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The invention and use of mobile commerce (m-commerce) technology have progressed in recent years. In small to medium-sized enterprises (SMEs), the decision to implement business-to-business m-commerce is a multiple-criteria decision analysis problem that involves both qualitative and quantitative factors, and its evaluation may be based on imprecise information or uncertain data. Furthermore, there may be significant dependences or important feedback among different levels of criteria or alternatives. However, most conventional decision models cannot capture these complex interrelationships. The purpose of this paper is to adopt a new hybrid multiple attribute decision model (MADM) model to evaluate the implementation of business-to-business m-commerce by SMEs in Taiwan to enhance the thinking of management and improve decision gaps.

In this study, the authors present the use of a new hybrid MADM combined with a decision-making trial and evaluation laboratory (DEMATEL) method to construct an influential network relationship map (INRM) and find the influential weights of DANP (DEMATEL-based ANP) in criteria from the influential relationship matrix, and the modified VIKOR method using influential weights to evaluate and integrate the criteria performance in the gaps and to analyze how to reduce the gaps to evaluate the decision to implement business-to-business m-commerce by SMEs based on INRM.

The findings of using the new hybrid MADM model can efficiently and successfully be applied to the business-to-business m-commerce of SMEs to improve and reduce the performance gaps among each criterion/aspect when setting improvement strategies for achieving an aspiration level based on INRM.

This study achieves the following: successfully building a decision network (called an influential network relation map (INRM)) of the technological advances and implementations of business-to-business m-commerce for solving the inter-dependence and feedback of criteria/aspects in the real world problem; how changing effective solutions from existing performance of situations/alternatives into aspiration levels to fit the current competitive markets in the business-to-business m-commerce of SMEs; and efficiently shifting from ranking and selection when determining the most preferable approaches to performance improvement by modified VIKOR methods.

This study aims to propose a hybrid technology evaluation process integrating the Decision Making Trial and Evaluation Laboratory (DEMATEL) technique and the analytic network process (ANP) with novel MCDM method for the organic light emitting diode (OLED) technology evaluation. The DEMATEL is used to detect and build the complex network relationship map (NRM) among dimensions/criteria. The ANP is employed to conduct the dependence and feedback among criteria and to decide the relative weights of the criteria by super‐matrix. The combination of DEMATEL technique and ANP with novel MCDM method is used to perform for constructing a technology evaluation structure.

This study proposes a hybrid approach integrating the DEMATEL and the ANP to construct a technology evaluation and decision‐making model for OLED. Taiwan is a research base in this study. This is for the purpose of offering an example as to how a technology firm or even a country can evaluate or position certain emerging technology in the process of resource investment decision‐making.

This study suggests a hybrid technology evaluation approach integrating the DEMATEL technique and the ANP approach. When policy makers and R&D planners design R&D programs in emerging technology fields, the authors' proposed model can help to assess whether it is worth investing in the expected technology or not, for all concerned. Besides, using Taiwan's OLED as an example, this study has generated a conclusion comprising several strategic suggestions and managerial implications.

The whole approach developed by this study can serve as a reference to construct an evaluation framework to evaluate other technology fields in order to propose some guidance for R&D planning or strategy making in the industry applied the aimed technology. The weights distributed to the prospects and the problems can also serve as a reference of whether the R&D investment in the new technology is worthwhile for all concerned. Future research can adopt the approach proposed in this study to evaluate the worth of R&D investment in other targeted technologies.

Concerns the logistics system of the Taiwan power company, Taipower, and the problem it faces of coal allocation and fleet deployment of dedicated coal carriers. The coal allocation problem can be viewed as a multi‐index transportation problem requiring the formulation of a coal allocation model. Owing to the high uncertainty of shipping operations, formulates a dynamic fleet assignment model to dispatch vessels and maximize ship usage. An integrated coal allocation and fleet assignment decision support system based on these models was developed to assist the decision maker. It not only guides Taipower marine operations management in annual schedule planning, but also assists operating personnel to allocate vessels to the schedules and to make rapid adjustments. Demonstrates the potential for substantial cost improvement in Taipower coal trade.

Few library studies have investigated recommendation classifications for e‐book (electronic book) usage, while none have directly compared what recommendation sources (word‐of‐mouth, advertising, and expert recommendation) might influence e‐book usage intentions. To fill this gap in the literature, the main purposes of this study are to: examine how users perceive the influence of recommendations on the intention to use e‐books for academic purposes; and to measure the level of the perception of trust and perceived risk when users receive e‐book recommendations from peers, advertisers, and experts.

Data for this study were collected from 382 academic digital library users between the ages of 18 and 25. A multiple regression analysis was then conducted to identify the key causal relationships.

The comparison of three recommendation sources (word‐of‐mouth, advertising, and expert recommendations) revealed that word‐of‐mouth (WOM) played a more important role than other recommendations in determining the intention to use e‐books in an academic digital library. In addition, enhancing the perceived trust and reducing the risk towards the use of e‐books can mediate the relationship between recommendation sources and the behavioural intentions to use e‐books.

This study assessed self‐reported behavioural intention as part of its survey and, as a result, could have introduced unintentional inaccuracies.

Librarians should emphasise e‐book advantages (e.g. easy searching, easily accessible index) to get positive recommendation if users follow all of the recommendations of the source. They can also create online discussion forums to provide usage intention discussions, which can influence users' perceptions of trust and risk and increase the willingness of potential users to read e‐books.

Little has been written on the intentions of using e‐books. Therefore, this conceptual model is novel. This model is also useful in explaining how recommendations stimulate the intentions of using e‐books by enhancing the perceived trust and reducing the perceived risk; these findings may generally be applicable to librarians, current users, and potential users.

The purpose of this paper is to apply social network analysis (SNA) to study faculty research productivity, to identify key leaders, to study publication keywords and research areas and to visualize international collaboration patterns and analyze collaboration research fields from all Management Information System (MIS) departments in Taiwan from 1982 to 2015.

The authors first retrieved results encompassing about 1,766 MIS professors and their publication records between 1982 and 2015 from the Ministry of Science and Technology of Taiwan (MOST) website. Next, the authors merged these publication records with the records obtained from the Web of Science, Google Scholar, IEEE Xplore, ScienceDirect, Airiti Library and Springer Link databases. The authors further applied six network centrality equations, leadership index, exponential weighted moving average (EWMA), contribution value and k-means clustering algorithms to analyze the collaboration patterns, research productivity and publication patterns. Finally, the authors applied D3.js to visualize the faculty members' international collaborations from all MIS departments in Taiwan.

The authors have first identified important scholars or leaders in the network. The authors also see that most MIS scholars in Taiwan tend to publish their papers in the journals such as Decision Support Systems and Information and Management. The authors have further figured out the significant scholars who have actively collaborated with academics in other countries. Furthermore, the authors have recognized the universities that have frequent collaboration with other international universities. The United States, China, Canada and the United Kingdom are the countries that have the highest numbers of collaborations with Taiwanese academics. Lastly, the keywords model, system and algorithm were the most common terms used in recent years.

This study applied SNA to visualize international research collaboration patterns and has revealed some salient characteristics of international cooperation trends and patterns, leadership networks and influences and research productivity for faculty in Information Management departments in Taiwan from 1982 to 2015. In addition, the authors have discovered the most common keywords used in recent years.

The purpose of this paper is to consider compromise solutions of multiple attribute decision-making methods (TOPSIS, VIKOR, and similarity-based approach) in order to evaluate and rank mutual funds and to compare the capabilities of different approaches based on the different traditional indices of mutual funds assessment. In addition, a new algorithm for ranking mutual funds was proposed subsequently.

In this research, three groups of indices including general, risk-modified performance evaluation, and risk-modified performance evaluation indices using semivariance were used in the mutual funds assessment, which led to the comparison between selected mutual funds, using three mentioned methods and three different groups of criteria. The results of this comparison were compiled and synthesized with linear assignment method. At the end, an algorithm for decision making and investing in mutual funds for professional and unprofessional investors was proposed.

Using different methods and different criteria proved that the results of similarity-based approach as a MADM technique have the ability to rank and evaluate mutual funds regardless of the criteria used compared to TOPSIS and VIKOR. Furthermore, the authors propose the algorithm of this research as a new model of mutual funds evaluation which considers a wide range of variables with respect to amateur and professional points of view.

The originality of this paper is threefold: first, different criteria were considered to make the evaluation more comprehensive. Second, four different approaches were used to make the results more authentic. Third, a holistic algorithm with its implication was proposed.

The feasibility and desirability of reverse logistics in market-motivated contexts are examined in China. Interactions between the major barriers, that hinder or prevent the application of reverse logistics in China are analyzed. Management’s key task is to diagnose barriers to the application of reverse logistics that could be crucial to the organization’s future survival. Simultaneity, a value delivery system exists to create value for customers and environments by supplying needed products and services. Value delivery systems are at the heart of every firm and, more than anything else, determine that, whether the firm survives in the marketplace or disappears into bankruptcy or takeover. The processes and model of market-motivated reverse logistics value delivery system are discussed, and the processes content and model are presented. Simultaneity, based on the advantage of the Third Party Reverse Logistics Providers (3PRLs) and Outsourced Service Providers, an integrated evaluation model is built to select 3PRLs by using the integrated decision-making methods. Reflecting the comprehensive information requirement, the Analytic Hierarchy Process and entropy approaches are applied to calculate the objective weights. A new kind of relative similarity degree is established by combining the Euclidean distance with the grey correlation degree. An example demonstrates the model’s efficiency.

Forecasting tourism demand accurately can help private and public sector formulate strategic planning. Combining forecasting is feasible to improving the forecasting accuracy. This paper aims to apply multiple attribute decision-making (MADM) methods to develop new combination forecasting methods.

Grey relational analysis (GRA) is applied to assess weights for individual constituents, and the Choquet fuzzy integral is employed to nonlinearly synthesize individual forecasts from single grey models, which are not required to follow any statistical property, into a composite forecast.

The empirical results indicate that the proposed method shows the superiority in mean accuracy over the other combination methods considered.

For tourism practitioners who have no experience of using grey prediction, the proposed methods can help them avoid the risk of forecasting failure arising from wrong selection of one single grey model. The experimental results demonstrated the high applicability of the proposed nonadditive combination method for tourism demand forecasting.

By treating both weight assessment and forecast combination as MADM problems in the tourism context, this research investigates the incorporation of MADM methods into combination forecasting by developing weighting schemes with GRA and nonadditive forecast combination with the fuzzy integral.

Given that the use of Google Trends data is helpful to improve forecasting performance, this study aims to investigate whether the precision of forecast combination can benefit from the use of Google Trends Web search index along with the encompassing set.

Grey prediction models generate single-model forecasts, while Google Trends index serves as an explanatory variable for multivariate models. Then, three combination sets, including sets of univariate models (CUGM), all constituents (CAGM) and constituents that survive the forecast encompassing tests (CSET), are generated. Finally, commonly used combination methods combine the individual forecasts for each combination set.

The tourism volumes of four frequently searched-for cities in Taiwan are used to evaluate the accuracy of three combination sets. The encompassing tests show that multivariate grey models play a role to be reckoned with in forecast combinations. Furthermore, the empirical results indicate the usefulness of Google Trends index and encompassing tests for linear combination methods because linear combination methods coupled with CSET outperformed that coupled with CAGM and CUGM.

With Google Trends Web search index, the tourism sector may benefit from the use of linear combinations of constituents that survive encompassing tests to formulate business strategies for tourist destinations. A good forecasting practice by estimating ex ante forecasts post-COVID-19 can be further provided by scenario forecasting.

To improve the accuracy of combination forecasting, this research verifies the correlation between Google Trends index and combined forecasts in tourism along with encompassing tests.

過去的研究顯示 Google 搜尋趨勢資料有助於改善旅遊需求預測的準確度，本研究就此進一步探討 Google 搜尋趨勢網頁搜尋指標與涵蓋性檢定的使用對於組合預測準確度所造成的影響。

本研究以 Google 搜尋趨勢指標做為多變量灰色預測模式的解釋變數，並以單變量與多變量灰色模式產生各別預測值。在分別產生由所有的單變量模式 (CUGM)所有的模式 (CAGM), 以及經過涵蓋性檢定所留存下來之模式 (CSET) 所組成之集合後，就各別的組合集以常用的組合方法產生預測值。

以台灣的四個熱搜旅遊城市的旅遊人數進行三個組合集的預測準確度分析。涵蓋性檢定顯示多變量灰色模式在組合預測中扮演重要的角色，而結果亦呈現線性組合方法在 CSET優於在 CUGM 與 CAGM 的準確度，突顯搜尋趨勢指標與涵蓋性檢定對於線性組合方法的有用性。

藉由 Google 搜尋趨勢網頁搜尋指標與涵蓋性檢定，旅遊部門應可透過線性組合方法的預測規劃旅遊目的地的經營策略。新冠疫情下於各季的事前預測亦可結合情境預測具體呈現。

為提升組合預測在旅遊需求的預測準確度，本研究結合涵蓋性檢定以分析 Google 搜尋趨勢指標與組合預測準確度之間的關聯性。

旅遊需求,涵蓋性檢定,Google 搜尋趨勢,灰色預測,組合預測

研究型论文

Dado que el uso de los datos de Google Trends es útil para mejorar la precisión de las predicciones, este estudio examina si el uso del índice de búsqueda web de Google Trends combinado con la agregación de relevancia puede mejorar la precisión del predictor.

El modelo predictivo gris genera predicciones bajo un único modelo, mientras que el modelomultivariado utiliza el indicador Google Trends como variable explicativa. Se generaron tresensamblajes generales, incluido el Modelo armónico único (CUGM), los ensamblajes de todos loscomponentes (CAGM) y la prueba de presencia de componentes con predicción (CSET). Laspredicciones individuales encada grupo luego se combinan utilizando métodos de correlación deuso común.

Utilizando el número de turistas en las cuatro ciudades más investigadas de Taiwán, los tresgrupos combinados se clasificaron según su precisión. Las pruebas incluidas muestran que losmodelos multivariados en escala de grises son importantes para la predicción. Además, losresultados de las pruebas muestran que el índice de Google Trends y las pruebas que incluyenmétodos de suma lineal son útiles porque los métodos combinados con CSET funcionan majorque los métodos combinados con CSET. CAGM y VCUG.

La industria de viajes puede usar el índice de búsqueda web de Google Trends para desarrollarestrategias comerciales para atracciones basadas en un conjunto lineal de componentes.

Con el objetivo de mejorar la precisión de los pronósticos agregados, este estudio investiga larelación entre el índice de tendencias de Google y las expectativas generales de viaje junto con laevidencia global.

Demanda de viajes, Experiencia global, Tendencias de Google, Predicción gris

Trabajo de investigación

This paper first aims to examine how business process change decisions (BPCDs) were implemented in a government organisation bound by tightly coupled temporal constraints (TTCs). Second, it focuses on how to achieve optimal and efficient BPCDs that require tight compliance with regulators’ temporal constraints. Finally, it formulates a rigorous framework that can facilitate the execution of optimal BPCDs with maximum efficiency and minimal effort, time and cost.

Decision-making biases by individuals or groups in organisations can impede optimal BPC implementation; to demonstrate this, a case study is investigated and the formulated framework is applied to tackle these failings.

The case study analysis shows 76 per cent of the BPCDs implemented were inefficient, mostly because of poor decisions, and these resulted in negative ripple effects. In response, the newly developed hierarchical change management structure (HCMS) framework was used to empower organisations to execute high-velocity BPCDs, enabling them to handle any temporal constraints imposed by regulators or other exogenous factors. The HCMS framework was found to be highly effective, scoring an average improvement of more than 100 per cent when measured using decision quality dimensions. This paper would be of value for business executives and strategic decision makers engaging with BPC.

The HCMS framework has been applied in a single case study as a proof of concept. Future research could extend its application to broader domains that have multi-attribute structures and environments. The evaluation processes of the proposed framework are based on subjective metrics. Causal links from the framework to business process metrics will provide a more complete performance picture.

The outcome of this research assists in formulating a systematic BPCD framework that is otherwise unavailable. The practical use of the proposed framework would potentially impact on quality outcomes for organisations. The model is derived from decision trees and analytical hierarchical processes and is tailored to address this problematic area. The proposed HCMS framework would help organisations to execute efficient BPCDs with minimal time, effort and cost. The HCMS framework contributes to the academic literature on BPCD that leverages diverse stakeholders to engage in BPC initiatives.

The research presents a novel framework –HCMS – that provides a platform for organisations to easily determine and solve hierarchical decision structure problems, thereby allowing them to efficiently automate and institutionalise optimal BPCDs.