Risk assessment and relationship management: practical approach to supply chain risk management

The literature suggests the need for incorporating the risk construct into the measurement of organisational performance, although few examples are available as to how this might be undertaken in relation to supply chains. A conceptual framework for the development of performance and risk management within the supply chain is evolved from the literature and empirical evidence. The twin levels of dyadic performance/risk management and the management of a portfolio of performance/risks is addressed, employing Agency Theory to guide the analysis. The empirical evidence relates to the downstream management of dealerships by a large multinational organisation. Propositions are derived from the analysis relating to the issues and mechanisms that may be employed to effectively manage a portfolio of supply chain performance and risks

Unobserved Heterogeneity: Evidence and Implications for SMEs' Hedging Behavior

Financial research indicates that several firm characteristics are related to the use of derivatives. Less attention has been paid to the role of the characteristics of managers, which are particularly important when studying derivative usage of small and medium sized enterprises (SMEs). In this paper we focus on the influence of manager's level of education, the manager's decision-making unit, and the fundamental determinants of risk management - managerial risk attitude and managerial risk perception - on SMEs' commodity derivative usage. In empirical studies to date, the heterogeneity of derivative users has been neglected. We propose a generalized mixture regression model that estimates the relationship between commodity derivative usage and a set of explanatory variables across segments of an industry. Accounting for unobserved heterogeneity reveals that segments of the industry have different determinants of derivative use. Moreover, the heterogeneity at the segment level appears to mask significant effects at the aggregate level, most notably the effects of risk attitude and risk perception.Marketing,

Quality of Information in Mobile Crowdsensing: Survey and Research Challenges

Smartphones have become the most pervasive devices in people's lives, and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as accelerometer, gyroscope, microphone, and camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this paper, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the current state-of-the-art on the topic. We also outline novel research challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN

‘Cold pies, warm beer, and misspent youth’: Acculturation strategies mediate ethnic self-identification and marginalization in first and second-generation Australian migrant youth from South-East Asia.

The literature on migrants and social adjustment in Australia has been limited, with theories on acculturation surpassing empirical knowledge. Additionally, most research in this arena has centered on biosocial correlates of adult migrant activity; few Australian based studies have investigated empirically the impact of acculturation strategies on familial and structural marginalization among migrant youth. Using the underpinning constructs of biculturalism across multiple domains, this thesis examines how ethnic self-identification and self-esteem are mediated by the adoption of bicultural (culturally integrated) or culturally separated strategies of adjustment, and how this in turn may relate to negative adjustment outcomes such as alienating migrant youth from their families (familial marginalization) and from salient social/governance structures (structural marginalization) in their lives. This proposed relationship is articulated in a hypothesized 6-factor model relating the constructs of: Self-Esteem, Ethnic Identity, Cultural Integration, Cultural Separation, Familial Marginalization, and Structural Marginalization. The robustness of the relationship between these constructs is then further tested using a scale of self-reported antisocial behaviour. The proposed mediation model is tested across 330 first and second-generation youth migrants from South-East Asia using structural equation modeling (SEM) and multiple-group analyses. The measurement model was evaluated using a series of confirmatory factor analyses to assess the factor structure of each of the 6 latent constructs examined for both first- and second-generation migrant youth samples: Congeneric (1-factor) models were tested separately for each construct, and configural and measurement equivalence across generations was assessed. The full structural model was then estimated and tested for factorial equivalence and multi-group invariance across generation 1 and 2 cohorts using both aggregate and individual item scores. Results from this arm of the study indicate that the hypothesized multi-group model for familial and structural marginalization is well fitting across generation 1 and 2 migrants, and that significant differences exist in the relationship between independent, mediating and outcome variables when comparing generation 1 and 2 cohorts. Results from the second arm of the study exploring the prediction of antisocial behaviour from the proposed model of cultural and social adjustment indicate that self-esteem, familial marginalization, and structural marginalization added significantly to the prediction of antisocial behaviour for the generation 1 cohort, while only structural marginalization significantly predicted antisocial behaviour for the generation 2 cohort. In terms of descriptive data, this study also reports frequency and correlational statistics obtained from preliminary means-testing procedures. This study contributes to work in the field of migrant adjustment by adopting a multidimensional approach to defining and examining the constructs of ethnic identity and acculturation, and by exploring how these constructs interact to predict experiences of marginalization and antisocial behaviour in South-East Asian youth. More globally, this has implications for how cultural identity and socialization practices may be shaped in a range of settings to which young migrants may become exposed (e.g. schools, refugee detention centers, offender rehabilitation programs) to ameliorate the risk of marginalization and criminalization

Efficient operation of recharging infrastructure for the accommodation of electric vehicles: a demand driven approach

Large deployment and adoption of electric vehicles in the forthcoming years can have significant environmental impact, like mitigation of climate change and reduction of traffic-induced air pollutants. At the same time, it can strain power network operations, demanding effective load management strategies to deal with induced charging demand. One of the biggest challenges is the complexity that electric vehicle (EV) recharging adds to the power system and the inability of the existing grid to cope with the extra burden. Charging coordination should provide individual EV drivers with their requested energy amount and at the same time, it should optimise the allocation of charging events in order to avoid disruptions at the electricity distribution level. This problem could be solved with the introduction of an intermediate agent, known as the aggregator or the charging service provider (CSP). Considering out-of-home charging infrastructure, an additional role for the CSP would be to maximise revenue for parking operators. This thesis contributes to the wider literature of electro-mobility and its effects on power networks with the introduction of a choice-based revenue management method. This approach explicitly treats charging demand since it allows the integration of a decentralised control method with a discrete choice model that captures the preferences of EV drivers. The sensitivities to the joint charging/parking attributes that characterise the demand side have been estimated with EV-PLACE, an online administered stated preference survey. The choice-modelling framework assesses simultaneously out-of-home charging behaviour with scheduling and parking decisions. Also, survey participants are presented with objective probabilities for fluctuations in future prices so that their response to dynamic pricing is investigated. Empirical estimates provide insights into the value that individuals place to the various attributes of the services that are offered by the CSP. The optimisation of operations for recharging infrastructure is evaluated with SOCSim, a micro-simulation framework that is based on activity patterns of London residents. Sensitivity analyses are performed to examine the structural properties of the model and its benefits compared to an uncontrolled scenario are highlighted. The application proposed in this research is practice-ready and recommendations are given to CSPs for its full-scale implementation.Open Acces

Perception of competition : A measurement of competition from the perspective of the firm

In this report, we study competition from a cognitive psychology, marketing and strategic management perspective and hope to contribute to the notion of competition and competitive processes. In addition, we propose a new method to measure competition that is based on these more psychological insights.��

Police Organizational Performance In The State Of Florida:confirmatory Analysis Of The Relationship Of The Environment And Design Structure to Performance

To date, police organizations have not been rigorously analyzed by organizational scholars and most analysis of these organizations has been captured through a single construct. The purpose of this study is to develop confirmatory police organizational analysis by validating a multi-dimensional conceptual framework that explains the relationships among three constructs: environmental constraints, the design structures of police organizations, and organizational performance indicators. The modeling is deeply rooted in contingency theory, and the influence of isomorphism and institutional theory on the covariance structure model are investigated. One hundred and thirteen local police organizations from the State of Florida are included in this non-experimental, cross-sectional study to determine the direct effect of the environmental constraints on the performance of police organizations, the indirect effect of environmental constraints on the performance of police organizations via the organizational design structure of police organizations, and the direct affect of organizational design structure on performance of police organizations. For the first time, structural equation modeling and data envelopment analysis are used together to confirm the effects of the environment on police organization structure and performance. The results indicate that environmental social economic disparity indicators have a large positive effect on police resources and a medium effect on police efficiency. Propensity of crime indicators has a large negative effect on police resources, and population density has a small to medium negative effect on crime clearance. Structure has a much smaller effect on performance than the environment. The results of the efficiency analysis revealed unexpected findings. Three of the top five largest police organizations in the study scored maximum efficiency. The cause of this unexpected result is explained and confirmed in the covariance model. The study methodology and results enhances the understanding of the relationship among the constructs while subjecting environmental and police organizational data to two comprehensive analytical techniques. The policy implications and practical contributions of the study provide new knowledge and information to organizational management of police organizations. Furthermore, the study establishes a new approach to police organizational analysis and police services management research called Police Services Management Research (PSMR) that encompasses a variety of disciplines with a primary responsibility of theory building and the selection of theoretical framework

A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics

In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to make talent-related decisions in a quantitative manner. Indeed, the recent development of Big Data and Artificial Intelligence (AI) techniques have revolutionized human resource management. The availability of large-scale talent and management-related data provides unparalleled opportunities for business leaders to comprehend organizational behaviors and gain tangible knowledge from a data science perspective, which in turn delivers intelligence for real-time decision-making and effective talent management at work for their organizations. In the last decade, talent analytics has emerged as a promising field in applied data science for human resource management, garnering significant attention from AI communities and inspiring numerous research efforts. To this end, we present an up-to-date and comprehensive survey on AI technologies used for talent analytics in the field of human resource management. Specifically, we first provide the background knowledge of talent analytics and categorize various pertinent data. Subsequently, we offer a comprehensive taxonomy of relevant research efforts, categorized based on three distinct application-driven scenarios: talent management, organization management, and labor market analysis. In conclusion, we summarize the open challenges and potential prospects for future research directions in the domain of AI-driven talent analytics.Comment: 30 pages, 15 figure