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Organizational researchers have become increasingly interested in multi-level constructs – that is, constructs that are meaningful at multiple levels of analysis. However, despite the plethora of theoretical and empirical work on multi-level topics, explicit frameworks for validation of multi-level constructs have yet to be fully developed. Moreover, available principles for conducting construct validation assume that the construct resides at a single level of analysis. We propose a five-step framework for conceptualizing and testing multi-level constructs by integrating principles of construct validation with recent advancements in multi-level theory, research, and methodology. The utility of the framework is illustrated using theoretical and empirical examples.

Organizational creativity and innovation are inherently complex phenomena, and subject to a myriad of broad contextual and social influences. As the evidence grows for the link between innovation and organizational effectiveness and, ultimately, organizational survival, there is no doubting the need for theoretical and practical advances in our understanding. The complex nature of these constructs, however, requires that such efforts utilize a multi-level lens. This chapter discusses key aspects of creativity and innovation in organizations, including fundamental construct definition issues, which underscore the need for a multi-level perspective. It also reviews extant theoretical perspectives for their contributions to a multi-level understanding, and the research in two key areas of social influence – group factors and leadership – that have received substantial attention in the organizational literature. The review and discussion of these areas reveal not only numerous advances, but also substantial limitations that must be resolved through more complex and comprehensive (i.e., multi-level) approaches. The chapter concludes with several recommendations intended to guide and inform future work in the organizational creativity and innovation field.

Industrial organizations often face difficulties in finding out the methods to meet ever increasing customer expectations and to remain competitive in the global market while maintaining controllable expenses. An effective and efficient green supply chain management (GSCM) can provide a competitive edge to the business. This paper focusses on the selection of green suppliers while simultaneously balancing economic, environmental and social issues.

In this study, it is assumed that two types of decision-makers (DMs), namely, the first level and second-level DMs operate at two separate groups in GSC. The first-level DMs always empathise to optimize carbon emissions, per unit energy consumption per product and per unit waste production, while the second-level DMs seek to optimize ordering costs, number of rejected units and number of late delivered units in the entire GSCM. In this paper, fuzzy goal programming (FGP) approach has been adopted to obtain compromise solution of the formulated problem by attaining the uppermost degree of each membership goal while reducing their deviational variables. Furthermore, demand has also been forecasted using exponential smoothing analysis. The model is verified on a real-time industrial case study.

This research enables DMs to analyse uncertainty scenarios in GSCM when information about different parameters are not known precisely.

The proposed model is restricted to vagueness only, however, DMs may need to consider probabilistic multi-choice scenarios also.

The proposed model is generic and can be applied for large-scale GSC environments with little modifications.

No prior attempt is made till date to present interval type-2 fuzzy sets in a multi-objective GSC environment where the DMs are at hierarchical levels. Interval type-2 fuzzy sets are considered as better ways to represent inconsistencies of human judgements, its incompleteness and imprecision more accurately and objectively. Also, crisp or deterministic forms of uncertain parameters have been obtained by taking expected value of the fuzzy parameters.

This paper aims to develop a grey decentralized bi-level multi-objective programming (MOP) model. A solution approach is also proposed for the given model. A production and transportation plan for a closed loop supply chain network under an uncertain environment and different scenarios is also developed.

In this paper, we combined grey linear programming (GLP) and fuzzy set theory to present a solution approach for the problem. The proposed model first solves the given problem using GLP. Membership functions for the decision variables under the control of the leader and for the goals are created. These membership functions are then used to generate the final solutions.

This paper provides insight for fomenting the decision-making process while providing a more flexible approach in uncertain logistics problems. The deviations of the final solution from the individual best solutions of the two levels are very little. These deviations can further be reduced by adjusting the tolerances associated with the decision variables under the control of the leader.

The proposed approach uses the concept of membership functions of linear form, and thus, requires less computational efforts while providing effective results. Most of the organizations exhibit decentralized decision-making under the presence of uncertainties. Therefore, the present study is helpful in dealing with such scenarios.

This is the first time, formulation of a decentralized bi-level multi-objective model under a grey environment is carried out as per the best knowledge of the authors. A solution approach is developed for bi-level MOP under grey uncertainty.

This chapter reviews research on multi-level organizational justice. The first half of the chapter provides the historical context for this issue, discusses organizational-level antecedents to individual-level justice perceptions (i.e., culture and organizational structure), and then focuses on the study of justice climate. A summary model depicts the justice climate findings to date and gives recommendations for future research. The second half of the chapter discusses the process of justice climate emergence. Pulling from classical bottom-up and top-down climate emergence models as well as contemporary justice theory, it outlines a theoretical model whereby individual differences and environmental characteristics interact to influence justice judgments. Through a process of information sharing, shared and unique experiences, and interactions among group members, a justice climate emerges. The chapter concludes by presenting ideas about how such a process might be empirically modeled.

The process approach to multi-level organizational behavior is based on the assumption that multi-level organizational behavior is processual in nature. This article defines group and organizational processes and their representation as process frameworks. Both functional and inclusional classes of levels exist, each of which has at least five categories of levels. All ten categories are special cases of process frameworks. This article provides examples of each category level, which it uses to illustrate new models of organizational work, extended models of interdependence, a new typology of theories based on their levels of processes, and a new tool for survey research called knobby analyses. After explaining the basic idea of knobby analysis, the article briefly describes the processual theory of the organizational hologram, the use of linear programming, and causal-chain analysis to provide multi-level explanations of employee opinion data. These ideas are embodied in conducting a strategic organizational diagnosis, which is the first stage of organizational design. Organizational design encompasses multiple stages, each of which itself involves multiple, multi-level phenomena and analyses. The basic point is that the processual nature of multi-level organizational phenomena gives more hope for improvements in theory building and their application if one uses the process approach rather than a variable approach.

For many of the current leadership theories, models, and approaches, the answer to the question posed in the title, “Is leadership more than ‘I like my boss’?,” is “no,” as there appears to be a hierarchy of leadership concepts with Liking of the leader as the primary dimension or general factor foundation. There are then secondary dimensions or specific sub-factors of liking of Relationship Leadership and Task Leadership; and subsequently, tertiary dimensions or actual sub-sub-factors that comprise the numerous leadership views as well as their operationalizations (e.g., via surveys). There are, however, some leadership views that go beyond simply liking of the leader and liking of relationship leadership and task leadership. For these, which involve explicit levels of analysis formulations, often beyond the leader, or are multi-level in nature, the answer to the title question is “yes.” We clarify and discuss these various “no” and “yes” leadership views and implications of our work for future research and personnel and human resources management practice.

Sport participation is not exclusively determined by individual socio‐demographic factors (micro level) since infrastructure factors such as the availability of sport facilities and sport programmes (macro level) can also play a role in this regard. The purpose of this paper is to provide evidence for these determinants of sport participation using multi‐level analyses.

A survey among the resident population in the city of Munich was carried out in 2008 (n=11,715). Furthermore, secondary data on the available sport infrastructure in every urban district of Munich (n=25) were collected. Multi‐level analyses were conducted to find the micro and macro level determinants of sport participation.

The results show that aside from micro level factors, the availability of swimming pools and parks is especially important for residents’ sport activity. Moreover, sport activity in non‐profit sport clubs can be enhanced by both a good supply of sport programmes offered by sport clubs as well as a poor supply of programmes from commercial sport providers and the municipality.

Multi‐level analyses can be recommended for future research on sport participation. The use of GIS data would be fruitful in this regard.

It can be recommended that municipalities invest in the construction of swimming pools and parks.

The paper shows that multi‐level analyses are a relatively new method of analysis for research on sport participation and that they represent the most suitable approach for analysing multi‐level data.

The purpose of this paper is to develop an Internet of Things (IoT)-based framework for supply chain (SC) performance measurement and real-time decision alignment. The aims of the proposed model are to optimize the performance indicator based on integrated supply chain operations reference metrics.

The SC multi-dimensional structure is modeled by multi-objective optimization methods. The operational presented model considers important SC features thoroughly such as multi-echelons, several suppliers, several manufacturers and several products during multiple periods. A multi-objective mathematical programming model is then developed to yield the operational decisions with Pareto efficient performance values and solved using a well-known meta-heuristic algorithm, i.e., non-dominated sorting genetic algorithm II. Afterward, Technique for Order of Preference by Similarity to Ideal Solution method is used to determine the best operational solution based on the strategic decision maker’s idea.

This paper proposes a dynamic integrated solution for three main problems: strategic decisions in high level, operational decisions in low level and alignment of these two decision levels.

The authors propose a human intelligence-based process for high level decision and machine intelligence-based decision support systems for low level decision using a novel approach. High level and low level decisions are aligned by a machine intelligence model as well. The presented framework is based on change detection, event driven planning and real-time decision alignment.

“Multi-Level Issues in Strategy and Methods” is Volume 4 of Research in Multi-Level Issues, an annual series that provides an outlet for the discussion of multi-level problems and solutions across a variety of fields of study. Using a scientific debate format of a key scholarly essay followed by two commentaries and a rebuttal, we present, in this series, theoretical work, significant empirical studies, methodological developments, analytical techniques, and philosophical treatments to advance the field of multi-level studies, regardless of disciplinary perspective.