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A large categories of time series fluctuate dramatically, for example, prices of agriculture produce. Traditional methods in time series and stochastic prediction may not capture such dynamics. This paper tries to use machine learning to tune the model for a real situation by establishing a price determination mechanism on the model of stochastic automata (SA) and evolutionary game (EG). Time series volatility attributed to the chaotic process can be obtained through the learning algorithm of Markov Chain Monte Carlo (MCMC). Using machine learning through the chaotic analysis of stochastic automata and evolutionary games, we find that a more spatially aggregated distribution (smaller entropy) leads to larger time series fluctuations, regardless of the initial distribution of crops. By integrating the factors discovered in this study, we can develop a better learning algorithm in a highly volatile time series in agriculture prices.

The uncertainty issue in real-work optimization affects the level of optimization significantly. Because most future uncertainties cannot be foreseen in advance, the optimization must take the uncertainties as a risk in an intelligent way in the process of computation algorithm. Based on our risk-sensitive filtering algorithm, this study adopts a model-predictive control to construct a risk-averse, predictable model that can be used to regulate the level of a real-world system. Our model is intelligent in that the predictive model needs not to identify the system parameters in advance, and our algorithm will learn the parameters through data. When the real-world system is under the disturbance of unexpected events, our model can still maintain suitable performance. Our results show that the intelligent model designed in this study can learn the system parameters in a real-world system and minimize unexpected real-world disturbances. Through the learning process, our model is robust, and the optimal performance can still be retained even the system parameters deviate from expected, e.g., material shortage in a supply chain due to earthquake. When parameter error risks occur, the control rules can still drive the overall system with a minimal performance drop.

This study proposes a framework implementing triangular estimation for better modeling and forecasting time series. In order to improve the performance of estimation, we employ two sources of triangulation to generate a time series, which is statistically indistinguishable with the latent time series hidden in a system. Thanks to Bayesian hierarchical estimation, which is akin to deep learning but more sophisticate and longer history, the framework has been validated by a large amount of records in vegetable auctions. The hierarchical Bayesian estimation and Monte Carlo Markov Chain particle filters used in hidden Markov model are appreciated during the massive bootstrapping of data. Our results demonstrate excellent estimation performance in discovering hidden states.

PurposeThe attributes of services can be categorised as service quality and service preference. While studies have addressed the importance of service quality, shippers’ service preference and its relationship to perceived value and purchase intentions remain unexplored. Therefore, the purpose of this study is to propose a causal model in the context of short sea shipping services to investigate the influence of purchase intention through the shipper’s service preference and perceived value.Design/methodology/approachStructural equation modelling is applied to assess the empirical strength of the relationships in the proposed model. The model is validated through empirical testing by taking samples from shippers in Taiwan.FindingsThe results show that service attributes, namely, timing related, pricing related, warehousing, sales, door-to-door, information and advertising, positively affect shippers’ service preference. Service preference significantly affects customer perceived value as well as purchase intentions. Moreover, perceived value strongly affects purchase intentions.Originality/valueMatching between the product offered and the diversified customer need is key to the business operation’s success. This study suggests that carriers should position themselves to both self-competence and market values.

Recent regulations often mandate the recycling of scrap products, especially electrical wastes. This study demonstrates how this end-of-pipe enforcement can be converted to an effective policy tool in improving product design towards green product. We show that collective recycling can enhance producer responsibility whilst being affordable for coalition participants by adopting the proposed two-part allocation scheme, which is a particular solution of the underlying cooperative game and is closer to the nucleolus than the Owen set. Practically, our scheme is easier to implement than other core solutions. This scheme can establish institutional trust and promote environmental quality for companies participating in a recycling coalition. The goal of promoting green market is achieved through the implicit cooperation of participating companies.

This paper investigates the impact of consumer preferences on the intensity of competition for companies in a duopoly market. A classical Hotelling’s competition problem will be different if consumers are allowed to distribute non-uniformly. New results in competition intensity are established and conditions for the existence of a subgame perfect Nash equilibrium is identified through a model that considers generic distribution in consumer preferences. A competition strategy is demonstrated to depend on the signs of local change rates of the density function at the endpoints of market segments.

Researchers in the public health and the management information systems fields have attempted to improve understanding of the intentions of people who use social networking sites (e.g., Facebook and blogs) to share and acquire health-related information. The aim of this study was to identify factors (e.g., opportunities for socializing and self-improvement of health) that affect the performance expectations of people who use social networking sites for this purpose. The analytical framework was the theory of planned behavior. Data from a survey of 203 active Facebook users in Taiwan revealed that both attitude and subjective norms were significantly associated with behavioral intention. Additionally, behavioral intention significantly affected health-related information sharing and acquisition. Finally, health-related information sharing and acquisition significantly affected socializing activity, but health-related self-improvement did not. The theoretical and practical implications of this study are discussed.

Purpose This study proposes the use of cumulative prospect theory (CPT) to predict over- and under-estimation of risks and the counteractive adjustment in a cold chain context. In particular, the purpose of this paper is to address the importance of the socio-demographic characteristics of an individual in influencing risk attitude and the analysis of measurable risk probability. Design/methodology/approach This study uses CPT as the basis to develop a decision analysis model in which the two functions of value editing and probability weighting are nonlinear to adequately determine the flexible risk attitudes of individuals, as well as their prospects with numerous outcomes and different probabilities. An experiment was conducted to obtain empirical predictions, and an efficient Markov Chain Monte Carlo algorithm was applied to overcome the nonlinearity and dimensionality in the process of parameter estimation. Findings The respondents overweigh the minor cold chain risks with small probabilities and behave in a risk-averse manner, while underweighting major events with larger ones, thereby leading to risk-seeking behavior. Judgment distortion regarding probability was observed under risk decision with a low probability and a high impact. Moreover, the findings indicate that factors, such as gender, job familiarity and confidentiality significantly influence the risk attitudes and subjective probability weighting of the respondents. Research limitations/implications The findings fit the framework of CPT and extend this theory to deal with human risk attitudes and subjective bias in cold chains. In particular, this study enhances the literature by providing an analysis of cold chain risk from both the human decision-making and managerial perspectives. Moreover, this research determined the importance of the socio-demographic characteristics of an individual to explain the variability in risk attitudes and responses. Practical implications Managers must consider the issues of flexible risk attitude and subjective judgment when making choices for risk mitigation strategies. Given the focus on counteractive adjustment for over- and under-estimated risk, firms could evaluate cold chain risk more accurately, and thereby enhance their resilience to risky events while reducing the variability of their performance. Originality/value The current study is the first to materialize the phenomena of over- and under-estimation of cold chain risks, as well as to emphasize the different characteristics for loss aversion and judgment distortion at the individual level.

Based on mechanism design theory on asymmetric information, this study devises a selectable menu of contracts with price discounts and trade credit rates and proposes strategies for mitigating the risk of payment default when a tourism service provider trades with unfamiliar tour operators. Tourism service providers are suggested to remain conservative under a positive economic condition and progressive under a moderate economic condition when engaging in a new business opportunity. However, a fringe level of progressivism may be maintained under a moderate economy and a low-risk environment. The net benefit of tourism service providers increases when the bank rate is high, and the profit of the entire tourism channel increases when the percentage or ability to pay reliable agents is high.

This study developed an interactive computer game-based visual perception learning system for special education children with developmental delay. To investigate whether perceived interactivity affects continued use of the system, this study developed a theoretical model of the process in which learners decide whether to continue using an interactive computer game-based visual perception learning system. The technology acceptance model, which considers perceived ease of use, perceived usefulness, and perceived playfulness, was extended by integrating perceived interaction (i.e., learner-instructor interaction and learner-system interaction) and then analyzing the effects of these perceptions on satisfaction and continued use. Data were collected from 150 participants (rehabilitation therapists, medical paraprofessionals, and parents of children with developmental delay) recruited from a single medical center in Taiwan. Structural equation modeling and partial-least-squares techniques were used to evaluate relationships within the model. The modeling results indicated that both perceived ease of use and perceived usefulness were positively associated with both learner-instructor interaction and learner-system interaction. However, perceived playfulness only had a positive association with learner-system interaction and not with learner-instructor interaction. Moreover, satisfaction was positively affected by perceived ease of use, perceived usefulness, and perceived playfulness. Thus, satisfaction positively affects continued use of the system. The data obtained by this study can be applied by researchers, designers of computer game-based learning systems, special education workers, and medical professionals.

This study examines the effect of market uncertainty and consumer rationality on product strategy when a company evaluates its entry into the green market. The risk in launching a green product is high because consumers may not be as environmentally conscious as they claim to be. This study develops a composite condition consisting of preference uncertainty, loss aversion, investment cost, and competition intensity to guide companies to react either conservatively or aggressively. An upgraded non-differentiation strategy is suggested for heterogeneous markets, loss-averse consumers, or high-quality reference when the indicator falls within the greenness range. Unlike conventional competitive analysis for non-green products, differentiation may not always be the best option to benefit the entire society and non-differentiation to green is favorable in the context of our analysis.

The concept of customer participation has emerged as an important factor for the success of service firms. Customers have become increasingly empowered in choosing the extent of a service they want to produce for themselves; consequently, service firms may find that they have no choice but to embrace customer participation in organising their service delivery. However, the number of studies related to customer participation in high-contact services remains considerably limited. This study develops an integrated model of customer participation management and is empirically tested in a real estate agency. The research model has four variables, including customer participation, customer satisfaction, customer–company identification, and customer loyalty. A survey questionnaire was employed to collect data for testing the hypotheses contained in the proposed model. Through structural equation modelling analysis, the results indicate that customer participation can be both a driver and a moderator for managing...

Travel agencies are high-contact service organizations and customer orientation plays a critical role in their success. However, not all the academic research on the effect of customer orientation on service performance has been positive. This research examines the effect of customer orientation on customer loyalty through two mediators: customer satisfaction and customer-company identification. The study also investigates how customer participation moderates the relationships between two mediators and customer loyalty. The findings are as follows: (1) customer orientation has significantly positive impacts on customer satisfaction and customer-company identification as well as on customer loyalty; (2) two mediators have significantly positive impacts on customer loyalty; and (3) customer participation moderates the relationships between the two mediators and customer loyalty. The research results indicate that customer-company identification can be used as an additional way to enhance customer lo...

This study investigates the business impacts and managerial insights of an exchange program of reusable assets by appreciate cost distribution schemes. This study constructs a competitive market model, where the member companies make their decisions in joining the platform and providing exchange quantities according to their profitability optimization. In the process of exchange, eco-efficiency is then achieved. The classical Owen core or Shapley core usually consider distribution scheme solely on a single dimension, such as benefit, and may not inhere other properties on fairness. Existing studies on coalition distribution mainly focus on financial utility without referring to environmental contribution. However, retaining the notion of fairness is essential in the design of distribution mechanism. This study derives a novel Fenchel core and overcomes drawbacks of Owen core. We prove that Fenchel core is fair in the second degree. The developed market model allows each member to provide and use assets in the exchange platform. The consequent analysis based on the developed core is suitable for exchange mechanism and conveys abundant managerial insights for operations management. Through our proposed distribution scheme, asset providers and takers can be compensated fairly and therefore the exchange platform can be sustained.