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This paper proposes and tests a model to explain the outcomes of three different information presentation formats. Based on cognitive fit theory, information visualization formats that best fit task characteristics are expected to lead to improved decision-making outcomes. We apply the Judgment and Decision-Making framework (Bonner, 2008) to investigate how certain factors can impact decision quality.

This study tests whether certain production variance presentation formats (percentages, dollar amounts, and schematic faces), task complexity, understanding of the presentation format, motivation, and effort increase the accuracy of a supervisor’s bonus calculation. A total of 281 students and professionals participated in this experiment. Their responses were examined using regression analysis.

Our results indicate that individuals mostly prefer the percentages presentation format and that the use of the percentages presentation format, a lower level of task complexity, and a better understanding of the variance presentation format lead to more accurate calculations in the experimental task.

Our study provides a call for further research on factors that influence the choice of presentation format as a potentially fruitful area for management accounting researchers.

We exhort practicing management accountants to exert direct influence on employees’ decision making through the use of variance presentation formats that fit their tasks and promote understanding.

Our experiment introduced two major innovations: it uses an interactive data visualization approach allowing subjects to select their preferred presentation format; and it focuses on production variances, a topic that has received less attention in the academic managerial accounting literature, but is still very relevant to practitioners.

The purpose of this paper is to document the level of risk in the Québec egg sector (conventional and specialty eggs) and analyze the optimal choices of Québec egg producers that must allocate limited resources to production of different types of eggs.

A quadratic programming approach applied to expected mean-variance models is used to analyze the impact of risk on decision to invest when the resources must be allocated to different type of production that have different risk levels. The model is calibrated using monthly data from 2009 to 2016.

Results indicated multiple uncertainty sources (technological, cost of production, price of eggs) that vary according to the types of eggs. Given risk aversion parameters, producer would favor production modes with the lowest producers’ price variance, which correspond to free-run eggs. Results also indicated that in response to a greater intensity of risk aversion, the course of action producers may choose is to increase the relative production of free-run eggs.

The empirical limits of this research are found in the lack of quality data on producer prices and costs for specialty eggs. Future research could explore the relationship between the growing impact of egg for processing, which price is based on the US price, and its relationship with specialty eggs.

The findings of the study will be useful for policy makers and managers of eggs supply chain. This is important, given the recent announcement by Canadian’s large retailers and fast food companies to increase cage free eggs offering and, in some cases, eventually only selling these types of eggs.

This study adds to the understanding of the role of risk and uncertainty in the investment decision of egg producers and different mode of production, as well as in the development of the growing production of specialty eggs in Canada. It fills a gap in the literature regarding the impact of risk in Canadian egg production. This gap is likely explained by the perception of a lack of risk in this supply managed sector in Canada and its small size relative to other supply managed sector.

The tool described is most appropriate for a first-level undergraduate course in cost/management accounting, which is typically taken in the second year of a post-secondary business program.

This chapter discusses a method for teaching a challenging topic within cost/management accounting, which is calculating variances for expenses. The proposed methodology focuses on a “common sense” understanding of variances as differences between budgeted and actual results. The new approach (i) uses a golfing analogy as a frame of reference, (ii) includes questions to assist in the analysis, and (iii) provides a table to organize and calculate variances. The variances examined include eight common expense-side variances used by manufacturers: material price and efficiency variances; labor price and efficiency variances; variable overhead spending and efficiency variances and fixed overhead spending and production volume variances.

By using this tool, students will be able to understand how and why variances are calculated. It will also provide them with better insight into appropriate corrective action that will address deviation from plans.

I provide a template to facilitate the calculation of variances, along with a list of questions that will guide students in their analysis. I also give an application of the suggested approach, using a standard textbook problem.

Productivity and production risks affect the use of agricultural production practices and inputs, particularly in developing countries. This paper aims to investigate the effects of adopting drought-tolerant maize varieties (DTMVs) on farm productivity, yield variance and downside risk exposure of maize growing households of Zambia.

The study uses household survey data collected from 11 maize producing districts of Eastern, Southern and Copperbelt provinces of Zambia using a structured questionnaire. The Antle’s flexible moment-based approach was used in specifying, estimating and testing a stochastic production function. The study further applied an endogenous switching regression model to control for both observable and unobservable sources of bias.

The study revealed that DTMV adoption increases maize yield by 15 per cent and reduces the risk of crop failure: reducing yield variance by 38 per cent and exposure to downside risk by 36 per cent.

This study establishes the benefits of DTMV adoption in Zambia with regards to productivity, yield stability and downside risk in the face of climate change. Results from this study underscore the need for more concerted efforts to scale-out DTMVs for both maize productivity enhancement and for risk mitigation against weather shocks.

The purpose of this paper is to delve into an extensive analysis of different food crops, ranging from bananas, beans, brinjals, cabbages, chillies, creepers, groundnuts, mixed vegetables, pineapples and tomatoes, over three decades. To maintain an ever-increasing population level, much stress is exerted on the production of food crops. However, till date, very little is known about how climate change is influencing the production of food crops in Mauritius, an upper-income developing country found in the Indian Ocean and highly vulnerable to climate risks.

Based on the interactions between production of crops, harvest area for crops and weather metrics, a vector autoregressive model (VAR) system is applied comprising production of each crop with their respective harvest area. Weather metrics are then entered into as exogeneous components of the model. The underlying rationale is that weather metrics are not caused by production or harvest area and should thereby be exogeneously treated. Should there be cointegration between the endogenous components, the vector error correction model (VECM) will be used. Diagnostic tests will also be entertained in terms of ensuring the endogeneity states of the presumed variables under investigation. The impact of harvest area on product is plain, as higher the harvest area, the higher is the production. However, a bi-directional causality can also manifest in the case that higher production leads towards lower harvest area in the next period as land is being made to rest to restore its nutrients to enable stable land productivity over time. Other dynamics could also be present. In case cointegration prevails, VECM will be used as the econometric model. The VAR/VECM approach is applied by virtue of the fact that traditional ordinary least squares (OLS) estimation approach will be biased and susceptible to trigger off unreliable results. Recourse is made towards the Johansen and Juselius (1990) technique. The Johansen and Juselius approach is based on the following VAR specification-bivariate VAR methodology. X1,t = A0 + A1,1X1,t – 1 + A1,2X1,t – 2+ […] .+ A1,p X1,t – p + A2,1X2,t – 1 + A2,2X2,t – 2+ […] .+ A2,pX2,t – p + ßjW + e1,t […] […]..(1) X2,t = B0 + B2,1X2,t – 1 + B2,2X2,t – 2+ […] .+ B2,p X2,t – p + B1,1X1,t – 1 + B1,2X2,t – 2+ […] .+ B1,pX2,t – p + ajW + e2,t […] […] […](2) X1,t is defined as the food crops production, while X2,t pertains to harvest area under cultivation for a given crop under consideration, both constituting the endogeneous components of the VAR. The exogeneous component is captured by W which consists of the nine aforementioned weather metrics, including the cyclone dummy. The subscript j under equation (1) and (2) captures these nine distinct weather metrics. In essence, the aim of this paper is to develop an econometric-based approach to sieve out the impacts of climate metrics on food crops production in Mauritius over three decades.

Results show weather metrics do influence the production of crops in Mauritius, with cyclone being particularly harmful for tomatoes, chillies and creepers. Temperature is found to trail behind bearish impacts on tomatoes and cabbages production, but positive impacts in case of bananas, brinjals and pineapples productions, whereas humidity enhances production of beans, creepers and groundnuts. Evidence is found in favour of production being mainly governed by harvest area. Overall, the study points out the need of weather derivatives in view of hedging against crop damages, let alone initiation of adaptation strategies to undermine the adverse effects of climate change.

To the best of the author’s knowledge, no study has been undertaken in Mauritius, let alone developing of an econometric model that properly integrates production, harvest area and weather metrics. Results show weather metrics do influence the production of crops in Mauritius, with cyclone being particularly harmful for tomatoes, chillies and creepers. Temperature is found to trail behind bearish impacts on tomatoes and cabbages production, but positive impacts in case of bananas, brinjals and pineapples productions, whereas humidity enhances production of beans, creepers and groundnuts. Evidence is found in favour of production being mainly governed by harvest area. Overall, the study points out the need of weather derivatives in view of hedging against crop damages, let alone initiation of adaptation strategies to undermine the adverse effects of climate change.

The increasing demands for customized services and frequent market variations have posed challenges to managing and controlling the manufacturing processes. Despite the developments in literature in this area, less consideration has been devoted to the growth of business social networks, cloud computing, industrial Internet of things and intelligent production systems. This study recognizes the primary factors and their implications for intelligent production systems' success. In summary, the role of cloud computing, business social network and the industrial Internet of things on intelligent production systems success has been tested.

Intelligent production systems are manufacturing systems capable of integrating the abilities of humans, machines and processes to lead the desired manufacturing goals. Therefore, identifying the factors affecting the success of the implementation of these systems is necessary and vital. On the other hand, cloud computing and the industrial Internet of things have been highly investigated and employed in several domains lately. Therefore, the impact of these two factors on the success of implementing intelligent production systems is examined. The study is descriptive, original and survey-based, depending on the nature of the application, its target and the data collection method. Also, the introduced model and the information collected were analyzed using SMART PLS. Validity has been investigated through AVE and divergent validity. The reliability of the study has been checked out through Cronbach alpha and composite reliability obtained at the standard level for the variables. In addition, the hypotheses were measured by the path coefficients and R², T-Value and GOF.

The study identified three variables and 19 sub-indicators from the literature associated that impact improved smart production systems. The results showed that the proposed model could describe 69.5% of the intelligence production systems' success variance. The results indicated that business social networks, cloud computing and the industrial Internet of things affect intelligent production systems. They can provide a novel procedure for intelligent comprehensions and connections, on-demand utilization and effective resource sharing.

Study limitations are as below. First, this study ignores the interrelationships among the success of cloud computing, business social networks, Internet of things and smart production systems. Future studies can consider it. Second, we only focused on three variables. Future investigations may focus on other variables subjected to the contexts. Ultimately, there are fewer experimental investigations on the impact of underlying business social networks, cloud computing and the Internet of things on intelligent production systems' success.

The research and analysis outcomes are considered from various perspectives on the capacity of the new elements of Industry 4.0 for the manufacturing sector. It proposes a model for the integration of these elements. Also, original and appropriate guidelines are given for intelligent production systems investigators and professionals' designers in industry domains.

Due‐date performance (DDP) is a very important performance indicator for the companies. Thus, companies with a high hit rate would have greater competitive advantage; on the contrary, companies that delay customers' orders frequently would lose sales opportunities and reputations. Therefore, there were many academic studies and practical efforts to improve DDP in the past, but the problem of low hit rate still exists. In order to increase the hit rate, some companies have focused on reducing the variation, while others focus on production management, but is the real problem affecting the low rate variability or production management? This is indeed difficult to be validated through practice. Therefore, this study designed three scenarios, tested each scenario for 30 times, each test involved seven subjects. The tests were to provide counter‐evidence in the Job Shop environment without variation. If the variation is the main factor of affecting hit rate, the rate at this time should be good; otherwise, the assumption that variation is the main cause is rebutted. The results demonstrated that production management planning is the main cause, and the method of enhancing the hit rate is obtained during the test.

The purpose of this paper is to conduct a large-sample empirical investigation of how relational capital impacts bullwhip at the supplier.

The study uses mandatory disclosures in regulatory filings of US firms to identify a supplier’s major customers and constructs empirical proxies of supply chain relational capital, i.e., length of the relationship between suppliers and customers and partner interdependence. Multivariate regression analyses are performed to examine the effects of relational capital on bullwhip at the supplier.

The findings show that bullwhip at the supplier is greater when customers are more dependent on their suppliers, but is reduced when suppliers share longer relationships with their customers. The results also provide additional insights on several firm characteristics that impact supplier bullwhip, including shocks in order backlog, selling intensity and variations in profit margins. Furthermore, the authors document that the effect of supply chain relationships on bullwhip tends to vary across industries and over time.

The study employs a novel data set that is constructed using firms’ financial disclosures. This large panel data set consisting of 13,993 observations over 36 years enables thorough and robust analyses to characterize supply chain relationships and gain a deeper understanding of their impact on bullwhip.

Information sharing and coordination between buyer and vendor have been considered as useful strategies to improve supply chain performance. The debate is about what information to share and how to share most cost‐effectively to maximize the mutual benefits of the supply chain as a whole and the individual business players. Proposes a systematic framework for investigating the impacts of sharing production information on the supply chain dynamic performance. This framework supports supply chain researches to study impacts of information sharing under various scenarios. Examines, under the framework, an inventory allocation problem in an arborescent distribution supply chain with two distribution channels competing for the same source of supply. Finds that the levels of benefits by sharing information vary with different players involved in the supply chain. Suggests some guidelines to balance the benefits in a supply chain in order to motivate information sharing.