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Different pollutants emission due to the conventional energy consumption process is of major concern owed to its significant effect on human health vis-à-vis regional climate. The purpose of this paper is to develop mitigation strategies to reduce the pollutant concentrations in the rural households conducted in a pristine rural village of Indian Sunderban area.

During winter, 2014, 20 households which were using traditional biomass cookstove for cooking and kerosene-lamp for lighting (KIT-TRD) were randomly selected. Specific type of improved forced draft biomass cookstove and solar lantern were used in ten of the selected households (KIT-IMP). Real time concentrations of particulate matter (PM) (PM_2.5, PM₁₀) and carbon monoxide (CO) during day and evening time cooking period were measured in KIT-IMP and KIT-TRD. A simulation model was established to evaluate most potential factor to control the level of pollutants inside the kitchen.

Conventional processes of energy consumption in the households, along with the outdoor concentration of pollutants influence the indoor concentration of measured pollutants. The concentration of PM and CO was significantly lower in the KIT-IMP than the other. In the KIT-TRD households, the daytime concentration of PM and CO was significantly higher compared to the evening. The simulated output overestimated the concentration of PM10 and CO in the KIT-TRD.

The concentration of PM_2.5, PM₁₀ and CO significantly reduces in the indoor environment with the introduction of improved cookstove and solar lantern; however, further research is required to develop optimum sizes of window and door in the rural households to reduce the concentrations of different pollutants inside the kitchen.

The purpose of this paper is to introduce a model of decision-making problem in multi-criteria optimization domain for project selection. The model is built by combining the soft set theory and analytic hierarchical model under fuzziness. Soft set model gives us the opportunity to use parameterization properties. Here, the authors have proved that multiple alternatives can be reduced to make the selection process computationally efficient. Here, the authors illustrate the hybrid method by means of an application of the new mathematical model of soft set theory.

This paper is designed to excel a decision support system with multiple criteria analysis tool, analytic hierarchy process combined with soft set theory under fuzziness.

In this paper, the authors have taken four projects P1, P2, P3 and P4. As per chosen parameters of softest theory the result of the illustrative example reveals that P2 is the best project. The ranking the authors get is in the order of P2, P3, P4 and P1. The algorithm leads the authors to maximize the proper choice in the environment of imprecise information. The main advantage of this method compare to others is that this hybrid method is very simple in terms of calculation and the computational complexity of the proposed algorithm is low.

This proposed decision support strategy for an intended project manager helped to take decision in the perspective environment.

Strict carbon-cap policy is one of the basic policies proposed by the regulatory bodies to reduce the anthropogenic greenhouse gas emission. The purpose of this paper is to examine whether it is beneficial for a company to invest in green technology or not under the strict carbon-cap policy and for that a two echelon supply chain model is developed. This paper gives insight about judicious decision about investment on green technology.

Mathematical modeling approach has been adopted to understand the effect of investment on green technology. All the cost and emissions parameters have been derived and the total cost (TC) and total emission equations have been formulated mathematically. Two constrained mixed-integer nonlinear programming (MINLP) problems have been formulated and solved considering with or without green investment. Further, supply chain cost is optimized without carbon constraint to understand the effect of carbon constraint.

The investment in green technology can reduce the total supply chain cost. The study reveals that handling different parameters optimally can reduce both cost and emissions.

This paper tries to assess the effectiveness of green investment on technology under strict carbon-cap policy on a supply chain and, thereby, added value to the existing work. It examines the role played by various parameters under strict carbon-cap policy to draw insights, which will be beneficial for the academic community and managers.

The purpose of this paper is to provide an understanding of different explanations in mergers and acquisitions (M&A) research that deal with M&A performance. After five decades of M&A research, the findings on M&A performance are diverse and sometimes inconsistent with each other. The explanatory variables studied in the empirical works reflect primarily on researchers’ approach, construct, measurement techniques and data availability, leading to inconsistencies among the findings. In order to understand how researchers have measured M&A performance so far, the gaps in existing work and to identify the scope of future work, the authors conduct a systematic review of empirical M&A research on explaining M&A performance.

This research has been carried out as a structured assessment of past literature. The findings from selected research works have been categorized, grouped and summarized to discern a meta‐analytic view of the work carried out to date.

The M&A performance measures are diverse owing to heterogeneous views on what constitutes M&A performance and organization performance. They are categorized under Accounting Measures, Market Measures and Other Measures, including subjective assessments. The explanatory variables found in the studies are extensive and can be categorized under Deal Characteristics, Managerial Effects, Firm Characteristics and Environmental Factors.

The paper extracts some key trends in M&A performance studies carried out in empirical works over two decades. The findings help to identify drawbacks and set an agenda for future work.

The purpose of this conceptual paper is to explicate the role played by information technology (IT) in enabling managerial dynamic capabilities. By doing so, this paper seeks to address a critical theoretical gap regarding IT’s role in enabling dynamic capabilities (DCs). DCs are knowledge-intensive and information-intensive processes and play a crucial role in facilitating strategic renewal of firms operating in volatile, uncertain, complex and ambiguous business environments. Although managers play a central role in the DCs framework, extant research has only focused on the role of IT in enabling firm-level and process-level DCs.

This conceptual paper uses the literatures on dynamic managerial capabilities, individual-level information system use, social capital, human capital, managerial cognition and technology-enabled learning to build propositions that link managerial IT use with the enablement of dynamic managerial capabilities.

This paper introduces a new construct called individual IT leveraging capability (IILC) and provides theoretically grounded arguments that link IILC with managerial social capital, managerial cognition and managerial human capital. It also explicates the relationships between managerial social capital, managerial cognition and managerial human capital and the dynamic managerial capabilities of sensing, seizing and reconfiguring.

The establishment of the linkage between IT and dynamic managerial capabilities extends the literature on the business value of IT. This work also adds to the literature on dynamic managerial capabilities by providing a theoretically grounded argument that IT can act as an antecedent of such capabilities.

To the best of the authors’ knowledge, this paper is arguably the first to theorize the role of IT in enabling managerial DC and thus addresses a critical gap in academic research literature.