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A popular production model for high-tech manufacturers is that they move most production lines abroad to produce formal products for sale and just keep a few production lines in headquarters to manufacture sample products for new product development. Under such a production model, the paper aims to develop a selection model of International Air Express (IAE) for high-tech manufacturers in airfreight of sample products using the fuzzy best-worst method (BWM).

In this paper, an assessment model based on the fuzzy BWM approach is proposed for high-tech manufacturers in selecting airfreight carriers for the shipping of sample products. Further, one high-tech electronic manufacturer in Taiwan was empirically investigated to validate the assessment model.

The result indicates that electronics manufacturer pays more attention to Promptness, Mutual trust, Freight rate and Financial status of fixed assets when selecting IAEs. Besides, FedEx is argued to be the most preferred IAE for the transportation of sample products. Based on the findings, some practical management implications were discussed.

Some literature limitations should be addressed. Initially, the adoption of the fuzzy BWM assumes independence among criteria. Nonetheless, this assumption is not yet to confirm in this study. Accordingly, this limitation leaves room for improvement in future studies. Further, in this paper, five experienced experts from the Radiant Opto-Electronics Corporation (ROEC) case were empirically surveyed. To ensure the validity of the surveying, this paper adopted an interviewing survey instead of a traditional mailed survey. However, more representative samples are still necessary to confirm the empirical results in future research.

Firstly, the proposed research model provides a systematic framework to the decision-making process, which assists high-tech manufacturers in identifying the most suitable IAEs based on multiple criteria. It has been illustrated that high-tech companies deliver their sample products requiring timely and secure means of transport. In practice, manufacturers can assess various IAEs considering some main factors, such as Operational Flexibility (OF), Partner Relationship (PR), Transportation Capability (TC) and Management, using fuzzy BWM. This process ensures the selection of IAEs aligning with their logistical needs and business priorities, ultimately enhancing operational efficiency and customer satisfaction. Secondly, empirical results from the ROEC case indicate that electronics manufacturer pays more attention to Promptness, Mutual trust, Freight rate and Financial status of fixed assets when selecting IAEs. Besides, FedEx is argued to be the most preferred IAE for transportation of sample products. In other words, ROEC should consider establishing long-term contracts with preferred IAEs (i.e. FedEx) to secure favorable rates and service commitments. On top of that, results not only provide practical information for manufacturers in selecting IAEs but also for IAE partners to improve their service policies.

The results not only provide practical information for high-tech manufacturers in selecting airfreight carriers but also for the airfreight carriers to improve their service quality.

This study explores whether a new machine learning method can more accurately predict the movement of stock prices.

This study presents a novel hybrid deep learning model, Residual-CNN-Seq2Seq (RCSNet), to predict the trend of stock price movement. RCSNet integrates the autoregressive integrated moving average (ARIMA) model, convolutional neural network (CNN) and the sequence-to-sequence (Seq2Seq) long–short-term memory (LSTM) model.

The hybrid model is able to forecast both linear and non-linear time-series component of stock dataset. CNN and Seq2Seq LSTMs can be effectively combined for dynamic modeling of short- and long-term-dependent patterns in non-linear time series forecast. Experimental results show that the proposed model outperforms baseline models on S&P 500 index stock dataset from January 2000 to August 2016.

This study develops the RCSNet hybrid model to tackle the challenge by combining both linear and non-linear models. New evidence has been obtained in predicting the movement of stock market prices.

Malnutrition is widespread and affects about one-third of humanity. Increasing production and consumption of vegetables is an obvious pathway to improve dietary diversity, nutrition and health. This chapter analyses how climate change is affecting vegetable production, with a special focus on the spread of insect pests and diseases. A thorough literature review was undertaken to assess current global vegetable production, the factors that affect the spread of diseases and insect pests, the implications caused by climate change, and how some of these constraints can be overcome. This study found that climate change combined with globalization, increased human mobility, and pathogen and vector evolution has increased the spread of invasive plant pathogens and other species with high fertility and dispersal. The ability to transfer genes from wild relatives into cultivated elite varieties accelerates the development of novel vegetable varieties. World Vegetable Center breeders have embarked on breeding for multiple disease resistance against a few important pathogens of global relevance and with large evolutionary potential, such as chili anthracnose and tomato bacterial wilt. The practical implications of this are that agronomic practices that enhance microbial diversity may suppress emerging plant pathogens through biological control. Grafting can effectively control soil-borne diseases and overcome abiotic stress. Biopesticides and natural enemies either alone or in combination can play a significant role in sustainable pathogen and insect pest management in vegetable production system. This chapter highlights the importance of integrated disease and pest management and the use of diverse production systems for enhanced resilience and sustainability of highly vulnerable, uniform cropping systems.

Outlines previous research on company failure prediction and discusses some of the methodological issues involved. Extends an earlier study (Tan 1997) using artificial neural networks (ANN) to predict financial distress in Australian credit unions by extending the forecast period of the models, presents the results and compares them with probit model results. Finds the ANN models generally at least as good as the probit, although both types improved their accuracy rates (for Type I and Type II errors) when early warning signals were included. Believes ANN “is a promising technique” although more research is required, and suggests some avenues for this.

Online social networks are increasingly important venues for businesses to promote their products and image. However, information propagation in online social networks is significantly more complicated compared to traditional transmission media such as newspaper, radio, and television. In this chapter, we will discuss research on modeling and forecasting diffusion of virally marketed content in social networks. Important aspects include the content and its presentation, the network topology, and transmission dynamics. Theoretical models, algorithms, and case studies of viral marketing will be explored.

This study seeks to observe the association between mobile banking failures, use of m-banking and customer engagement to determine the contribution of user satisfaction towards m-banking as mediator between the aforementioned relationship.

This study proposes a Mobile Banking Failure Model (MBFM) by integrating four failure dimensions (functional, system, information and service) based on Tan's failure model and DeLone and Mclean's Information Success model. In this paper, data was gathered from 338 respondents, who were the customers of banks and regular users of m-banking services of their respective banks in India. A survey method was employed to collect data. Structure equation modelling (SEM) was used to analyse the collected data.

The results suggest that all m-banking failure dimensions (functional, system, information and service) affect the use of m-banking, which in turn affects user satisfaction towards m-banking and customer engagement. Additionally, this study found that user satisfaction towards m-banking acts as a partial mediator between the use of m-banking and customer engagement.

The banking failure and its use by customers have been examined in the context of mobile banking in India only and thereby limits the generalization of results to other industry and country contexts.

The results of this paper will guide bank managers and policy planners in implementing MBFM in the Indian banking context, specifically for their m-banking apps.

The use of m-banking, user satisfaction towards m-banking and customer engagement have been added as three supportive variables to the basic Tan's failure model and DeLone and Mclean's Information Success model to examine the impact of m-banking failure on bank customers' usage behaviour. This is a novel addition to the extant literature, as most empirical works in this domain are from industries other than banking (specifically m-banking) and with differing contexts.

The purpose of this paper is to develop a method for solving the multiple attribute decision-making (MADM) problem in which the decision maker can provide the five types of attribute aspirations, namely: benefit type with requirements; cost type with requirements; interval type; benefit type; and cost type.

First, for each type of attribute aspiration, the calculation formula of utility values of alternative concerning attributes is given. Then, using the calculation formulae, the attribute values are transformed into the corresponding utility values. On the basis of this, the overall ranking value of each alternative is calculated. Further, a ranking order of alternatives can be determined according to the obtained overall ranking values.

Research shows that it is necessary to develop the method for MADM with attribute aspirations. The example shows that the proposed method is applicable.

The proposed method can be applied to the selection of wastewater treatment technologies or other areas.

This paper proposes a new MADM method with multiple types of attribute aspirations. It develops and enriches the existing MADM methods.

The purpose of this paper is to extend the unified theory of acceptance and use of technology with psychological constructs, namely, perceived playfulness, mobile innovativeness in information technology and perceived expressiveness (PEX) to examine on the consumers’ intention to adopt mobile applications (m-apps) as another mean in purchasing tourism-related product and services via their mobile devices. The study also included gender as moderating variable.

Partial least square-structural equation modeling approach was adopted to test the research framework using 474 valid respondents.

The results demonstrated that only performance expectancy and PEX are non-significant with the intention to adopt. In addition, gender was found to have a moderating effect between social influence and intention.

The study has several useful implications for researchers, m-apps developers, travel-related organizations and even government agencies when rendering m-apps services and disseminating information to their potential consumers.

The study contributes to the growing literature on m-apps in tourism from a developing country’s perspectives.