Purchase Plans Research Articles

What Drives and Mediates Organic Food Purchase Intention: An Analysis Using Bounded Rationality Theory

The prime objectives of this article are—to examine the effects of attitude, subjective norms, perceived social support and self-efficacy of organic food purchase plan. In so doing, a theoretical framework using the theory of bounded rational planned behavior (TBRPB) as its basis was established. Data (n = 335) were collected from the consumers residing in Dhaka city. The data were analyzed employing structural equation modeling (SEM). Findings show that attitude, subjective norms and perceived social support have direct effects on consumers’ intent or plan as well as mediating roles in the link between self-efficacy and organic food purchase plan.

Sustainable Perspective in Public Educational Institutions Restaurants: From Foodstuffs Purchase to Meal Offer

This study aimed to evaluate aspects of sustainable nutrition in Public Educational Institutions Restaurants (PEIR) in a Brazilian state. Cross-sectional descriptive research was conducted in six PEIR. Purchased foodstuffs for a one-month period were investigated from the perspective of their origin (place of production), processing degree and nutritional profile. The presence of Genetically Modified Organisms (GMO) in packaged foodstuffs was also evaluated. Regarding served meals, a four-week period was evaluated in each PEIR considering the Water Footprint (WF) and the nutrient composition of the lunch meals. Results showed that 31.6% of foodstuffs purchased in the period evaluated were from national origin. Analysis of the processing degree of food purchased showed 64.8% unprocessed or minimally processed foods. However, 60.8% of the foodstuffs purchased in a one-month period presented sodium excess, 46.9% had an excess of saturated fat and 40.1% contained an excess of free sugar. The presence of GMO was observed in 9.2% of packed foods. The meals showed a per capita WF average of 2165.8 liters, an energy supply of 834.6 kcal and 1,289.6 mg of sodium per meal served. Foodstuff purchase and menu planning are essential steps towards achieving sustainable meal production and the results showed that foodstuffs purchased in institutional restaurants during the evaluated period was not in line with the precepts of a healthy and sustainable diet. Studies that assess the impact of meal production on the different dimensions of sustainable nutrition are essential to better understand this complex production process.

Yield Spread and Economic Policy Uncertainty: Evidence from Japan

In this paper, we adopt the nonlinear autoregressive distributed lags (NARDL) model extended by Shin et al. (2014) to investigate the relationship between the treasury yield spread and economic policy uncertainty (EPU) in Japan. This model helps us to explore the short- and long-run asymmetric reactions of explained variables through positive and negative partial sum decompositions of changes in the explanatory variable(s). In our research, the testing of the NARDL specification reveals the existence of a significant long-run asymmetric equilibrium between the yield spread and EPU in Japan. On the other hand, we find a significant positive nexus between the treasury yield spread and EPU reduction in the long run. We speculate that because of low inflation, a poor economic outlook and the low interest rate environment since 1990, financial agents are markedly sensitive to negative shocks resulting from EPU. This means that when facing a good economy, bond agents are quick to sell, especially with higher-risk long-term interest rate bonds. Meanwhile, because the Bank of Japan announced the Stock Purchasing Plan in October 2002 and from the point view of portfolio management, while the influence of a positive economic outlook dominates the negative outlook, flight from quality has no role in asset portfolio adjustment. The empirical implications are that the long history of unconventional monetary policy supports the demand for both bonds and stock markets. When taking the stock market into consideration, the correlations between the yield spread, EPU and stock market capture the full wealth effects of the low interest rate environment in Japan.

International tourist’s expenditure on souvenirs in Ghana: Do their socio-demographics have any influence?

Shopping for souvenir is an important aspect of the tourism activity which almost all tourists participate in. The purpose of this paper is to examine the expenditure pattern of international tourists on souvenirs in Accra. The study adopted the descriptive design and questionnaire was administered to gather data. Data collected from 196 international tourists who were sampled from hotspots in Accra, using convenient sampling method, revealed that there was a significant relationship between socio-demographic characteristics of tourists and expenditure on souvenirs. Therefore, socio-demographic characteristics of an individual was found to have likely influence on the individual’s expenditure on souvenirs. Also, a majority of the respondents, who were first time visitors, spent less on souvenirs. A significant average expenditure on souvenirs signifies that the souvenirs business is a profitable business that needs to be maintained and enhanced. Majority of the respondents had souvenirs as part of their purchase plans. Leisure travellers were noted to spend more on souvenirs than other travellers. Souvenir shopping generates a good amount of money and as such the Centre for National Culture and Ghana Tourism Authority should embark on activities aimed at packaging and promoting the arts and crafts of the country so as to arouse sales of souvenirs.Keywords: Souvenirs, Arts and Crafts, Tourists, Expenditure, Purchase

Price risk management by using dynamic hedging based on advanced Black–Scholes model

Price Risk Management plays an important role in Commodity trading and corporate purchasing or Sales plan. Futures are used to hedge the price risk which is linear, while options are used for the nonlinear one. This paper proposes an evaluation method of dynamic hedging strategy for corporate hedging commodity price risk based on advanced Black–Scholes Model. By using the inverse replication method, we get the dynamic hedging strategy which uses futures to replicate options. Finally, we apply the dynamic hedging strategy for corporate purchases and sales to either lower purchase cost or maintain the sales price.

Load forecasting based on grasshopper optimization and a multilayer feed-forward neural network using regressive approach

This paper introduces a proposed model for mid-term to short-term load forecasting (MTLF; STLF) that can be used to forecast loads at different hours and on different days of each month. The combined MT-STLF model was investigated to aid in power generation and electricity purchase planning. A hybrid model of a multilayer feed-forward neural network (MFFNN) and the grasshopper optimization algorithm (GOA) was introduced to obtain high-accuracy results for load forecasting using the combined MT-STLF model. The MFFNN is prepared by processing the input layer and output layer and finally selecting a suitable number of hidden layers. The main steps in developing the model from the MFFNN include entering the data into the network, training the model and finally implementing the prediction process. The accuracy of the model obtained before using the GOA was lower than that after applying the GOA. Weather factors such as the temperature were used as inputs to the MFFNN during MT-STLF modelling to ensure high accuracy. In the proposed model, the temperature had a clear effect on the forecasted load. Additionally, there was a difference between the maximum and minimum loads in winter and summer months. A regressive model was introduced to determine the relations between the dependent variable (the load) and the independent variables that affect the load, such as the temperature. The regressive model used in the paper highlights the effect of the temperature on the hourly load. The accuracy of the hybrid model is satisfied with deviation error varied between −0.06 and 0.06. Moreover, the performance of the proposed forecasting model has been assessed by three indices; Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) then, compared with other forecasting models considering other optimization algorithms.

Design and Implementation of ERP System Based on SSM Framework

With the continuous development of enterprise information technology, information resource sharing has become a major subject of scientific research projects. Traditional enterprises have complex business processes, low management efficiency and high operation cost, which leads to enterprises of low competitiveness. Therefore, it is the best choice for enterprises to regain their core competitiveness to use ERP management system to improve their operation process, simplify their operation and make their operation methods transparent. This paper designs an ERP system based on SSM framework. This paper introduces the basic principles of SSM framework, respectively realizes the business functions of Controller layer, Service layer and DAO layer in the framework, and designs Web pages for data collection and display. The main components of the system consist of six modules: purchase plan, purchase order, purchase contract, purchase document inquiry, purchase to order and purchase return, which can well realize the actual requirements of enterprises.

Evaluating forces associated with sentient drivers over the purchase intention of organic food products

The study proposes to find out the factors which influence awareness among the consumers towards purchasing organic food product. The study is based on primary data by using tools Chi-square test, Cronbach alpha, KMO, and Bartlett's test, ANOVA, regression, correlation, and cross-tabulation. The study found that awareness driver's nutritional information, price, certification, brand name, and logos have an essential influence on the purchase intention of the product of organic food. However, labeling and food standards do not show a noteworthy rapport between labeling and organic food products' purchase plans. The core commitment and flow to explore are to analyze purchasers with respect to organic guarantee systems (accreditation, guidelines, logo, imprints, and confirmation) so we can distinguish the genuine organic products. The independent factors of awareness like organic buying preference and buying frequency, have a significant influence on the purchase intention of organic food. The research provided evidence of consumer awareness and purchase intention of organic food that would help the organic food industry to promote their products according to the attribute of customers.

A framework to enhance the procurement processes based on hybrid approach of FMEA-DEA

In the present study, the re-engineering of procurement processes, as a crucial process in any organisation, is investigated based on the FMEA-DEA hybrid approach. Using the business process reengineering (BPR) method and holding many meetings with the industry elites, the prime challenges of procurement processes are detected. FMEA method is implemented to assess these challenges and DEA method is also utilised to prioritise them. In addition, the sub-processes of procurement process with higher levels of priority are then selected and the feasibility of using information technology in these sub-processes for tackling the identified problems is investigated. Inappropriate purchase plans, fluctuation of prices for supplied materials and prolongation of the purchase procedure are the main ranked challenges based on the proposed model. Finally, providing a suitable purchase plan, implementing a cost management system, and enhancement of documentation system are the main suggested actions to be executed for improving the procurement process.

Consumer Search and the Uncertainty Effect

We consider a model of Bertrand competition where consumers are uncertain about the qualities and prices of firms' products. Consumers can inspect all products at zero cost. A share of consumers is expectation-based loss averse. For these consumers, a purchase plan, which involves buying products of varying quality and price with positive probability, creates scale-dependent dis-utility from gain-loss sensations. Even if their degree of loss aversion is modest, they may refrain from inspecting all products and choose an individual default that is first-order stochastically dominated. Firms' strategic behavior can exacerbate the scope for this uncertainty effect', and sellers of inferior products may earn positive profits despite Bertrand competition. We find suggestive evidence for the predicted association between consumer behavior and loss aversion in new survey data.

Research on Electricity Market Transaction Model under the Background of Big Data

With the advancement of electricity market reform and the influence of new entities, electricity market transactions have formed a diversified development trend, which puts forward higher requirements for transaction technology support systems. In the trading market a few days ago, power producers participated in the bidding of electricity generation. The trading center gave the optimal trading plan and settlement price under the condition of satisfying the safety constraints of the power grid according to the published competitive load curve and the declared data of power generation companies. The commodities traded in the electricity market are electric energy and various services necessary for the realization of electric energy trading, so the trading in the electricity market is very different from the traditional generation plan. Only by establishing the bidding trading plan model of the trading center can the trading plan be carried out, thus forming the power purchase plan for the power generation company. This paper analyzes the basic sources and characteristics of big data in the power market, obtains the characteristic values of big data attributes of mutual benefit and win-win of both parties through the model, and realizes the visualization of transaction price.

The Design of Forecasting System Used for Prediction of Electro-Motion Spare Parts Demands as an Improving Tool for an Enterprise Management

Abstract This article describes the design of a simple forecasting system and its practical application to predict the sporadic needs for a spare part. The article shows new approach already implemented in the special servicing and production company in Slovakia and its results during a short period of performance after its implementation. Such a proposed model can be a part of the purchase planning of spare parts within the company’s logistics system. In some companies, the material flow of spare parts is dominant element in terms of logistics costs. Their management is therefore important for cost optimization, customer satisfaction and market sustainability in a competitive environment. The article, in its introductory part, provides an overview of similar practical solutions within the research of this topic, but many models are designed to be applied in a global market environment and predict the amount of spare parts needed in different industries. However, these models are difficult to use for the needs of a small enterprise, because the main problem lies in the time of a spare part demand rather than its quantity. If there is a need for a specific spare part, which costs several hundred or thousands of euros, but the consumption is only a few pieces per year or more than a year, the time prediction of required spare parts is therefore crucial.

Prediction of the service life of surgical instruments from the surgical instrument management system log using radio frequency identification

BackgroundBar code- or radio frequency identification (RFID)-based medical instrument management systems have gradually been introduced in the field of surgical medicine for the individual management and identification of instruments. We hypothesized that individual management of instruments using RFID tags can provide previously unavailable information, particularly the precise service life of an instrument. Such information can be used to prevent medical accidents caused by surgical instrument failure. This study aimed to predict the precise service life of instruments by analyzing the data available in instrument management systems.MethodsWe evaluated the repair history of instruments and the usage count until failure and then analyzed the data by the following three methods: the distribution of the instrument usage count was determined, an instrument failure probability model was generated through logistic regression analysis, and survival analysis was performed to predict instrument failure.ResultsThe usage count followed a normal distribution. Analysis showed that instruments were not used uniformly during surgery. In addition, the Kaplan–Meier curves plotted for five types of instruments showed significant differences in the cumulative survival rate of different instruments.ConclusionsThe usage history of instruments obtained with RFID tags or bar codes can be used to predict the probability of instrument failure. This prediction is significant for determining the service life of an instrument. Implementation of the developed model in instrument management systems can help prevent accidents due to instrument failure. Knowledge of the instrument service life will also help in developing a purchase plan for instruments to minimize wastage.

Change in Information Professionals’ Satisfaction with Discovery Services

ABSTRACTDiscovery services provide a one-box search engine, consolidating library content with a centralized index. In 2015, the authors surveyed hospital librarians regarding the purchase, implementation and maintenance, and information professionals’ satisfaction with discovery services. In 41% of the cases, librarians had purchased a discovery tool, and 58% of those respondents said they were satisfied with their product. The information professionals who decided against a purchase cited the cost, insufficient user benefit, overwhelming retrieval, lack of staff, and unfamiliarity with discovery tools. In 2018, the authors replicated the study to determine if information professionals’ attitudes toward discovery services had changed. The later survey showed a significant change in librarians’ attitudes in favor of discovery tools, as 92% had purchase plans. Of those who implemented a discovery service, 73% expressed their satisfaction with discovery products.

How Consumer Behavior in Daily Food Provisioning Affects Food Waste at Household Level in The Netherlands.

Food production and consumption have remarkable negative environmental effects, in particular food waste. Food waste occurs throughout the entire food system, but households make the largest contribution. Reducing unnecessary waste of food represents a crucial step toward overcoming global issues of food waste, hunger, and climate change. Identifying barriers in reducing food waste is important not only to government and policymakers, but also to food producers, retailers, and marketers. Therefore, the objective of this research was to find out how consumer behavior in daily food provisioning affects food waste. An online survey was set up to question Dutch consumers (partly) in charge of the household’s food management. A total of 211 consumers participated in answering questions on household composition, food management behavior (e.g., food purchase planning) and food waste awareness (i.e., concern about wasting food and intention not to waste food). Results show that purchase behavior in-store was the main driver of food waste. Specifically, participants indicated that buying more food than needed often had led to food waste. In addition, intention not to waste food acted as a moderator in the relationship between planning behavior and food waste. Age appears to have a diminishing impact on wasting food.

Inovação organizacional em compras públicas pelo SRP: análise da acurácia do planejamento participativo do Instituto Federal do Triângulo Mineiro (IFTM)

O Sistema de Registro de Preços (SRP) tem sido amplamente utilizado pelos órgãos públicos no planejamento de suas compras. Contudo, um dos grandes desafios em sua utilização é evitar a superestimação dos quantitativos registrados, isto é, adequá-los às reais demandas do órgão. No intuito de tornar as demandas do SRP mais próximas da realidade, o IFTM promoveu uma inovação em seu processo de planejamento de compras a partir de 2013, tornando-o participativo. Essa pesquisa teve por objetivo avaliar a inovação implementada por meio da análise dos resultados da efetivação das demandas dos registros de preços, a fim de verificar se estes refletiram a demanda real. Para isto, realizou-se um levantamento de todos os itens do planejamento participativo no período de 2013 a 2016, comparando os quantitativos registrados com os efetivamente adquiridos, por meio do indicador GARP – grau de aproximação entre o real e o planejado. Os dados foram submetidos ao teste de duas amostras de Kolmogorov-Smirnov e à escala GARP. Constatou-se que o planejamento participativo não atingiu o objetivo de aproximar as demandas do SRP às reais necessidades do órgão. Como propostas de melhoria, sugeriu-se: adoção do índice do período anterior como subsídio para planejamento de quantitativos; conciliação dos valores licitados com o orçamento; capacitação dos servidores envolvidos; ações de monitoramento e controle e revisão da política de uso do SRP

Attention for the Inattentive: Positive Effects of Negative Financial Shocks*

Abstract Using unique data on employee ownership plans, we find that negative shocks lead to changes in portfolio choices among previously inattentive investors. Employees within employee ownership plans did not actively select into ownership, but instead received grants upon employment. Many employees remain initially passive within these plans. However, following the 2008 Financial Crisis, they become more likely to exercise options, sell restricted stock, and participate in an Employee Stock Purchase Plan (ESPP). These changes likely improve welfare. For example, nonparticipation in an ESPP leaves money on the table. Our results improve our understanding of how investors’ personal return experiences affect investment choices. Negative shocks can increase attention among previously passive investors, yielding decisions that are closer to the optimum.

Learning from Coworkers: Peer Effects on Individual Investment Decisions

ABSTRACTUsing unique data on employee stock purchase plans (ESPPs), we examine the influence of networks on investment decisions. Comparing employees within a firm during the same election window with metro area fixed effects, we find that the choices of coworkers in the firm's ESPP exert a significant influence on employees’ own decisions to participate and trade. Moreover, we find that the presence of high‐information employees magnifies the effects of peer networks. Given participation in an ESPP is value‐maximizing, our analysis suggests the potential of networks and targeted investor education to improve financial decision‐making.

I am therefore I own: Implications of organization‐based identity for employee stock ownership

AbstractMost research on employee stock plan participation investigates the effects of such forms of ownership on employee attitudes, leaving our understanding of the individual differences that contribute to employee ownership largely unknown. Drawing from the consumer behavior literature, our study explores the effect of organization‐based identity on the decision to participate in an employee stock purchase plan. The study was conducted in a newly public firm where we examine the effect of organization‐based identity on the purchase decision in two time periods. The first point in time was the initial public offering (IPO), when the employee has little information on how the firm's stock will perform in the market. The second point in time was the first quarter following the IPO, when employees have market data to help them with decision‐making. Results indicate that, as predicted, organization‐based identity is associated with participation in an employee stock purchase plan, at both points in time, above and beyond the influence of several economic and psychological predictors reported in prior studies.

A hybrid short-term electricity price forecasting framework: Cuckoo search-based feature selection with singular spectrum analysis and SVM

Under the liberalization and deregulation of the power industry, price forecasting has become a cornerstone for market participants' decision-making such as bidding strategies and purchase plans. However, the exclusive nonlinearity dynamics of electricity price is a challenge problem that largely affects forecasting accuracy. To address this task, this paper presents a hybrid forecasting framework for short-term electricity price forecasting by exploiting and mining the important information hidden in the electricity price signal. Moreover, a hybrid feature selection method (HFS) is introduced into the forecasting strategy. To exhibit the dynamical characteristics of electricity price, we primarily perform a singular spectrum analysis (SSA)-based systematic analysis process by using the merit of SSA and analyzing the multiple seasonal patterns of short-term electricity price series, providing a meaningful representation of the hidden patterns and time-varying volatility of electricity price series. Aiming at selecting the key features, the candidate variables are constructed considering the dynamic behavior of price series; further, to capture the optimal features from the candidates, the correlation threshold θ is defined for the adjustable parameters in HFS and optimally determined by the intelligent search algorithm. Additionally, triangulation based on the Pearson, Spearman and Kendall rank correlation coefficient is performed to strengthen the reliability of the proposed method. The proposed hybrid forecasting framework is validated in the New South Wales electricity market, which demonstrates that the developed approach is truly better than the benchmark models used and a reliable and promising tool for short-term electricity price forecasting.