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Regional differences in crop insurance uptake have persisted over time. To partly explain this phenomenon, the purpose of this paper is to propose and evaluate a budget constraint (heuristic) effect within the standard expected utility theory (EUT) framework through simulation methods.

Within the EUT framework, a standard simulation model is used to gain insights into farm insurance decisions when a budget constraint is in effect. The budget constraint is modeled as it has been revealed through the data on farmers’ insurance expenditures. In the simulation analysis, certainty equivalent values are used to rank farm options subject to the revealed budget constraint.

A budget constraint effect within the EUT framework stands out in explaining the observed regional differences. The proposed explanation is consistent with the historical trends on the ratio of crop insurance expenditure to expected crop value, higher premium rates in regions with lower crop insurance uptake, and the limited turnout for the 2014 Farm Bill’s supplemental area-based crop insurance products. Farmers’ crop insurance choices are found to be mostly constrained-optimal.

This appears to be the first study taking the revealed preferences approach to farmers’ crop insurance choices in a simulation analysis. Some policy implications are drawn and future research avenues are suggested. The findings should be of considerable value to policymakers, academics, bankers, and producers in regard to the design and use of risk management tools.

This paper aims to address a distributed assembly permutation flow-shop scheduling problem (DAPFSP) considering budget constraints and factory eligibility. The first stage of the considered production system is composed of several non-identical factories with different technology levels and so the factories' performance is different in terms of processing time and cost. The second stage is an assembly stage wherein there are some parallel work stations to assemble the ready parts into the products. The objective function is to minimize the maximum completion time of products (makespan).

First, the problem is formulated as mixed-integer linear programing (MIP) model. In view of the nondeterministic polynomial (NP)-hard nature, three approximate algorithms are adopted based on variable neighborhood search (VNS) and the Johnsons' rule to solve the problem on the practical scales. The proposed algorithms are applied to solve some test instances in different sizes.

Comparison result to mathematical model validates the performance accuracy and efficiency of three proposed methods. In addition, the result demonstrated that the proposed two-level self-adaptive variable neighborhood search (TLSAVNS) algorithm outperforms the other two proposed methods. Moreover, the proposed model highlighted the effects of budget constraints and factory eligibility on the makespan. Supplementary analysis was presented by adjusting different amounts of the budget for controlling the makespan and total expected costs. The proposed solution approach can provide proper alternatives for managers to make a trade-off in different various situations.

The problem of distributed assembly permutation flow-shop scheduling is traditionally studied considering identical factories. However, processing factories as an important element in the supply chain use different technology levels in the real world. The current paper is the first study that investigates that problem under non-identical factories condition. In addition, the impact of different technology levels is investigated in terms of operational costs, quality levels and processing times.

This paper applies the theory of loss aversion to public budgeting. It seeks to understand how loss aversion affects recommended budget amounts in two scenarios, one with explicit and one with implied risk levels. It also furthers the understanding of how the personality trait of risk propensity moderates recommended budget amounts in these scenarios.

Utilizing original data gathered from experimental vignettes, 339 US-based participants provided budget recommendations on two separate federal education programs. Participants were current budget professionals and master's-level students. One program utilizes a risky choice frame scenario while the other uses a goal frame scenario.

Participants are more likely to select a risky program option when the options are framed in terms of loss. Additionally, participants recommended larger budgets when they select the riskier program option. When presented with program goals, participants budget more when the goals are framed in terms of loss as opposed to gains. Results on participant risk propensity are mixed.

The discussion section includes multiple recommendations on how managers can approach budgeting with the intent of obtaining the most efficient budget allocation for the programs under their control.

The study is the first to examine framing and risk propensity in budgeting using two different types of framed messaging. Additionally, it is the only study to ask participants to recommend a budget amount after selecting a risky choice option. Therefore, results are more relevant to the entire process of public budgeting. Also, the study includes a mixture of participants with and without finance experience, providing insight into how different public employees allocate funds.

This chapter demonstrates (1) divergence between spending based upon a budget ratcheting model and a benchmark spending model, (2) that this divergence affects organizational performance, and (3) that internal benchmarking enables unit-to-unit performance comparisons, despite claims of organizational or unit uniqueness. We contrast two spending models to examine whether the divergence, or cost estimation gap, affects operating performance across inpatient (n=4,536) and outpatient departments (n=8,438) in 23 U.S. Army hospitals. Using a fixed-effects panel data methodology for fiscal years 2004–2006, we find that unit managers’ spending in this setting is more closely approximated by budget ratcheting. Using multiple performance metrics measured via a DuPont-like decomposition, we find that, within a specified range, operating performance generally improves as resources become constrained. Outside that range, however, we find nonlinear performance effects that approximate a quadratic loss function. Our benchmark model enables clinical department comparisons while controlling for facility, clinical specialty, and case mix severity. The resulting departmental comparability facilitates identification and communication of best practices across the entire Army hospital system. These results should be of interest to corporate executives, government officials, and agency managers who have responsibility for establishing funding mechanisms that include performance-based components.

The article examines the impact of policy change on enterprise unit subsidies that took place in 2009 on the quantity demanded for crop insurance.

The analysis covers corn, soybeans, and wheat that are grown in six economic regions and uses various measures of purchasing such as acres insured, unit structure, coverage levels, as well as crop hail use as proxies for the quantity demanded.

The analysis first employs time series econometric tools to analyze whether the time path of the share of enterprise units within buyup acres is influenced by the policy change in enterprise unit subsidies. It then comparatively examines the insurance experience between 2008 (right before the change) and 2015 (well after the change).

For corn, soybean, and wheat, the analysis establishes that the time path of the share of enterprise units within buyup coverage acres is statistically and economically influenced by the intervention. The analysis further quantifies the intervention's immediate and long-term impacts and finds that farmers' unit choices are highly responsive (elastic) to subsidy rates in those units.

Between 2008 and 2015, the insurance experience generally indicates that the share of enterprise units within buyup coverage surged, the share of acres under catastrophic coverage declined, and the share acres in high coverage levels increased. Meanwhile, growers have increasingly utilized crop-hail policies.

This appears to be the first study (1) quantifying the sensitivity of farmers' unit choices with respect to subsidy rates in those units and finding that such choices are actually highly responsive (elastic), and (2) pointing out the interaction between MPCI and crop-hail products and offering insights as to their combined use. The findings should be of considerable value to policymakers, academics, bankers, and producers in regards to the design and use of risk management tools.

To analyze capital budgeting practice in a group of small cotton ginning firms in Brazil. The study aims at describing how investment decision-making in the agribusiness context may be influenced by heuristics and by the business setting.

This research adopted an exploratory and qualitative approach in gauging the practice of capital budgeting in Brazilian cotton ginning firms and discussing actual managerial decision-making. Data collection involved interviews with managers of ten different firms and a further content analysis was performed.

Results reveal a practical managerial approach aimed at ensuring satisfactory net operating results in the short run. Sophistication in capital budgeting is not considered as essential, as institutional and strategic environment influences directly affect impose high risks. Investment decision-making is highly influenced by managerial experience.

Because of the chosen research approach, results may lack generalizability. However, in addressing a specific sector in a specific location, one can identify and craft strategies in response to managerial needs more effectively.

The paper clarifies how heuristics, managerial experience and the institutional context may influence investment decision-making in cotton ginning operations. It also suggests how actions aimed at evaluating risk and improving the screening of investment perspectives could contribute to improve investment decisions.

The paper provides an in-depth perspective in addressing the practice of capital budgeting in the context of a specific activity and describing key issues related to it.

The purpose of this research is to study the Multi-Skill Resource-Constrained Multi-Project Scheduling Problem (MSRCMPSP), where (1) durations of activities depend on the familiarity levels of assigned workers, (2) more efficient workers demand higher per-day salaries, (3) projects have different due dates and (4) the budget of each period varies over time. The proposed model is bi-objective, and its objectives are minimization of completion times and costs of all projects, simultaneously.

This paper proposes a two-phase approach based on the Statistical Process Control (SPC) to solve this problem. This approach aims to develop a control chart so as to monitor the performance of an optimizer during the optimization process. In the first phase, a multi-objective statistical model has been used to obtain control limits of this chart. To solve this model, a Multi-Objective Greedy Randomized Adaptive Search Procedure (MOGRASP) has been hired. In the second phase, the MSRCMPSP is solved via a New Version of the Multi-Objective Variable Neighborhood Search Algorithm (NV-MOVNS). In each iteration, the developed control chart monitors the performance of the NV-MOVNS to obtain proper solutions. When the control chart warns about an out-of control state, a new procedure based on the Conway’s Game of Life, which is a cellular automaton, is used to bring the algorithm back to the in-control state.

The proposed two-phase approach has been used in solving several standard test problems available in the literature. The results are compared with the outputs of some other methods to assess the efficiency of this approach. Comparisons imply the high efficiency of the proposed approach in solving test problems with different sizes.

The proposed model and approach have been used to schedule multiple projects of a construction company in Iran. The outputs show that both the model and the NV-MOVNS can be used in real-world multi-project scheduling problems.

Due to the numerous numbers of studies reviewed in this research, the authors discovered that there are few researches on the multi-skill resource-constrained multi-project scheduling problem (MSRCMPSP) with the aforementioned characteristics. Moreover, none of the previous researches proposed an SPC-based solution approach for meta-heuristics in order to solve the MSRCMPSP.

This study investigates the impact of the COVID-19 pandemic on investors' sentiments, behavioral biases and investment decisions in the Pakistan Stock Exchange (PSX).

The authors have assessed investors' behaviors and sentiments and the stock market overreaction during COVID-19 using a questionnaire and collected data from 401 investors trading in the PSX.

Results of structural equation modeling revealed that the COVID-19 pandemic affected investors' behaviors, investment decisions and trade volume. It created feelings of fear and uncertainty among market participants. Evidence suggests that behavioral heuristics and biases, including representative heuristic, anchoring heuristic, overconfidence bias and disposition effect, negatively influenced investors' decisions at the PSX.

This study will contribute to behavioral finance literature in the context of developing countries as it has revealed the impact of COVID-19 on the emerging stock market, and its results are generalizable to other emerging stock markets.

The findings of this study will help academicians, researchers and policymakers of developing countries. Academicians can formulate new behavioral models that can depict the solutions of dealing with an uncertain situation like COVID-19. Policymakers like the Securities Exchange Commission and the PSX can formulate crisis management strategies based on behavioral finance concepts to cope with situations like COVID-19 in the future and help lessen investors' losses in the stock markets. The role of the Securities Exchange Commission is crucial as it regulates the financial markets. It can arrange workshops to educate investors to manage their decisions during crisis time and focus on the best use of irrational and rational decision-making at the same time using Lo (2004) adaptive market hypothesis.

The novelty of the paper is that the authors have introduced overconfidence and disposition effect as mediators that create a connection between representative and anchoring heuristics and investment decisions using primary data collected from investors (institutional and retail) to demonstrate the presence of psychological biases during COVID-19, and it has been done for the first time according to authors' knowledge. It is a contribution and addition to the behavioral finance literature in the context of developing countries' stock markets and their efficiency.

The purpose of this study is to illustrate the potentially detrimental effects on audit decision-making of certain judgmental heuristics, which can lead to systematic judgmental biases. This paper provides background on the heuristics and biases approaches to decision-making to increase auditors' awareness of the anchoring and adjustment effects affecting audit judgments adversely.

This study reports the results of an experimental research design analyzing the audit judgment of 85 auditors in Switzerland.

Based on the results of the experiment, the results indicate evidence on the existence of the anchoring and adjustment heuristic in Swiss audit judgments. The authors could identify an influence of the audit company size, the auditors' experience and the auditors' knowledge about behaviorism and anchor heuristic with regard to the anchoring and adjustment effect on audit judgment.

The experimental tasks were relatively simple abstractions from the more complex analytical review situations faced by practicing auditors. Due to the small sample size, the authors cannot ensure representativeness of the results.

Professional judgment is a skill that auditor acquires overtime, combined with experience and knowledge, that allows him to achieve reasonable judgments, being independent of other opinions and free from material biases in a given circumstance. Our results show that auditors who are aware of biases and heuristics are less prone to judgment biases.

This paper is the first to analyze the impact of auditors' explicit experience and knowledge about behaviorism and anchor heuristic on the anchoring and adjustment effect on audit judgment. Through a stronger awareness of cognitive biases, a professional skepticism can be enhanced.

The way organizations cope with uncertainty in strategic decision making is prominently discussed. Concepts such as heuristics and simple rules are gaining increasing attention in strategic management research. However, despite their importance, little is known how heuristics and simple rules operate. Our qualitative study reveals that, first, strategic decisions consist of three basic elements: single rules, rule patterns, and emotional handling. Second, we find that firms develop generalizable rule patterns which follow a sequential order of inter-linked rules. Based on the findings we introduce the concept of organizational heuristics as inter-linked rule patterns drawing on organizational experience.