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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.

This paper aims to explore the impact of a recent earthquake activity on house prices and their spatial distribution in the Istanbul housing market.

The paper uses a multi-level approach within an event study framework to model changes in the pattern of house prices in Istanbul. The model allows the isolation of the effects of earthquake risk and explores the differential impact in different submarkets in two study periods – one before (2007) and one after (2012) recent earthquake activity in the Van region, which although in Eastern Turkey served to alter the perceptions of risk through the wider geographic region.

The analysis shows that there are variations in the size of price discounts in submarkets resulting from the differential influence of a recent earthquake activity on perceived risk of damage. The model results show that the spatial impacts of these changes are not transmitted evenly across the study area. Rather it is clear that submarkets at the cheaper end of the market have proportionately larger negative impacts on real estate values.

The robustness of the models would be enhanced by the addition of further spatial levels and larger data sets.

The methods introduced in this study can be used by real estate agents, valuers and insurance companies to help them more accurately assess the likely impacts of changes in the perceived risk of earthquake activity (or other environmental events such as flooding) on the formation of house prices in different market segments.

The application of these methods is intended to inform a fairer approach to setting insurance premiums and a better basis for determining policy interventions and public investment designed to mitigate potential earthquake risk.

The paper represents an attempt to develop a novel extension of the standard use of hedonic models in event studies to investigate the impact of natural disasters on real estate values. The value of the approach is that it is able to better capture the granularity of the spatial effects of environmental events than the standard approach.

The purpose of this paper is to develop and test a multi-level healthcare service quality (HSQ) model in Jakarta, Indonesia.

The research used a quantitative research method. Data were collected via a survey with questionnaire. The respondents are 154 patients of a healthcare institution in Jakarta, Indonesia.

The research result shows a multi-level HSQ model. The HSQ model consists of three primary dimensions, namely, healthcare service outcome, healthcare service interaction, and healthcare service environment. Healthcare service outcome has three subdimensions, i.e. waiting time, medicine, and effectiveness. Healthcare service interaction has three dimensions, namely, soft interaction, medical personnel expertise, and hard interaction. Healthcare service environment has two dimensions, which are equipment condition and ambient condition.

This research was only conducted in one healthcare institution in Jakarta, Indonesia. The data collection using convenience sampling method as well as the use of small sample size caused the limitation of the research results in representing across the customer of the healthcare institution. This study can be replicated with larger sample size and involving more healthcare institutions in order to examine the stability of the HSQ model.

Healthcare institution’s managers can use the HSQ model to monitor, measure, and improve their service quality.

There is a lack of research that develops and tests HSQ model based on multi-level approach in the context of developing country. This paper has fulfilled the gap.

This paper aims to develop a house price forecasting model to investigate the impact of neighbourhood effect on property value.

Multi-level modelling (MLM) method is used to develop the house price forecasting models. The neighbourhood effects, that is, socio-economic conditions that exist in various locations, are included in this study. Data from the local government area in Greater Sydney, Australia, has been collected to test the developed model.

Results show that the multi-level models can account for the neighbourhood effects and provide accurate forecasting results.

It is believed that the impacts on specific households may be different because of the price differences in various geographic areas. The “neighbourhood” is an important consideration in housing purchase decisions.

While increasing housing supply provisions to match the housing demand, governments may consider improving the quality of neighbourhood conditions such as transportation, surrounding environment and public space security.

The demand and supply of housing in different locations have not behaved uniformly over time, that is, they demonstrate spatial heterogeneity. The use of MLM extends the standard hedonic model to incorporate physical characteristics and socio-economic variables to estimate dwelling prices.

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 examine multiple learning cycles across a UK housebuilder organization following changes made to their quality management routine at the organizational level, through to subsequent understanding and enactment at the level of the individuals involved.

This study uses a qualitative case study methodology based on an analysis of six-weeks of participant observation, semi-structured ethnographic interviews and documentation within three of the organization’s regional offices. Through an abductive process, it draws on gathered data and extant literature to develop a multi-level learning model.

Four levels of learning cycles are observed within the model: individual, team (within which inter-organizational relationships nest), region and organization. Three inter-related factors are identified as influencing feed-forward and feedback across the levels: time, communication and trust. The impact of these levels and factors on the process of learning is conceptualized through the metaphor of coupling and decoupling and discussed using examples from housing development projects.

While previous models of organizational learning highlight important multi-level interaction effects, they do not explore how the different levels of learning synchronize over time for learning to move between them. This paper addresses this gap by shedding important light on how layers of learning synchronize and why and when this can occur within multi-level organizations.

The paper introduces a multi-level model to reduce prejudice through supporting diversity, equity, and inclusion (DEI) at the institutional, organizational, and individual levels. The purpose of the model is to provide theoretically undergirded pathways to explain how societal events calling for systemic changes in DEI practices can engage and inculcate such systemic changes in organizations and institutions.

The model draws upon macro-level (i.e. institutional theory and institutional logics) theories from sociology and strategic management, meso-level theories from leadership and strategy, and micro-level organizational behavior and human resource management theories.

Resting on open systems theory (Katz and Kahn, 1966) as a backdrop, the authors address how institutional changes result in organizational level changes driving multi-level outcomes of increased DEI, reduced prejudice in work-related settings, and performance gains. The authors suggest the recursive nature of the model can trigger institutional level shifts in logics or result in isomorphic pressures that further change organizational fields and organizations.

The contribution rests in a multi-level examination to help understand how environmental pressures can motivate organizations to enact broader changes related to social justice, specifically increasing efforts in DEI inside the operational aspects of the organization. By enacting these changes, the authors suggest the resultant positive changes in organizations will enhance culture and performance, creating isomorphic pressure for industry wide changes that may begin to move the needle on addressing systemic problems that feed prejudicial behavior in the workplace.

The purpose of this study is to examine how five different multi-level governance (MLG) models affect place branding (PB) performance in Saudi Arabia.

In hierarchical administrative systems, central governments exert control on PB, influencing its effectiveness. While PB as such is widely studied, the effect of MLG on PB performance in centralized administrative systems remains understudied. The study is approached as a multiple case study of nine cities.

The study reveals that different MLG models indeed affect PB performance differently. Direct access to central leadership and resources boosts branding performance, while privatization promotes flexibility with similarly positive effects. Study findings, furthermore, show that some cities are considered too big to fail. Cities such as Riyadh and Neom are of prime importance and receive plenty of resources and leadership attention, while others are considered peripheral, are under-resourced and branding performance suffers accordingly. Emerging differences in PB performance associated with different MLG models are thus likely to deepen the gap between urban economic winners and losers.

This paper introduces five MLG models based on the actors involved in PB, their interactions and their access to resources. For each model, this paper assesses other factors which may influence the effectiveness of PB as well, such as access to the national leadership and staff capacity. This research thereby adds to the literature by identifying specific factors within MLG models influencing PB performance in hierarchical administrative systems.

The purpose of this paper is to empirically explore the underlying mechanisms through which empowerment affects organizational outcomes via a multi-dimensional model. Specifically, the paper suggests that empowerment climate (EC) is positively related to psychological empowerment (PE) and the effect of PE on the outcome variable of job satisfaction is mediated by justice perceptions and role clarity.

In total, 765 employees at the executive level across 26 distinct groups in a large manufacturing firm participated in the survey. Multi-level analyses based on both hierarchical linear modeling and multi-level structural equation modeling techniques were utilized to test the hypotheses.

This research demonstrated that EC significantly affected PE and that perceptions of justice and role clarity mediated the relationship between PE and job satisfaction. Additional analyses demonstrated that EC did not significantly affect organizational outcomes above and beyond PE, justice, and role clarity.

The current study suggests that managers concerned with increasing employee perceptions of justice may be able to do so by increasing perceptions of employee empowerment. As only individual employee perceptions of empowerment related directly to organizational outcomes, this demonstrates the importance for managers to understand perceptions of employees.

Exploring these mechanisms will strengthen the knowledge regarding how empowerment works to enhance organizational effectiveness. This will enable practitioners to better determine how and when empowerment will be most effective.

The purpose of this paper is to focus on the relationship between college and university faculty collegiality, conceptualized as a set of prosocial behaviors, and job satisfaction.

A multi-level structural equation model was developed to examine the relationship between faculty collegiality and job satisfaction at the individual and institutional levels, the effects of gender and race/ethnicity, the effect of institutional type (i.e. research universities vs non-research universities), and whether institutional-level perceptions of faculty collegiality and job satisfaction influence perceptions of faculty collegiality and job satisfaction at the individual level.

Faculty collegiality was highly and significantly related to job satisfaction at the individual level (0.86) and at the institutional level (0.93). At the individual level, pretenured women faculty and faculty of color indicated significantly lower levels of collegiality. At the institutional level, pretenured faculty interactions with tenured faculty colleagues were positively and significantly related to individual-level perceptions of faculty collegiality.

Study limitations include self-report data that were dependent upon accurate responses from faculty participants, and cross-sectional data. Future analyses could extend study findings by examining the influence of faculty collegiality upon such outcomes as faculty productivity and retention in future multi-level analyses.

It is recommended that interventions be undertaken to embed prosocial behaviors into faculty research, teaching, and service activities, and to foster relationships between pretenured and tenured faculty members.

This paper underscores the importance of collecting nationally representative faculty data and conducting rigorous multi-level analyses to inform higher education policy and practice.