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There are multiple perspectives of divestiture and its performance that require reconciliation. While research finds a positive market response to divestment announcement, divestiture of prior acquisitions are generally viewed negatively. The purpose of this paper is to develop and empirically test different explanations for the divestment of prior acquisitions.

This research employs event study to capture market reaction at acquisition announcement and subsequent divestments in a sample of 69 public US high-technology acquisitions between 2003 and 2008 that were divested by 2015. Only initial acquisitions involving public firms were included from the Thomson One Banker SDC database. Public press releases and companies’ SEC filings were reviewed to track divestitures back to prior acquisitions. Ordinary least squared regression was used to estimate coefficients.

Results indicate a positive relation between acquisition and divestiture performance around announcement dates. This finding rejects the correction of mistake explanation, suggesting that a negative stigma surrounding divestments is largely unwarranted and that investors reward capable acquirer’s divestiture decisions.

Investors do not treat all information signals at divestiture equally. For example, acquisitions made by larger and more profitable firms, or acquisitions paid for with stock, are associated with lower return upon divestiture announcement.

This study finds that investors view divestiture as a proactive strategy, suggesting firms can improve performance by actively managing acquisitions and divestments to optimize their portfolio of businesses.

Increasing chances of firm survival requires enduring entrepreneurship or the ability to balance competing demands for exploration and exploitation. We developed how acquisitions can provide needed disruption to change a firm’s dominant orientation toward exploration or exploitation or enable a continued focus on a firm’s dominant orientation. The result is a new typology for acquisition integration associated with different pre- and post-acquisition characteristics. For example, a firm with an exploitation orientation faces different integration challenges in acquiring targets with an exploration or exploitation orientation. We also distinguished between human and task integration to enable more nuanced integration decisions that help to reconcile conflicting findings on acquisition integration decisions. Implications for management research and practice were discussed.

This study aims to analyze and predict a user’s behavior and create recommender systems in libraries and information centers, using data mining techniques.

The present study is an analytical survey study of cross-sectional type. The required data for this study were collected from the transactions of the users of libraries and information centers in Hamadan University of Medical Sciences. Using data mining techniques, the existing patterns were investigated, and users’ loan transactions were analyzed.

The findings showed that the association rules with the degree of confidence above 0.50 were able to determine user access patterns. Furthermore, among the decision tree algorithms, the C.05 predicted the loan period, referrals and users’ delay with the highest accuracy (i.e. 90.1). The other findings on feedforward neural network with R = 0.99 showed that the predicted results of neural network computation were very close to the real situation and had a proper estimation of user’s delay prediction. Finally, the clustering technique with the k-means algorithm predicted users’ behavior model regarding their loyalty.

The results of this study can lead to providing effective services and improve the quality of interaction between librarians and users and provide a good opportunity for managers to align supply of information resources with the real needs of users.

The results of the study showed that various data mining techniques are applicable with high efficiency and accuracy in analyzing library and information centers data and can be used to predict a user’s behavior and create recommendation systems.

One of the problems in post-earthquake disaster management in developing countries, such as Iran, is the prediction of the residual network available for disaster relief operations. Therefore, it is important to use methods that are executable in such countries given the limited amount of accurate data. The purpose of this paper is to present a multi-objective model that seeks to determine the set of roads of a transportation network that should preserve its role in carrying out disaster relief operations (i.e. known as “emergency road network” (ERN)) in the aftermath of earthquakes.

In this paper, the total travel time of emergency trips, the total length of network and the provision of coverage to the emergency demand/supply points have been incorporated as three important metrics of ERN into a multi-objective mixed integer linear programming model. The proposed model has been solved by adopting the e-constraint method.

The results of applying the model to Tehran’s highway network indicated that the least possible length for the emergency transportation network is about half the total length of its major roads (freeways and major arterials).

Gathering detailed data about origin-destination pair of emergency trips and network characteristics have a direct effect on designing a suitable emergency network in pre-disaster phase.

To become solvable in a reasonable time, especially in large-scale cases, the problem has been modeled based on a decomposing technique. The model has been solved successfully for the emergency roads of Tehran within about 10 min of CPU time.

Expert systems are computer-based systems that mimic the logical processes of human experts or organizations to give advice in a specific domain of knowledge. Fuzzy expert systems use fuzzy logic to handle uncertainties generated by imprecise, incomplete and/or vague information. The purpose of this paper is to present a comprehensive review of the methods and applications in fuzzy expert systems.

The authors have carefully reviewed 281 journal publications and 149 conference proceedings published over the past 37 years since 1982. The authors grouped the journal publications and conference proceedings separately accordingly to the methods, application domains, tools and inference systems.

The authors have synthesized the findings and proposed useful suggestions for future research directions. The authors show that the most common use of fuzzy expert systems is in the medical field.

Fuzzy logic can be used to manage uncertainty in expert systems and solve problems that cannot be solved effectively with conventional methods. In this study, the authors present a comprehensive review of the methods and applications in fuzzy expert systems which could be useful for practicing managers developing expert systems under uncertainty.

The purpose of this paper is to develop an economic statistical design based on the concepts of adjusted average time to signal (AATS) and ANF for X ¯ control chart under a Weibull shock model with multiple assignable causes.

The design used in this study is based on a multiple assignable causes cost model. The new proposed cost model is compared with the same cost and time parameters and optimal design parameters under uniform and non-uniform sampling schemes.

Numerical results indicate that the cost model with non-uniform sampling cost has a lower cost than that with uniform sampling. By using sensitivity analysis, the effect of changing fixed and variable parameters of time, cost and Weibull distribution parameters on the optimum values of design parameters and loss cost is examined and discussed.

This research adds to the body of knowledge relating to the quality control of process monitoring systems. This paper may be of particular interest to practitioners of quality systems in factories where multiple assignable causes affect the production process.

The cost functions for uniform and non-uniform sampling schemes are presented based on multiple assignable causes with AATS and ANF concepts for the first time.

The purpose of this paper is to develop a cost model by the variable sampling interval and optimization of the average cost per unit of time. The paper considers an economic–statistical design of the X̅ control charts under the Burr shock model and multiple assignable causes were considered and compared with three types of prior distribution for the mean shift parameter.

The design of the modified X̅ chart is based on the two new concepts of adjusted average time to signal and average number of false alarms for X̅ control chart under Burr XII shock model with multiple assignable causes.

The cost model was examined through a numerical example, with the same cost and time parameters, so the optimal of design parameters were obtained under uniform and non-uniform sampling schemes. Furthermore, a sensitivity analysis was conducted in a way that the variability of loss cost and design parameters was evaluated supporting the changes of cost, time and Burr XII distribution parameters.

The economic–statistical model scheme of X̅ chart was developed for the Burr XII distributed with multiple assignable causes. The correlated data are among the assumptions to be examined. Moreover, the optimal schemes for the economic-statistic chart can be expanded for correlated observation and continuous process.

The economic–statistical design of control charts depends on the process shock model distribution and due to difficulties from both theoretical and practical aspects; one of the proper alternatives may be the Burr XII distribution which is quite flexible. Yet, in Burr distribution context, only one assignable cause model was considered where more realistic approach may be to consider multiple assignable causes.

This study presents an advanced theoretical model for cost model that improved the shock model that presented in the literature. The study obviously indicates important evidence to justify the implementation of cost models in a real-life industry.

The purpose of this paper is to identify and categorize the social and environmental impacts of transportation systems, and address the appropriate solutions to mitigate and manage these impacts in order to achieve sustainability.

This research performs a comprehensive literature review to suggest a framework on socio-environmental impacts of transportation and related solutions. The proposed framework is analyzed through quantitative methods and a survey study in freight transport.

Findings support the direct relation of potential solutions and socio-environmental impacts of transportation. All four categories of solutions (technological, socio-economic and political, cultural and behavioral, and infrastructure related) reveal direct impact on reduction of environmental impacts. However only technological solutions were found to be influential on social impacts of the transportation.

To the best of the authors’ knowledge there has not been any comprehensive framework covering social and environmental impacts of transportation in the literature. In addition, this paper categorizes potential solutions to enhance socio-environmental performance of the transportation and investigates their effectiveness.