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It is well known that internal rate of return (IRR) and net present value (NPV) rankings of mutually exclusive investments are sometimes inconsistent. This inconsistency, when it occurs, requires decision makers to choose between the two ranking methods. The purpose of this paper is to deduce sufficient conditions for consistent IRR and NPV investment rankings of mutually exclusive investments.

Deductive reasoning is used to obtain the sufficient conditions required for consistent rankings of mutually exclusive investments.

There are different sufficient conditions (methods) that can be used to resolve inconsistent IRR and NPV rankings. However, the different methods do not necessarily produce the same consistent rankings. In particular, different size adjustment methods and reinvestment rate assumptions can produce different IRR and NPV consistent rankings. This paper suggests the appropriate criteria for selecting a particular method for ranking mutually exclusive investments.

Like all deduced models, the results apply only to the set of assumptions and preconditions adopted in the model. Furthermore, the application is to ranking mutually exclusive investments.

There is probably no other issue in the capital budgeting literature that has generated more attention and debate than the consistency (or lack thereof) between IRR and NPV rankings. This paper summarizes conditions that can be followed to resolve the conflict which should have near universal interest to those working in the capital budging area. This paper offers alternative methods for obtaining consistent IRR and NPV rankings which can be used to improve investment ranking decisions. The particular method used should depend on the decision environment. Guides for choosing the appropriate ranking method are described in the paper.

Significant decisions, projects, and investments are evaluated using either IRR or NPV methods. This paper shows that existing evaluation methods can lead to sub-optimal investment choices and provides an improved framework that facilitates better investment choices. Lacking an understanding of the sufficient conditions for IRR and NPV consistency – means that resource allocations have been made to investments and projects that are not optimal.

To the best of the authors’ knowledge, the results are this paper have not been published nor are they available elsewhere. That said, this paper builds on important earlier work which is carefully cited and credited.

The purpose of this paper is to examine the long‐term post‐acquisition share performance of Malaysian acquiring firms over the period 2000‐2004.

The authors use the event‐type methodology to analyse acquirer returns in relation to target status, method of payment and other firm characteristics, using both univariate and multivariate analyses. In total three performance measures are used to identify the long‐term share performance of acquiring firms: cumulative market‐adjusted abnormal returns, the buy‐and‐hold market‐adjusted and buy‐and‐hold matched‐sample abnormal returns.

The results show the existence of negative abnormal returns to acquirers over two‐ and three‐year periods after acquisition. The study also finds that acquirers of private targets earn negative returns, while acquirers of public targets earn insignificant returns. It is also found that under‐performance is limited to the small size acquirers and to large relative‐size acquisitions. Furthermore, the results indicate that acquirer's long‐term performance is not related to the method of payment and book‐to‐market ratio of the acquirer.

The Malaysian stock market is relatively small compared to the US and UK markets where most previous research has been carried out. The current study allows us to assess the robustness of the models and whether the findings in developed markets may be generalized to the smaller developing markets. This paper contributes to the present body of knowledge by offering evidence of acquirer's post‐acquisition performance from a developing market.

This paper aims to provide a comprehensive analysis of the state of the art in energy efficiency for autonomous mobile robots (AMRs), focusing on energy sources, consumption models, energy-efficient locomotion, hardware energy consumption, optimization in path planning and scheduling methods, and to suggest future research directions.

The systematic literature review (SLR) identified 244 papers for analysis. Research articles published from 2010 onwards were searched in databases including Google Scholar, ScienceDirect and Scopus using keywords and search criteria related to energy and power management in various robotic systems.

The review highlights the following key findings: batteries are the primary energy source for AMRs, with advances in battery management systems enhancing efficiency; hybrid models offer superior accuracy and robustness; locomotion contributes over 50% of a mobile robot’s total energy consumption, emphasizing the need for optimized control methods; factors such as the center of mass impact AMR energy consumption; path planning algorithms and scheduling methods are essential for energy optimization, with algorithm choice depending on specific requirements and constraints.

The review concentrates on wheeled robots, excluding walking ones. Future work should improve consumption models, explore optimization methods, examine artificial intelligence/machine learning roles and assess energy efficiency trade-offs.

This paper provides a comprehensive analysis of energy efficiency in AMRs, highlighting the key findings from the SLR and suggests future research directions for further advancements in this field.

The paper proposed a fast procedure for solving the reliability-based robust design optimization (RBRDO) by modifying the RBRDO formulation and transforming it into a series of RBRDO subproblems. Then for each subproblem, the objective function, constraint function and reliability index are approximated using Taylor series expansion, and their approximate forms depend on the deterministic design vector rather than the random vector and the uncertain estimation in the inner loop of RBRDO can be avoided. In this way, it can greatly reduce the evaluation number of performance function. Lastly, the trust region method is used to manage the above sequential RBRDO subproblems for convergence.

As is known, RBRDO is nested optimization, where the outer loop updates the design vector and the inner loop estimate the uncertainties. When solving the RBRDO, a large evaluation number of performance functions are needed. Aiming at this issue, the paper proposed a fast integrated procedure for solving the RBRDO by reducing the evaluation number for the performance functions. First, it transforms the original RBRDO problem into a series of RBRDO subproblems. In each subproblem, the objective function, constraint function and reliability index caused are approximated using simple explicit functions that solely depend on the deterministic design vector rather than the random vector. In this way, the need for extensive sampling simulation in the inner loop is greatly reduced. As a result, the evaluation number for performance functions is significantly reduced, leading to a substantial reduction in computation cost. The trust region method is then employed to handle the sequential RBRDO subproblems, ensuring convergence to the optimal solutions. Finally, the engineering test and the application are presented to illustrate the effectiveness and efficiency of the proposed methods.

The paper proposes a fast procedure of solving the RBRDO can greatly reduce the evaluation number of performance function within the RBRDO and the computation cost can be saved greatly, which makes it suitable for engineering applications.

The standard deviation of the original objective function of the RBRDO is replaced by the mean and the reliability index of the original objective function, which are further approximated by using Taylor series expansion and their approximate forms depend on the deterministic design vector rather than the random vector. Moreover, the constraint functions are also approximated by using Taylor series expansion. In this way, the uncertainty estimation of the performance functions (i.e. the mean of the objective function, the constraint functions) and the reliability index of the objective function are avoided within the inner loop of the RBRDO.

Three adjustment modes are alternatives for mixed-model assembly lines (MMALs) to improve their production plans according to constantly changing customer requirements. The purpose of this paper is to deal with the decision-making problem between these modes by proposing a novel multi-classification method. This method recommends appropriate adjustment modes for the assembly lines faced with different customer orders through machine learning from historical data.

The decision-making method uses the classification model composed of an input layer, two intermediate layers and an output layer. The input layer describes the assembly line in a knowledge-intensive manner by presenting the impact degrees of production parameters on line performances. The first intermediate layer provides the support vector data description (SVDD) of each adjustment mode through historical data training. The second intermediate layer employs the Dempster–Shafer (D–S) theory to combine the posterior classification possibilities generated from different SVDDs. The output layer gives the adjustment mode with the maximum posterior possibility as the classification result according to Bayesian decision theory.

The proposed method achieves higher classification accuracies than the support vector machine methods and the traditional SVDD method in the numerical test consisting of data sets from the machine-learning repository and the case study of a diesel engine assembly line.

This research recommends appropriate adjustment modes for MMALs in response to customer demand changes. According to the suggested adjustment mode, the managers can improve the line performance more effectively by using the well-designed optimization methods for a specific scope.

The adjustment mode decision belongs to the multi-classification problem featured with limited historical data. Although traditional SVDD methods can solve these problems by providing the posterior possibility of each classification result, they might have poor classification accuracies owing to the conflicts and uncertainties of these possibilities. This paper develops a novel classification model that integrates the SVDD method with the D–S theory. By handling the conflicts and uncertainties appropriately, this model achieves higher classification accuracies than traditional methods.

This research aims to provide an updated meta-analysis of cross-cultural training effectiveness through quantitatively reviewing the links between cross-cultural training and three outcomes: adjustment, cultural intelligence and job performance.

Drawing from 37 independent samples from 1991 through August 2021 involving a total of 7,040 trainees, the author adopt random-effects meta-analyses method to assess the relationships between cross-cultural training and three outcomes and examine potential moderators (i.e. training phase, training method and rating source) that influence these relationships.

The meta-analytical results mainly support our hypotheses. Cross-cultural training -adjustment exhibits a medium and significant positive effect, whereas cross-cultural training is positive significantly related to job performance and cultural intelligence of small effect sizes. Training method and data source significantly moderate the cross-cultural training's impact on cultural intelligence and performance correspondingly.

This study considers longitudinal statistics for measuring cross-cultural training effectiveness, breaking down the inadequacy of cross-sectional measurement and covers sub-dimensions of cross-cultural training outcomes to provide more comprehensive and advanced evidence of cross-cultural training effectiveness for the latest 30 years.

The purpose of this study is to examine the relationship between cultural intelligence (CQ) and cross-cultural adjustment (CCA) using meta-analytic methods. The paper serves a dual purpose as it critically examines the CQ-CCA literature and provides summary effects using meta-analysis to determine how CQ and its facets affect CCA and its three dimensions.

A meta-analysis of 77 studies involving 18,399 participants was conducted to obtain the summary effects. The studies reporting the relationship of CQ and/or its facets with CCA or any of its dimensions were included in the analysis.

Results revealed that CQ (overall) and all individual CQs were positively and significantly related to CCA and its three subdimensions. Although CQ (overall) had a strong effect on CCA and moderate to strong effects on all the subdimensions of CCA, the strongest effect size was measured for the relationship of motivational CQ with CCA. Not only this, when individual CQs' relationships were assessed with the individual adjustment dimensions, the motivational aspect of CQ happened to be the most influencing factor, having a close to strong effect on interaction adjustment.

Since the study combines the results from numerous empirical research conducted over time, it avoids the limitations that an individual study has, which is carried out at a single point in time and on a limited sample.

This study adds to the academic research by critically reviewing the CQ-CCA literature. It also works as a guiding map for future research in the area. The study highlights the summary effects for each association between CQ and CCA and their dimensions, elucidating the mixed findings reported in previous research.

Income from investment properties can fluctuate depending on the state of the economy. The idea that there is always a potential exit (sale) value whenever the property stops performing at its optimum or deflation in the economy will always appeal to investors. To determine housing prices, investors would rely on a direct comparison approach (DCA) of recent substitute sales in the open market. Appraisers use this approach to develop an opinion of value when there is a plethora of recent sales to analyse.

The study was designed to establish the use of the analytical hierarchy process (AHP) approach as a support tool for deciding property appraisals. A case study of an industrial single-storey stand-alone building with grade-level parking in the south-east of Calgary, Canada, was investigated with the AHP approach. The result was cross-referenced with the DCA.

Using a consistency index of 0.077321 and a consistency ratio of 0.085912, the matrix multiplication was determined to be 0.456706. The average valuations derived from the adjusted price per square foot using the direct comparison method and the unadjusted price per square foot using the AHP were deemed the best value estimate in the light of available comparables. The implications of the findings suggest that AHP, as a quantitative technique, can support and validate the use of similar non-recent sale comparables when appraising investment properties with the DCA.

AHP is an alternative aid in quantitatively deciding the most significant value attribute for comparison before subjective adjustments. When intuitively applied in the DCA, these subjective adjustments almost always lead to an overvaluation of properties.

The purpose of this paper is to study the dynamics of capital structure in the context of Indian manufacturing companies in a partial‐adjustment framework during the period 1993‐1994 to 2007‐2008.

This paper specifies a partial‐adjustment model and uses the generalized method of moments technique to determine the variables which affect the target capital structure and to find out the factors affecting the adjustment speed to target capital structure.

Firm‐specific variables like size, tangibility, profitability and market‐to‐book ratio were found to be the most important variables which determine the target capital structure across the book and market leverage and the factors like size of the company, growth opportunity and the distance between the target and observed leverage determine the speed of adjustment to target leverage for the Indian manufacturing companies.

The behavioural variables like managers' confidence and attitude towards raising the external finance have not been incorporated in the model to determine the target capital structure due to the data constraint.

This paper has implications for corporate managers in India, for example, to consider the various adjustment costs while altering the financing decisions of the company with other variables like flexibility of the manager, direct cost of debt and equity.

This paper is first of its kind to study both the determination of target capital structure and the speed of adjustment to target capital structure in the context of Indian companies.

The paper seeks to explore home sewers' satisfaction with the fit of garments made from home sewing patterns and to provide useful information to the home sewing industry to improve products and services for the target consumer.

Home sewers attending home sewing expos in the states of Minnesota and Washington were surveyed to determine satisfaction with fit of home sewing patterns and to determine factors that may affect satisfaction with the patterns.

The results clearly indicate that these respondents were frustrated with the fit of home sewing patterns and that their efforts to adjust patterns for better fit were typically not successful.

The limited sample does not allow generalization, but may lend some insight into problems the home sewing pattern industry may have in attracting and maintaining customer loyalty.

Although there are many studies demonstrating consumer dissatisfaction with the fit of ready‐to‐wear apparel, few studies have focused specifically on the home sewing pattern industry. This industry could benefit from further understanding of its customer base.