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The purpose of this paper is to numerically investigate the combined effects of canopy (leaf area index [LAI]) and root properties (root distribution function [Rdf] and root area index [RAI]) on a suction induced in soil-root composite under three different scenarios.

Richards equation coupled with sink term was solved using a commercial finite element package “HYDRUS” to investigate suction induced in soil-root composite.

Scenario 1 unveiled that soil-root composite induces 1 to 20 per cent higher suction than bare soil under the absence of transpiration. From Scenario 2, value of suction at depth of maximum RAI in case of linearly decreasing Rdf was found to be higher than that of other Rdfs. However, depth of suction influence zone (SIZ) for uniform Rdf and non-linear Rdf was found to be 10 and 11 per cent higher than that of linearly decreasing Rdf. Depth of evaporation dominant zone (EDZ) for uniformly decreasing Rdf and non-linear Rdf was found to be 1.08 to 3 times higher than that of linearly decreasing Rdf. From Scenario 3, influence of LAI on depth of SIZ is minimal. Depth of EDZ was found to decrease with the increase in LAI. Based on simple calculation on infinite slope stability, influence of variation in root and shoot properties was found to be significant on its factor of safety.

Numerical constitutive model has limitations that it does not consider aging of plant. This model is only applicable for a particular set of soil conditions. A long-term study is required in this field to further quantify parameters for improving calibration and modeling performance.

Following are the practical implication: consideration of vegetation properties into engineered design of green infrastructure (slopes in this case) and selection of vegetation with appropriate characteristics in design for enhancement of stability of green infrastructure.

Contents of this paper are original, and they have not been submitted to any other journal.

The contribution of women toward entrepreneurial activities has gained significant attention in recent years because of economic and social concerns, government support and initiatives and increased education and awareness. Women’s entrepreneurial activity has increased and women-owned businesses can today be found in every sector of the economy, irrespective of region. In India, government bodies such as the Ministry of Micro, Small and Medium Enterprises (MSMEs) and several other organizations (private and NGOs) have adopted considerable measures to promote women entrepreneurship (WE). To improve WE, the critical factors of WE adoption need to be identified and evaluated. The purpose of this paper is to identify, prioritize and evaluate the critical success factors of WE adoption in Indian MSMEs.

This paper proposes a methodology based on fuzzy analytical hierarchal process to prioritize the critical success factors of WE adoption. A numerical analysis of Indian MSMEs is presented to demonstrate the use of the proposed method. This proposed method considered fuzzy framework, which can handle impreciseness and uncertainty. Sensitivity analysis is also performed to test the robustness of the proposed model.

Potential critical success factors are identified from relevant literature and validated by industry experts. This research finalize the critical success factors of WE adoption in Indian MSMEs under seven dimensions, so prioritization of identified critical success factors can be developed and insights relationship of factors would be explored. The results of the study found that individual, management and government dimensions take paramount importance while women aim to become entrepreneurs in Indian MSMEs.

This study is limited to identifying evaluation factors; other factors have not been identified and categorized. Evaluation is one by experts in this area so it is natural that views of decision-makers may be subjective and vary with regard to industry-type, priorities, resources, etc.

This study will help industry to identify, evaluate and prioritize factors for successful implementation of women entrepreneurship. MSMEs could device these factors by applying the outcome of the study in their decisions with higher priority to implement women entrepreneurship culture.

Potential factors are identified from relevant literature and validated by industry experts. Indian MSMEs could device these factors by applying the outcome of the study in their decisions with higher priority to adopt women entrepreneurship.

Despite widespread interest in “behavioral strategy,” it is not clear what this term, or its associated academic subfield, is all about. Unless a critical mass of scholars can agree on the meaning of behavioral strategy, and professionally identify with it, this embryonic community may face a marginal existence. We describe three alternative conceptions for the academic subfield of behavioral strategy, along with assessments of the pros and cons of each. The “small tent” version amounts to a direct transposition of the logic of behavioral economics to the field of strategic management, specifically in the style of behavioral decision research. The “midsize tent” view is that behavioral strategy is a commitment to understanding the psychology of strategists. And the “large tent’ view includes consideration of any and all psychological, sociological, and political factors that influence strategic outcomes. We conclude that the midsize tent represents the best path forward, not too narrow and not too broad, allowing rich scope but with coherence. The large tent conception of behavioral strategy, however, is not out of the question and warrants serious consideration.

Complex cost structures and multiple conflicting objectives make selecting an appropriate cloud service difficult. The purpose of this study is to propose a novel group consensus decision making method for cloud services selection with knowledge deficit by trust functions.

This article proposes a knowledge deficit-based multi-criteria group decision-making (MCGDM) method for cloud-service selection based on trust functions. Firstly, the concept of trust functions and a ranking method is developed to express the decision-making opinions. Secondly, a novel 3D normalized trust degree (NTD) is defined to measure the consensus levels. Thirdly, a knowledge deficit-based interactive consensus model is proposed for the inconsistent experts to modify their decision opinions. Finally, a real case study has been carried out to illustrate the framework and compare it with other methods.

The proposed method is practical and effective which is verified by the real case study. Knowledge deficit is an important concept in cloud service selection which is verified by the comparison of the proposed recommended mechanism based on KDD with the conventional recommended mechanism based on average value. A 3D NTD which considers three values (trust, not trust and knowledge deficit) is defined to measure the consensus levels. A knowledge deficit-based interactive consensus model is proposed to help decision-makers reach group consensus. The proposed group consensus model enables the inconsistent decision-makers to accept the revised opinions of those with less knowledge deficit, rather than accepting the recommended opinions averagely.

The proposed a knowledge deficit-based MCGDM cloud service selection method considers group consensus in cloud service selection. The concept of knowledge deficit is considered in modeling the group consensus measuring and reaching method.

The purpose of this study is to measure the effects of density, moisture, fiber content on unconfined compressive strength (UCS) of soil by formulating the models based on evolutionary approach and artificial neural networks (ANN).

The present work proposes evolutionary approach of multi-gene genetic programming (MGGP) to formulate the functional relationships between UCS of reinforced soil and four inputs (soil moisture, soil density, fiber content and unreinforced soil strength) of the silty sand. The hidden non-linear relationships between UCS of reinforced soil and the four inputs are determined by sensitivity and parametric analysis of the MGGP model.

The performance of MGGP is compared to those of ANN and the statistical analysis indicates that the MGGP model is the best and is able to generalize the UCS of reinforced soil satisfactorily beyond the given input range.

The explicit MGGP model will be useful to provide optimum input values for design and analysis of various geotechnical infrastructures. In addition, utilization of Water hyacinth reinforced fiber reinforced soil will minimize negative impact of this species on environment and may generate rural employment.

This work is first of its kind in application and development of explicit holistic model for evaluating the compressive strength of heterogeneous soil blinded with fiber content. This includes the experimental and cross-validation for testing robustness of the model.

Human resource (HR) process research emerged as a response to questions about how (bundles of) HR practices related to organizational outcomes. The goal of HR process research is to explain variability in employee and organization outcomes by focusing on how HR practices are intended (adopted) by senior managers, the way that these HR practices are implemented and communicated by line managers, and how employees perceive, understand, and attribute these HR practices. In the first part of this chapter, we present a review of 20 years of HR process research from the start, to how it developed, and is now maturing. Within the body of HR process research, several different research theoretical streams have emerged, which are largely studied in isolation without benefiting from each other. Therefore, in the second part of this chapter, we draw on previous work to propose a staged process model in which we integrate the different research streams of HR process research, recognizing contingencies in the model. This leads us to an agenda for future research and practical implications in the final part of the chapter.

Performance measurement (PM) of any supply chain is prerequisite for improving its competitiveness and sustainability. This paper develops a framework for supply chain performance measurement (SCPM) for agriculture supply chain (ASC) based on internet of things (IoT). Moreover, this article explains the role of IoT in data collection and communication (SC visibility) based on the supply chain operation reference (SCOR) model.

This research identifies various key performance indicators (KPIs) and also their role in SCPM for improving its sustainability by using SCOR. Further, Shannon entropy is utilized for weighing the basic processes of SCPM and by using weights, fuzzy TOPSIS is applied for ranking of identified KPIs at metrics level 2 (deeper level).

“Flexibility” and “Responsiveness” have been reported as two most important KPIs in IoT based SCPM framework for ASC towards achieving sustainability.

In this research, metrics are explained only at SCOR level 2. But, this research will guide the managers and practitioners of various organizations to set their benchmark for comparing their performance at different levels of business processes. Further, this paper has managerial implications to develop an effective system for PM of IoT based data-driven ASC.

By using IoT based data driven system, this article fills the gap between SCPM by measuring different SC strategies in their performance measurable form of reliable, responsive and asset management etc.

Cloud computing provides a dynamic, heterogeneous and elastic environment by offering accessible ‘cloud services’ to end-users. The tasks involved in making cloud services available, such as matchmaking, selection and composition, are essential and closely related to each other. Integration of these tasks is critical for optimal composition and performance of the cloud service platform. More efficient solutions could be developed by considering cloud service tasks collectively, but the research and academic community have so far only considered these tasks individually. The purpose of this paper is to propose an integrated QoS-based approach for cloud service matchmaking, selection and composition using the Semantic Web.

In this paper, the authors propose a new approach using the Semantic Web and quality of service (QoS) model to perform cloud service matchmaking, selection and composition, to fulfil the requirements of an end user. In the Semantic Web, the authors develop cloud ontologies to provide semantic descriptions to the service provider and requester, so as to automate the cloud service tasks. This paper considers QoS parameters, such as availability, throughput, response time and cost, for quality assurance and enhanced user satisfaction.

This paper focus on the development of an integrated framework and approach for cloud service life cycle phases, such as discovery, selection and composition using QoS, to enhance user satisfaction and the Semantic Web, to achieve automation. To evaluate performance and usefulness, this paper uses a scenario based on a Healthcare Decision-Making System (HDMS). Results derived through the experiment prove that the proposed prototype performs well for the defined set of cloud-services tasks.

As a novel concept, our proposed integrated framework and approach for cloud service matchmaking, selection and composition based on the Semantic Web and QoS characterisitcs (availability, response time, throughput and cost), as part of the service level agreement (SLA) will help the end user to match, select and filter cloud services and integrate cloud-service providers into a multi-cloud environment.

The purpose of this paper is to propose a holistic approach for supplier evaluation and purchasing order allocation among the ranked suppliers who meet acceptable levels of economic, environmental and social measures.

A mixed research method of case study and analytical approach is adopted in this research. A fuzzy analytical hierarchical process (FAHP) is applied for ranking of suppliers. Supplier ranks are validated using judgements from multiple decision makers. Purchasing order allocation among the ranked suppliers is determined using cost minimization subject to multiple criteria of economic, environmental and social conditions. A cement manufacturing case example demonstrates and validates the proposed approach.

The research shows that both economic and environmental considerations are significant when suppliers are evaluated for sustainable procurement within the best practice of supply management process. Ranking of suppliers, based on experts’ opinions, indicates varying degrees of importance for each criterion. Adoption of sustainable procurement criteria for evaluating supplier in a cement manufacturing organization is explained by three organizational theories including resource-based, institutional and dynamic capabilities theories. Preferred suppliers from FAHP method are confirmed by judgements from multiple decision-makers. The analysis reveals that purchasing order allocation is different when suppliers are evaluated based on their relative importance and overall ranking.

Currently, individual performance measures and decision-makers are selected from a limited set. The purchasing allocation among ranked suppliers, subjected to cost minimization, incorporates environmental objective of acceptable carbon dioxide emission and social perspective of health and safety of workers, and provides a new approach for dual supplier evaluation and purchasing allocation problem in cement industry. Adopting the proposed supplier evaluation and order allocation approach in practice needs to be guided by the operational principles and an overall methodology which is appropriate for the specific industry with sustainability objectives.

This research enables decision-makers to incorporate sustainability analysis in the supplier evaluation as the basis for best practice with an industry-friendly holistic approach. Using organizational theories, the research re-enforces the importance of not only the energy consumption and environmental management systems of environmental dimension as driving forces/factors from Institutional theory perspective, but also pollution controls and prevention as purchasing capabilities from resource-based theory perspective. The proposed approach is expected to motivate decision-makers to consider sustainable perspectives in supplier evaluation and order allocation processes in a global supply chain and can become a benchmarking tool.

Suppliers’ information on health and safety of their truck drivers are used in order allocation, thus emphasizing the importance of social dimension and encouraging better conditions and benchmarking for delivery drivers.

This paper extends the contribution to the literature by providing guidelines for managers to set strategies, benchmarks and policies within broader sustainable supply chain practices and demonstrates the applicability of the approach using a cement-manufacturing scenario in an emerging economy.

When a firm implements certain HR practices, different employees attribute different motives and intentions to the firm with regard to those HR practices. Research on HR attributions has made progress toward understanding the relationship between HR practices and employee outcomes from a process perspective. However, this research is still fragmented and lacks a systematic typology of the different types of HR attributions and a compelling organizing research framework. Furthermore, a number of research gaps and opportunities have emerged regarding the nomological net of employee HR attributions. To address the gaps and capitalize on the opportunities, the authors propose an overarching theory-driven multi-level framework that guides the choice of the antecedents and outcomes of employee HR attributions and explains their relationships along with both mediating and moderating mechanisms. Drawing on signaling theory embedded in the proposed framework, the authors identify and categorize various antecedents of employee HR attributions to explain their relationships. The authors also use several additional theories such as social exchange and the job demands–resources model included in their review to identify and categorize various outcomes of employee HR attributions across levels of analysis (i.e., individual, collective [team/group/unit], organization) and explain their relationships. In addition, the proposed framework explains how individual-level employee HR attributions emerge at the collective level and influence collective processes and outcomes. The authors end their review by pinpointing future research needs and discussing related future research directions.