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The purpose of this paper is to argue that comparative advantage of host country’s industry can be one of the significant determinants of the decision on mergers and acquisitions (M&A) or greenfield in foreign direct investment (FDI).

The authors extract five-related properties of an industry with comparative advantage in a host nation from Bernard et al.’s (2007) international trade model with heterogeneous firms and attempt to empirically test their roles in a multinational enterprise’s (MNE) M&A or greenfield investment decision, using the inward FDI data set in Korea from 1999 to 2006.

The theoretical framework finds that the five properties derived from an industry with comparative advantage in a host country have mixed motives for M&A or greenfield. The empirical results show that selected conventional independent variables generally affect the M&A or greenfield entry mode decision with significance individually and that their impacts become more or less prominent when the authors employ interaction terms combining them with comparative advantages in the industries.

This implies that MNEs not only consider their own firm-specific advantages or other country-level factors for foreign market entries as the previous research generally found, but also seriously take into account industry-specific factors, especially industry-wide comparative advantages based on heterogeneous productivities of firms.

This paper reconciles multinationals’ strategic motives under an oligopolistic market with their efficiency gains under a monopolistic competitive market, which are considered as two main factors for cross-border M&A. Furthermore, this paper adds a new firm-level data set into entry mode research.

Successful bridge management system (BMS) development requires a reliable bridge deterioration model, which is the most crucial component in a BMS. Historical condition ratings obtained from biennial bridge inspections are a major source for predicting future bridge deterioration in BMSs. However, historical condition ratings are very limited in most bridge agencies, thus posing a major barrier for predicting reliable future bridge performance. The purpose of this paper is to present a preliminary study as part of a long‐term research on the development of a reliable bridge deterioration model using advanced Artificial Intelligence (AI) techniques.

This proposed study aims to develop a reliable deterioration model. The development work consists of two major Stages: stage 1 – generating unavailable bridge element condition rating records using the Backward Prediction Model (BPM). This helps to provide sufficient historical deterioration patterns for each element; and stage 2 – predicting long‐term condition ratings based on the outcome of Stage 1 using time delay neural networks (TDNNs).

Long‐term prediction using proposed method can also be expressed in the same form of inspection records – element quantities of each bridge element can be predicted. The proposed AI‐based deterioration model does not ignore critical failure risks in small number of bridge elements in low condition states (CSs). This implies that the risk in long‐term predictions can be reduced.

The proposed methodology aims to utilise limited bridge inspection records over a short period to predict large datasets spanning over a much longer time period for a reliable, accurate and efficient long‐term bridge deterioration model. Typical uncertainty, due to the limitation of overall condition rating (OCR) method, can be minimised in long‐term predictions using limited inspection records.

Organizational cultures that facilitate collaboration are valuable, but little is known about how to create them. The authors investigate the microfoundations of this problem using computational models of dyadic coupled learning. The authors find that merely altering initial beliefs about the consequence of actions (without altering the consequences themselves) can under some conditions create cultures that promote collaboration. The results of this study show why the right initial “framing” of a situation – established for instance through persuasive rhetoric, an inspiring vision, or careful recruitment choices – may under the right conditions be self-reinforcing, instead of becoming empty symbolism.

In the past few years, numerous economic uncertainty challenges have occurred globally. These uncertainties grasp the attention of the researchers and they examine the role of economic policy uncertainties in several aspects. Therefore, this study contributes to the literature by exploring the house prices volatility and economic policy uncertainty nexus in G7 countries.

The authors applied the newly introduced econometric technique, the GARCH-MIDAS model, to the sample size of January 1998–May 2021.

The result shows a significant relationship between house prices volatility and economic policy uncertainty. Moreover, economic policy uncertainty acts as a significant determinant of house prices volatility. In addition, the out-of-sample also shows that the economic policy uncertainty is an effective predictor and the GARCH-MIDAS has a better predictive ability.

This paper makes a unique contribution to the literature with reference to developed economies, being a pioneering attempt to investigate the GARCH-MIDAS model to analyze the relationship between housing prices volatility and economic policy uncertainty by applying more rigorous and advanced econometric techniques.

The purpose of this paper is to present a three‐dimensional CFD model that simulates the pyrolysis, combustion and heat transfer phenomena in a refuse‐derived fuel (RDF) gasifier. Correlations between different operation conditions and the waste stack morphology are also investigated. Parametric studies are conducted to optimize operating conditions to achieve an even stack surface minimal the local oxidation in the waste stack.

This paper proposes a Lagrangian pyrolysis submodel which can be applied to determine the local pyrolysis rate and porosity field by introducing the local characteristic diameter of the waste solid sphere. The flow field is described by a single‐phase porous flow model using the SIMPLE algorithm with momentum extrapolation. A one‐step global reaction was adapted for the chemical reactions inside the gasifier.

Computational results produced three‐dimensional distribution of the flow field, temperature, species concentration, porosity and the morphology of the waste stack under different operation conditions. Some parametric studies were conducted to assess the effects of the inlet temperature and the feeding rate on the waste stack shape. The results demonstrated that the model can properly capture the essential physical and chemical processes in the gasifier and thus can be used as a predictive simulation tool.

Due to the lack of accurate reaction rate information, the computational results have not been directly compared against experimental data. Additional refinement and subsequent validation against prototype gasifier experiment will be reported in future work.

A full three‐dimensional computational model is developed for the complex two‐phase flow based on porous medium representation of the solid stack. A Lagrangian pyrolysis model based on the characteristic diameter of the solid waste material was proposed to describe the pyrolysis rate history. The developed model reproduces correct physical and chemical behavior inside gasifier with adequate computational efficiency and accuracy.

An open question of behavioral pricing literature is: What are the factors which influence consumers’ judgments of acquisition value and transaction value? An important framework to explain consumers’ shopping and purchase decisions is their decision-making styles. This paper aims to examine the influence of consumers’ decision-making styles, that is, perfectionistic high-quality conscious, brand conscious-price equals quality, novelty-fashion conscious, recreational-hedonistic, price conscious-value for money, impulsive-careless, habitual-brand loyal and confused by overchoice on their judgments of acquisition value and transaction value.

From the literature, a conceptual framework was formulated. Data was collected from a survey of 304 respondents. The measurement model was tested using exploratory factor analysis and confirmatory factor analysis. The structural model was tested using structural equation modeling.

The consumers’ judgments of acquisition value and transaction value vary with their decision-making styles. The measurement and structural models exhibited good fit, and 12 of the 16 proposed hypotheses were found to be significant.

The respondents for this research study were urban and postgraduate students.

The results of this study can help managers personalize their promotional offers and market offerings targeted at consumers with different decision-making styles.

Behavioral pricing literature has not convincingly shown that consumers make the judgments of the two values, acquisition value and transaction value, in a purchase scenario. There is limited literature on the impact of decision-making styles on the marketing variables. The results of this study contribute to the literature by showing that consumers make the judgments of these two values, and these judgments vary with their decision-making styles. Also, this is one of only a few studies to examine the two components of the purchase value in an Indian context.