase, not perceptions of the prototype. Across multiple dual-process decision-making models, considering the rational and reactive decision-making processes together explains more significant variance in risk-taking behavior than either process alone.

The current work done by researchers defined decision-making processes as novel but partially interconnected methods; the reasoned or logical approach is portrayed as deliberate and well thought-out, whereas a reactive process is seen as more instinctive and spontaneous (Smith, 2013). The methods can be entirely different. In this instance, the distal elements of each operation may only be coupled to the proximal predictor of the respective process and not to the other method. Thus, sentiments and subjective norms are connected to risk-taking behavior via action intents but not willingness within the rational approach. Similarly, prototype perceptions are associated with risk-taking behavior via willingness but not behavioral intentions within the reactive phase.

Most studies examining different decision-making processes whereas predictors of risk taking decisions have concentrated on predicting a single type of risk-taking (Litt, 2014). On the other hand, individuals who engage in one form of risk-taking are likely to participate in additional forms. Additionally, variability in various forms of risk-taking shows that few persons specifically involve in certain forms of risk-taking while avoiding participation in others (Burfeind, 2015). Assessing several kinds of risk-taking allows for examining dual decision making processes as determinants of between-individual and within-person variability in risk taking.

Although the implications of a “rear-view mirror” decision-making approach to the present crisis will not be instantly evident, we have a sense of what can happen to businesses adopting this strategy. At best, executives, managers, and support staff are constantly in an emergency, putting out fires rather than working to achieve goals. Another example finances, an issue with which many companies are currently grappling. Due to a sharp decline in revenue, many businesses are attempting to evaluate their survival strategies. The short-term approach is to patch any gaps — whatever services or subscriptions are underutilized or not used.

In the Age of Big Data

Acquiring and exploiting statistics is crucial in decision-making, especially in an era where more data is created than ever before. In a world where the amount of data to examine is overwhelming, corporate and organizational leaders are faced with making decisions one after another. Consider the following data regarding data: 

• Internet traffic consists of 44 zettabytes of online data. 
• In the past two years, 90 percent of the world’s existing data was created.
• Every day, humans generate 2.5 quintillion bytes of fresh data. 
• We will generate 463 exabytes of data collectively by 2025 (Zarina Abdul Jabar, 2022).

Leaders and executives who want to make better-informed, evidence-based decisions may encounter issues with information overload. As never-ending heaps of data are sifted and translated into actionable information and steps, this highlights the significance of statistics in decision-making.

Finding Meaning in Substantial Amounts of Data

Through statistical analysis, a company or organization can collect and interpret data sets to reveal trends, patterns, and other factors that affect its current and future performance. Then, leaders can apply this information in a variety of productive ways.

• Assessing employee output and identifying areas for development.
• Recognizing tendencies in consumer tastes and actions. 
• Using historical data and current trends, we may anticipate how well a product will sell. 
• Efficiency auditing involves looking at how well different procedures are working.

Conclusion

Forecasting the future is perhaps the most compelling argument for using statistics in decision making. Although there are bounds to what can be predicted, predictive analytics has shown to be a valuable tool for helping decision-makers generate more well-informed forecasts of future events and trends. On the other hand, predictive analytics uses current and past statistics derived from precise data gathering to apply statistical algorithms and machine learning (or artificial intelligence) to forecast future trends and results. However, this technique is only valid when used by statisticians who are well-versed in data collection and analysis.

References

• Albert, D. a. (2011). Judgment and decision making in adolescence. Journal of research on Adolescence, 211–224.
• Burfeind, J. &. (2015). Juvenile delinquency: An integrated approach. . Routledge.
• Gerrard, M. G. (2008). A dual-process approach to health risk decision making: The prototype willingness model. . Developmental review, 29–61.
• Gibbons, F. X. (1998). Reasoned action and social reaction: willingness and intention as independent predictors of health risk. Journal of personality and social psychology, 1164.
• Litt, D. M. (2014). Spring break versus spring broken: Predictive utility of spring break alcohol intentions and willingness at varying levels of extremity. . Prevention science, 85–93.
• Osman, M. (2013). A case study: Dual-process theories of higher cognition — Commentary on Evans & Stanovich. Perspectives on Psychological Science, 248–252.
• Smith, A. R. (2013). Impact of socio-emotional context, brain development, and pubertal maturation on adolescent risk-taking. Hormones and behavior, 323–332.
• Zarina Abdul Jabar, Z. W. (2022). Zarina Abdul Jabar, Z., Wook, M., Zakaria, O., Ramli, S., & Afiza Mat Razali, N. (2022). ESTABLISHMENT OF BIG DATA ANALYTICS APPLICATION MODEL FOR MALAYSIAN PUBLIC SECTOR: AN EXPERT VALIDATION. . EDPACS, 1–20.