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We evaluate the hypothesis of temporal stability in risk preferences using two recent data sets from longitudinal lab experiments. Both experiments included a combination of decision tasks that allows one to identify a full set of structural parameters characterizing risk preferences under Cumulative Prospect Theory (CPT), including loss aversion. We consider temporal stability in those structural parameters at both population and individual levels. The population-level stability pertains to whether the distribution of risk preferences across individuals in the subject population remains stable over time. The individual-level stability pertains to within-individual correlation in risk preferences over time. We embed the CPT structure in a random coefficient model that allows us to evaluate temporal stability at both levels in a coherent manner, without having to switch between different sets of models to draw inferences at a specific level.

We take Cumulative Prospect Theory (CPT) seriously by rigorously estimating structural models using the full set of CPT parameters. Much of the literature only estimates a subset of CPT parameters, or more simply assumes CPT parameter values from prior studies. Our data are from laboratory experiments with undergraduate students and MBA students facing substantial real incentives and losses. We also estimate structural models from Expected Utility Theory (EUT), Dual Theory (DT), Rank-Dependent Utility (RDU), and Disappointment Aversion (DA) for comparison. Our major finding is that a majority of individuals in our sample locally asset integrate. That is, they see a loss frame for what it is, a frame, and behave as if they evaluate the net payment rather than the gross loss when one is presented to them. This finding is devastating to the direct application of CPT to these data for those subjects. Support for CPT is greater when losses are covered out of an earned endowment rather than house money, but RDU is still the best single characterization of individual and pooled choices. Defenders of the CPT model claim, correctly, that the CPT model exists “because the data says it should.” In other words, the CPT model was borne from a wide range of stylized facts culled from parts of the cognitive psychology literature. If one is to take the CPT model seriously and rigorously then it needs to do a much better job of explaining the data than we see here.

Purpose: Small and medium enterprises (SMEs) are the most significant contributors to maximum employment generation, the gross domestic product (GDP) of many countries, and the overall global economy. It is also evident that cyber threats are becoming a big challenge for SMEs, which is directly impacting global economy.

Methodology: Existing research inputs were accessed to understand current cyber threats for SMEs and their cybersecurity posture. Additionally, this research has collected the latest insights by taking direct inputs from SMEs and conducting a well-designed research survey. It has provided a few direct inputs to designing solutions for the SME segment. For analysis and recommendations, cybersecurity best practices and core cybersecurity concepts are considered at the centre of the solution.

Findings: Implementing existing cybersecurity standards or frameworks is not easy for SMEs, as they generally have limited resources and different priorities for their business when it comes to the implementation of any cybersecurity controls. Currently, many cybersecurity standards are not able to support the implementation of business domain-specific controls.

Practical implications: Along with the research findings shared in this chapter, as a resolution to the problems faced by SMEs, the authors will propose a new framework as a solution. This framework is designed using core concepts of cybersecurity such as confidentiality, integrity, and availability (CIA triad) as well as defence in depth (DiD) mechanisms in each layer of organisation. The authors will also share a high-level idea about how reliable artificial intelligence-based software can help identify recommended controls for particular SMEs.

This chapter of the book seeks to use famous mathematical functions (statistical distribution functions) in evaluating and analyzing supply chain network data related to supply chain management (SCM) elements in organizations. In other words, the main purpose of this chapter is to find the best-fitted statistical distribution functions for SCM data. Explaining how to best fit the statistical distribution function along with the explanation of all possible aspects of a function for selected components of SCM from this chapter will make a significant attraction for production and services experts who will lead their organization to the path of competitive excellence. The main core of the chapter is the reliability values related to the reliability function calculated by the relevant chart and extracting other information based on other aspects of statistical distribution functions such as probability density, cumulative distribution, and failure function. This chapter of the book will turn readers into professional users of statistical distribution functions in mathematics for analyzing supply chain element data.