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This study uses structural equation modeling to investigate the impact of ABC implementation factors (management support, clarity and consensus of ABC objectives, non-accounting ownership, and training) on quality, cost, and cycle time improvements, the relations among quality, cost, and cycle time improvements and, the influence of quality, cost, and cycle time improvement on financial performance at the business unit level. Overall, the results of the structural analyses support the theoretical model indicating that ABC implementation factors influence quality, cost, and cycle time, and partial support for the relations among quality, cost, and cycle time improvement and their effect on financial performance. When these relationships are further analyzed within the context of ABC implementation stage, adoption of advanced manufacturing practices, industry characteristics and plant size to determine if these contextual factors impact the model constructs and the relationships between the variables in the theoretical model, the results show that these contextual factors do not affect the model constructs, however, they affect the model relations.

Rare but high impact (R-HI) disruptions, which are caused by legal changes, socio-technical accidents, or natural disasters, are becoming more frequent and have strong short-term and long-term impacts on performance. Meanwhile, the short-term perspective of managers leads to adoption of mitigation strategies with lower investments and immediate performance improvement. The purpose of this paper is to provide insights on supply chain collaboration (SCC) to establish so-called twin-objective strategy to help both risk mitigation (through moderation effects) and performance improvement (through a direct positive impact). Moreover, power position will be considered as the control variable.

A cross-sectional approach was adopted with primary data collected through a survey in China. Data were analyzed using structural equation modeling with partial least squares estimations. A sub-group model analysis was applied to test the effect of the control variable.

The findings verify that SCC has both a direct positive impact on performance and moderation effects on the relationships between sources of R-HI disruptions and performance. The results of sub-group model analysis illustrate that both powerful and weak focal firms benefit from SCC, but in different ways.

The study shows that the allocation of gains from collaborative advantage should be added to the theory-building of relational view. Meanwhile, the research extends the focal firm’s context to its supply chain’s context so that classic contingency theory can be extended to adequately explain supply chain management phenomena.

The purpose of this paper is to demonstrate how inter-organizational learning (including supply chain learning and imitation prevention) mediates the relationships between supply chain integration (SCI) and two dimensions of focal firm performance (i.e. customer service performance and innovation performance).

A cross-sectional approach was adopted with primary data collected through a survey in China. Data were analyzed using structural equation modeling with partial least-squares estimations.

The findings verify that inter-organizational learning mediates the relationship between SCI and focal firm performance. The results of sub-group model analysis illustrate that both powerful and weak focal firms benefit from inter-organizational learning, but in different ways.

The responses were all from young executives who had four years’ work experience on average. Top-level executives may provide more comprehensive and accurate input for similar future research.

The results suggest that successfully integrating the supply chain to create customer value requires both supply chain learning and imitation prevention.

This paper responds to calls for an inter-disciplinary research between supply chain management and inter-organizational learning by taking into account supply chain learning and imitation prevention as links between SCI and both customer service performance for current success and innovation performance for future prosperity.

Competition and cooperation co‐exist in various sub‐fields of organizational strategies, while a research gap remains in the links between how managers perceive their cognitive relations with rival partners and how they choose a strategy. The purpose of this paper is to investigate how different focuses of competition and cooperation are put in core and supportive strategic importance based on business manager's individual perception toward a particular rivalling cognition.

A conceptual model is developed composed by several hypotheses. An empirical study is conducted by analysing data collected from 89 pharmacies, including public hospital pharmacies and community service, private chain retailing pharmacy, and independent pharmacies, out of hundreds of outlets in a capital city in China to test hypotheses. By using factor analysis and correlation analysis, several hypotheses are supported in linking competitive cognition with either core marketing strategies or supportive marketing strategies.

Observational results indicate that large and small pharmacies, motivated by relational perceptions among competitors, tend to rely selectively on some strategic tools of competition and cooperation in terms of their different business nature.

These results are valuable for business managers in the healthcare industry, enabling them to rethink their relations with strategic partners and their strategies.

The paper's findings enrich understanding of how a competing environment influences strategic orientation of competition and cooperation under a collaborative marketing framework.

Annual influenza epidemics cause great losses in both human and financial terms. The purpose of this paper is to propose a model for optimizing a large-scale influenza vaccination program (VP). The goal is to minimize the total cost of the vaccination supply chain while guaranteeing a sufficiently high level of population protection. From a practical point of view, the analysis returns the number of shipments and the quantity of vaccines in each periodic shipment that should be delivered from the manufacturers to the distribution center (DC), from the DC to the clinics, and from the clinics to each sub-group of customers during the vaccination season.

A mixed-integer programming optimization model is developed to describe the problem for a supply chain consisting of vaccine manufacturers, the healthcare organization (HCO) (comprising the DC and clinics), and the population being vaccinated (customers). The model suggests a VP that implemented by a nation-wide HCO.

The benefits of the proposed approach are shown to be particularly salient in cases of limited resources, as the model distributes demand backlogs in an efficient manner, prioritizing high-risk sub-groups of the population over lower-risk sub-groups. In particular, the authors show a reduction in direct medical burden of consumers, such as the need for doctors, hospitalization resources, and reduction of indirect, non-medical burden, such as loss of workdays.

Drawing from the extended enterprise paradigm, and, in particular, taking consumer benefits into account, the authors suggest an operational-strategic model that creates impressive added value in a highly constrained supply chain. The model constitutes a powerful decision tool for the deployment of large-scale seasonal products, and its implementation can yield multiple benefits for various consumer segments.

The model proposed herein constitutes a decision support tool comprising operational-tactical and tactical-strategic perspectives, which logistics managers can utilize to create an enterprise-oriented plan that takes into account medical and non-medical costs.

States that it is critical that incumbent firms understand the processes that enhance or inhibit entry of new firms into their industry. A new entrant into an industry may create additional demand by legitimizing the technology/products, and/or may share the existing market by drawing buyers away from incumbents. An analysis of market entry rates is especially important in new, high technology industries where sub‐groups of firms pursue different technology and global market diversification strategies because such sub‐groups may have asymmetrical cross‐effects on entry rates of new firms. Suggests a community ecology approach to assessing the impact of industry density on new firm entry rates. The framework is demonstrated by applying it to the global personal computer industry during the period of 1977‐1992. Results suggest that density has a nonmonotonic positive effect, while the firm‐level variables of technological strategy and market expansion strategies have a monotonic positive effect on new firm entry rates.

Class-based storage has been studied extensively and proved to be an efficient storage policy. However, few literature addressed how to cluster stuck items for class-based storage. The purpose of this paper is to develop a constrained clustering method integrated with principal component analysis (PCA) to meet the need of clustering stored items with the consideration of practical storage constraints.

In order to consider item characteristic and the associated storage restrictions, the must-link and cannot-link constraints were constructed to meet the storage requirement. The cube-per-order index (COI) which has been used for location assignment in class-based warehouse was analyzed by PCA. The proposed constrained clustering method utilizes the principal component loadings as item sub-group features to identify COI distribution of item sub-groups. The clustering results are then used for allocating storage by using the heuristic assignment model based on COI.

The clustering result showed that the proposed method was able to provide better compactness among item clusters. The simulated result also shows the new location assignment by the proposed method was able to improve the retrieval efficiency by 33 percent.

While number of items in warehouse is tremendously large, the human intervention on revealing storage constraints is going to be impossible. The developed method can be easily fit in to solve the problem no matter what the size of the data is.

The case study demonstrated an example of practical location assignment problem with constraints. This paper also sheds a light on developing a data clustering method which can be directly applied on solving the practical data analysis issues.

The rapid intensification of the internet and electronic commerce diffusion has given rise to electronic business process management (e‐BPM) which enhances the overall connectivity of the business processes. However, when confronted by the range of e‐BPM best practices (e‐BPM^BPs), organizations struggle to identify the one most appropriate to their needs. The paper aims to address these issues.

The paper proposes a novel fuzzy group multi‐objective method for e‐BPM^BP evaluation and selection. First, a fuzzy group linear assignment method is used to rank the e‐BPM^BPs drawing on the four perspectives of a balanced scorecard (BSC). Second, a fuzzy group real options approach is used to estimate the financial values of the ranked e‐BPM^BPs. Third, a four‐objective assignment model is used to select the optimal e‐BPM^BP in deferral time with respect to their rankings, financial values, and a series of pertinent constraints.

The contribution of the proposed method is threefold: it is grounded in the four perspectives of a BSC, it considers imprecise or vague judgments which lead to ambiguity in the decision process, and it uses a meaningful and robust multi‐objective model to aggregate both qualitative judgments and quantitative data. A case study is presented to demonstrate the applicability of the proposed framework and to exhibit the efficacy of the procedures and algorithms.

The novel fuzzy group multi‐objective framework for e‐BPM^BP evaluation and selection proposed in the paper takes into consideration (1) the qualitative and quantitative criteria and their respective value judgments; (2) the verbal expressions and linguistic variables for qualitative judgments which lead to ambiguity in the decision process; and (3) imprecise or vague judgments.

Purpose – The purpose of the chapter is to provide an analytical overview of information research in the United Kingdom and of the role of the Research Assessment Exercises (RAE) in shaping the form and structure of that research.

Design/methodology/approach – The approach adopted is a detailed content analysis of the submissions made to the last UK RAE. This analysis is carried out in relation to four broad subject categorisations, and specific analysis of accounts of research carried out in the departments and research groups.

Findings – The RAE have played a key role in promoting research specialisms in library and information studies (LIS) research in the United Kingdom. The former general approach to research in information studies has been replaced by more focused research activities carried out in a variety of research groups spread across a diverse range of disciplines and departments, from LIS, to business and management, information systems, and computing and engineering.

Research implications – The prospects for general LIS research departments may be increasingly limited, as research becomes concentrated in sub-groups within larger organisational structures, subverting both departmental lines and conventional subject boundaries.

Originality/value – This overview provides a novel synthesis of information research in the United Kingdom in relation to four broad categories of research in information studies and information science, information management and social informatics, information systems and information interaction, and social computing and computational informatics. The account brings together a fragmented field of research in a compact and intelligible form.

The purpose of this study is to suggest a new framework that we call the CIR#, which allows forecasting interest rates from observed financial market data even when rates are negative. In doing so, we have the objective is to maintain the market volatility structure as well as the analytical tractability of the original CIR model.

The novelty of the proposed methodology consists in using the CIR model to forecast the evolution of interest rates by an appropriate partitioning of the data sample and calibration. The latter is performed by replacing the standard Brownian motion process in the random term of the model with normally distributed standardized residuals of the “optimal” autoregressive integrated moving average (ARIMA) model.

The suggested model is quite powerful for the following reasons. First, the historical market data sample is partitioned into sub-groups to capture all the statistically significant changes of variance in the interest rates. An appropriate translation of market rates to positive values was included in the procedure to overcome the issue of negative/near-to-zero values. Second, this study has introduced a new way of calibrating the CIR model parameters to each sub-group partitioning the actual historical data. The standard Brownian motion process in the random part of the model is replaced with normally distributed standardized residuals of the “optimal” ARIMA model suitably chosen for each sub-group. As a result, exact CIR fitted values to the observed market data are calculated and the computational cost of the numerical procedure is considerably reduced. Third, this work shows that the CIR model is efficient and able to follow very closely the structure of market interest rates (especially for short maturities that, notoriously, are very difficult to handle) and to predict future interest rates better than the original CIR model. As a measure of goodness of fit, this study obtained high values of the statistics R² and small values of the root of the mean square error for each sub-group and the entire data sample.

A limitation is related to the specific dataset as we are examining the period around the 2008 financial crisis for about 5 years and by using monthly data. Future research will show the predictive power of the model by extending the dataset in terms of frequency and size.

Improved ability to model/forecast interest rates.

The original value consists in turning the CIR from modeling instantaneous spot rates to forecasting any rate of the yield curve.