Search results

Adopting the dynamic capability perspective, this study aims at exploring which problem-solving capabilities result in fundamental solutions with a potentially low likelihood for problems to recur. This can also shed light on why, despite many attempts, process improvement programs often fail to produce such long-term solutions.

A case study is carried out to inductively describe and classify problem-solving in companies and to indicate why problem-solving efforts are typically bounded to short-term solutions. The empirical findings are triangulated with findings from the extant literature.

First, the authors propose three problem-solving modes with different characteristics and potential impacts on operational performance: intuitive problem-solving, semi-structured problem-solving and systematic problem-solving. Second, by emphasizing dynamic capabilities' micro-foundations and with the focus on learning mechanisms, the authors show that, among these modes, only systematic problem-solving can serve as a dynamic capability with fundamental solutions. Third, based on insights from the case study, the authors address behavioral and organizational impediments that curb dynamic capabilities and limit systematic problem-solving adoption.

This study is an empirically informed attempt to understand systematic problem-solving as a dynamic capability. The authors uncover the micro-foundations and the learning mechanisms through which systematic problem-solving becomes a dynamic capability. By highlighting problem-solving orientation as a hardly investigated dimension of improvement programs, the authors show that a mixture of a static problem-solving approach and a set of impediments at both individual and organizational levels is the major reason of failures of improvement programs over time.

This study aims at determining the factors that favor a systematic approach to deal with complex operational and strategic problems. Management literature on problem-solving makes a clear distinction between either fixing a problem temporarily by eliminating its symptoms or solving it by diagnosing and altering underlying causes. Adopting a cognitive perspective of the dual-processing theory, this study labels these two approaches intuitive problem-solving and systematic problem-solving (SPS). While the superior effectiveness of SPS in fostering organizational learning is widely documented, existing literature fails to provide an overview of the conditions that support the adoption of SPS.

This paper presents a systematic literature review to shed light on the main supporting factors of SPS in operational as well as strategic domains.

Seven supporting factors of SPS (namely, nature of the problem, time availability, information availability, collaborative culture, transformational leadership, organizational learning infrastructure and environmental dynamism) are first identified and then discussed in an integrative model.

This work is an original attempt to inclusively address organizational, environmental and problem nature-related factors that favor SPS adoption. By determining the SPS supporting factors, this study highlights why many organizations fail or struggle to implement and sustain SPS over time.

One pertinent dynamic phenomenon in supply chains is the amplification of order variance, i.e. the bullwhip effect. Its continued significance is underscored in contemporary empirical research. While numerous publications have pinpointed various causes of the bullwhip effect, there remains a gap in their systematic consolidation. The purpose of this paper is to compile a comprehensive list of the causes of the bullwhip effect from existing literature and categorize them appropriately.

This study conducts a systematic literature review to offer a comprehensive overview of bullwhip effect causes addressed in the existing literature. The identified causes are categorized using a qualitative content analysis approach.

The study shows the diversity of the causes of the bullwhip effect and their interdependencies. In addition, this study demonstrates that, at the highest level of aggregation, causes of the bullwhip effect can be classified into four main categories: causes inherent in the system structure, causes related to uncertainty, causes related to misaligned incentives and causes related to inadequate cognition of the situation.

The work provides an extensive overview and categorization of bullwhip effect causes, offering valuable insights for both researchers and practitioners seeking a deeper understanding of this phenomenon. In addition, it underscores managerial implications and highlights future research opportunities.

The inevitable occurrence of natural disasters and crisis arising from them causes a lot of losses globally, particularly in disaster-prone countries such as Iran. One of the main issues considered by organizations involved in crisis management is the selection of suitable sites for temporary sheltering for disaster victims. This study aims to choose safe places to establish temporary sheltering in urban environment.

Initially, relevant factors are identified by reviewing literature and through consultation with disaster experts. Next, the important layers were collected and analytical hierarchy process was used to assess the criteria weights based on their effectiveness on selection of safe sites for temporary sheltering. Finally, for integrating layers of factors, overlay and fuzzy models were used in Geographic Information System (GIS) environment, and subsequently, a proper map was prepared and suitable areas were identified.

7 main criteria and 19 sub-criteria were selected to provide safe places for temporary sheltering. The results of fuzzy model in this study provide more accurate and limited safe areas for temporary sheltering when compared to index overlay model.

The results of this study will help decision-makers and local and regional managers to reduce the vulnerability of at-risk communities in urban environments. Moreover, choosing appropriate places for temporary shelters would help build community disaster resilience according to these criteria.

This paper aims to apply a new approach for resource sharing and efficiency estimation of subunits in the presence of non-discretionary factors and partial impacts among inputs and outputs in the data envelopment analysis (DEA) framework.

First, inspired by the Imanirad et al.’s model (2013), the authors consider that each decision-making unit (DMU) may consist of several subunits, that each of which can be affected by non-discretionary inputs. After that, the Banker and Morey’s model (1996) is used for modeling non-discretionary factors. For measuring performance of several subunits, which can be considered as DMUs, the aggregate efficiency is suggested. At last, the overall efficiency is computed and compared with each other.

One of the important features of proposed model is that each output in this model applies discretionary input according to its need; therefore, the result of this study will make it easier for the managers to make better decisions. Also, it indicates that significant predictions of the development of the overall efficiency of DMUs can be based on observing the development level of subunits because of the influence of non-discretionary input. Therefore, the proposed model provides a more reasonable and encompassing measure of performance in participating non-discretionary and discretionary inputs to better efficiency. An application of the proposed model for gaining efficiency of 17 road patrols is provided.

More non-discretionary and discretionary inputs can be taken into consideration for a better analysis. This study provides us with a framework for performance measures along with useful managerial insights. Focusing upon the right scope of operations may help out the management in improving their overall efficiency and performance. In the recent highway maintenance management systems, the environmental differences exist among patrols and other geotechnical services under the climate diverse. Further, in some cases, there might exist more than one non-discretionary factor that can have different effects on the subunits’ performance.

The purpose of this paper was to measure the performance of a set of the roadway maintenance crews and to analyze the impact of non-discretionary inputs on the efficiency of the roadway maintenance. The application of the proposed model, on the one hand, showed that each output in this model uses discretionary input according to its requirement, and on the other hand, the result showed that meaningful predictions of the development of the overall efficiency of DMUs can be based on observing the development level of subunits because of the impact of non-discretionary input.

Providing information on resource sharing by taking into account non-discretionary factors for each subunit can help managers to make better decisions to increase the efficiency.