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This study aims to simultaneously examine customer complaints through the proposed novel Design for Six Sigma (DFSS) model which incorporates of creating the new insurance services to win customers' hearts and mind for the insurance industry.

A novel DFSS research methodology which includes the theory of inventive problem solving (TRIZ), Pugh concept selection, creative product analysis matrix and importance–satisfaction model (I–S Model) was proposed. In addition, a real insurance company case was studied to illustrate the effectiveness of the proposed DFSS model.

The results of a novel DFSS model not only can establish new services, but also can dramatically reduce the cost of resolving customer complaints.

The findings of this study are useful for insurance companies and other related service providers in devising tailored strategies to offer quality and suitable services to their customers.

This study addresses the paucity of research and marketing gaps through the proposed novel DFSS model for the first time in the insurance industry. These study findings would enable researchers and practitioners to formulate strategies for solving customer complaints effectively and develop new services from time to time.

Greece, Ireland, Portugal and Spain have been in the spotlight of the recent economic crisis in Europe. With their economy strongly reliant on the construction industry, these countries have become widely exposed to the downturn in the property sector. This paper aims to examine residential property bubble dynamics in the period from 2003 to 2014 and investigate the role of financing conditions in the formation of these bubbles.

Building on the present value model in conjunction with the rational bubble assumption, the study applies the discounted cash flow (DCF) approach and applies weighted average cost of capital (WACC) to capture real estate bubble dynamics in the four countries. Reduced form vector autoregression models are used to examine the relationship between financing conditions and the bubble indicator.

The bubble indicator suggests that Spain and Ireland experienced a large rise in the bubble relative to moderate increases in Portugal and Greece in the period from 2003 up to the collapse in 2008. Our findings from the empirical analysis indicate that central bank policy shifts that impact interest rates and lending volumes on the domestic level have a significant and leading effect on the formation of residential property bubbles.

Only little research on real estate bubbles takes financial leverage into account. This paper bridges this gap by applying the WACC in the DCF model to identify real estate bubbles. While using a distinct bubble indicator, this analysis provides new insights into the linkage between financing conditions and real estate bubbles.

The paper aims to propose a modified multi‐activity data envelopment analysis (MDEA) to provide information on the efficiency performance of farmers' cooperatives with inputs shared among several closely‐related activities.

The directional distance functions are used to construct a non‐radial measure of performance in which the optimal input/output adjustment and the optimal allocation of shared inputs is simultaneously taken into consideration. The model is applied to study the case of 201 farmers' cooperatives in Taiwan.

The empirical results suggest that there exist significant discrepancy in terms of the performance among the four departments of the farmers' cooperatives. Furthermore, the wide discrepancy in the returns to scale warrant further deregulations by easing restrictions on their consolidation with other cooperatives to operate over broader geographical areas.

An empirical study on Taiwan's farmers' cooperatives is used to demonstrate its applicability and how they can effectively allocate their fixed resources in a multi‐activity environment. Such a measure can be used for rewarding the individual groups of an organization based on their relative contributions to the overall performance.

Exploratory learning is regarded as an important ability for developing knowledge from open environments. During the exploration, learners not only need to acquire new information based on their current interests, but also they need to form new perspectives by incorporating new knowledge into their previous knowledge. This paper seeks to address these issues.

To this end, this paper proposes an approach that includes a concept association bank to recommend related concepts in a domain based on the goal of an exploration. By doing so, learners' knowledge can be expanded beyond their current understanding. An experiment was conducted to investigate how the proposed approach facilitated the learners' exploration.

The results indicated that the concept association bank is a useful mechanism to help learners gain new understanding, including providing exploration directions, reducing complexity and cognitive load, facilitating data‐ and goal‐driven exploration strategies, and commenting on new understanding. The implications of these results are discussed.

Current recommendation systems emphasise a data‐driven strategy, which seeks isolated pieces of information, instead of suggesting directions related to their exploration goal. The problem with such an approach is that learners' exploration will be limited by their existing knowledge. Thus, this paper presents an approach to support both data‐ and goal‐driven strategies.

The purpose of this paper is to assess the degree of tariff escalation in Taiwan's agriculture-related commodities and the economy-wide impact of tariff harmonization.

A computable general equilibrium model of the Taiwan economy is applied to simulate for the economy-wide impact of three alternative proposals that reduce tariff rates as well as the degree of tariff escalation in agriculture-related products.

The paper shows that reduction in tariff wedge helps increase social welfare of Taiwan at the expense of some agricultural sectors. Based on the pair-wise comparisons of the three tariff reduction proposals, the scenario where the upstream products have the least reduction would have agricultural sectors fare better than in the other scenarios where more negative impact on output and employment would occur to agricultural sectors.

The paper assesses quantitatively the economy-wide impact of reducing tariff wedges between unprocessed and processed products, which is rarely seen in the literature using a detailed computable general equilibrium model.

This study deals with attenuating the risk of relying on a single export market, which was heightened by the outbreak of the COVID-19 pandemic. It focuses on Taiwanese atemoya (a fruit with short storage life) and the adoption of active controlled atmosphere (CA) containers, a new technology which lengthens storage time for other export markets. This study looks at the financial feasibility of the technology's first ever use in atemoya exports.

Apart from the standard financial assessment tools—like net present value (NPV), internal rate of return (IRR), benefit-cost ratio (BCR) and payback period (PBP)—this study calibrated five different scenarios based on data gathered from relevant market agents including suppliers, exporters, customs brokers and technology developer.

Due to the high profit margin and low investment cost, the use of active CA containers for long-haul exports of this highly perishable fruit is found both technically and financially feasible, despite the generally higher operational cost during the pandemic.

This study looked at three specific export markets: Malaysia, Dubai and Canada. Results here may lack generalizability in other markets, although it is believed that slight deviations would not invalidate the conclusions of this research because short, medium and long distances were all covered therein.

This paper studies the first time that active CA is used for export of atemoyas to expand existing markets.