The role of emotional labour and role stress on burnout and psychological strain in high contact service employees

This study examines the relationships between job demands (in the form of role stressors and emotional management) and employee burnout amongst high contact service employees. Employees in customer facing roles are frequently required to manage overwhelming, conflicting or ambiguous demands, which they may feel ill-equipped to handle. Simultaneously, they must manage the emotions they display towards customers, suppressing some, and expressing others, be they genuine or contrived. If the in-role effort required of employees exceeds their inherent capacity to cope, burnout may result. Burnout, in turn, can have serious detrimental consequences for the psychological well being of employees. We find that both emotional management and role stressors impact burnout. We also confirm that burnout predicts psychological strain. In line with the Job Demands and Resources Model, we examine the mitigating impact of perceived support on these relationships but do not find a significant mitigating impact.

Racing for radical innovation:how motorsport companies harness network diversity for discontinuous innovation

The motorsport industry is a significant part of the UK economy. According to industry estimates approximately 4,500 companies are involved in the UK Motorsport and Performance Engineering Industry and its wide-ranging support activities. The industry has an annual turnover of £6.0 billion, and contributes £3.6 billion worth of exports. The Motorsport Industry Association estimates that the support side of the sector alone "involving events management, public relations, marketing, sponsorship and a host of other support functions" accounts for approximately £1.7 billion of the yearly industry total. And in terms of employment, UK Motorsport supports 38,500 full and part-time jobs, including 25,000 engineers.

Communities and emerging semantics in semantic link network:discovery and learning

The World Wide Web provides plentiful contents for Web-based learning, but its hyperlink-based architecture connects Web resources for browsing freely rather than for effective learning. To support effective learning, an e-learning system should be able to discover and make use of the semantic communities and the emerging semantic relations in a dynamic complex network of learning resources. Previous graph-based community discovery approaches are limited in ability to discover semantic communities. This paper first suggests the Semantic Link Network (SLN), a loosely coupled semantic data model that can semantically link resources and derive out implicit semantic links according to a set of relational reasoning rules. By studying the intrinsic relationship between semantic communities and the semantic space of SLN, approaches to discovering reasoning-constraint, rule-constraint, and classification-constraint semantic communities are proposed. Further, the approaches, principles, and strategies for discovering emerging semantics in dynamic SLNs are studied. The basic laws of the semantic link network motion are revealed for the first time. An e-learning environment incorporating the proposed approaches, principles, and strategies to support effective discovery and learning is suggested.

A multimodal investigation of dynamic face perception using functional magnetic resonance imaging and magnetoencephalography

Motion is an important aspect of face perception that has been largely neglected to date. Many of the established findings are based on studies that use static facial images, which do not reflect the unique temporal dynamics available from seeing a moving face. In the present thesis a set of naturalistic dynamic facial emotional expressions was purposely created and used to investigate the neural structures involved in the perception of dynamic facial expressions of emotion, with both functional Magnetic Resonance Imaging (fMRI) and Magnetoencephalography (MEG). Through fMRI and connectivity analysis, a dynamic face perception network was identified, which is demonstrated to extend the distributed neural system for face perception (Haxby et al.,2000). Measures of effective connectivity between these regions revealed that dynamic facial stimuli were associated with specific increases in connectivity between early visual regions, such as inferior occipital gyri and superior temporal sulci, along with coupling between superior temporal sulci and amygdalae, as well as with inferior frontal gyri. MEG and Synthetic Aperture Magnetometry (SAM) were used to examine the spatiotemporal profile of neurophysiological activity within this dynamic face perception network. SAM analysis revealed a number of regions showing differential activation to dynamic versus static faces in the distributed face network, characterised by decreases in cortical oscillatory power in the beta band, which were spatially coincident with those regions that were previously identified with fMRI. These findings support the presence of a distributed network of cortical regions that mediate the perception of dynamic facial expressions, with the fMRI data providing information on the spatial co-ordinates paralleled by the MEG data, which indicate the temporal dynamics within this network. This integrated multimodal approach offers both excellent spatial and temporal resolution, thereby providing an opportunity to explore dynamic brain activity and connectivity during face processing.

Dynamic causal modeling of load-dependent modulation of effective connectivity within the verbal working memory network

Neuroimaging studies have consistently shown that working memory (WM) tasks engage a distributed neural network that primarily includes the dorsolateral prefrontal cortex, the parietal cortex, and the anterior cingulate cortex. The current challenge is to provide a mechanistic account of the changes observed in regional activity. To achieve this, we characterized neuroplastic responses in effective connectivity between these regions at increasing WM loads using dynamic causal modeling of functional magnetic resonance imaging data obtained from healthy individuals during a verbal n-back task. Our data demonstrate that increasing memory load was associated with (a) right-hemisphere dominance, (b) increasing forward (i.e., posterior to anterior) effective connectivity within the WM network, and (c) reduction in individual variability in WM network architecture resulting in the right-hemisphere forward model reaching an exceedance probability of 99% in the most demanding condition. Our results provide direct empirical support that task difficulty, in our case WM load, is a significant moderator of short-term plasticity, complementing existing theories of task-related reduction in variability in neural networks. Hum Brain Mapp, 2013. © 2013 Wiley Periodicals, Inc.

Organizational learning and emotion:constructing collective meaning in support of strategic themes

Missing in the organizational learning literature is an integrative framework that reflects the emotional as well as the cognitive dynamics involved. Here, we take a step in this direction by focusing in depth over time (five years) on a selected organization which manufactures electronic equipment for the office industry. Drawing on personal construct theory, we define organizational learning as the collective re-construal of meaning in the direction of strategically significant themes. We suggest that emotions arise as members reflect on progress or lack of progress in achieving organizational learning. Our evidence suggests that invalidation - where organizational learning fails to correspond with expectations - gives rise to anxiety and frustration, while validation - where organizational learning is aligned with or exceeds expectations - evokes comfort or excitement. Our work aims to capture the key emotions involved as organizational learning proceeds. © The Author(s) 2012.

QoS guarantee with adaptive transmit power and message rate control for DSRC vehicle network based road safety applications

Quality of services (QoS) support is critical for dedicated short range communications (DSRC) vehicle networks based collaborative road safety applications. In this paper we propose an adaptive power and message rate control method for DSRC vehicle networks at road intersections. The design objective is to provide high availability and low latency channels for high priority emergency safety applications while maximizing channel utilization for low priority routine safety applications. In this method an offline simulation based approach is used to find out the best possible configurations of transmit power and message rate for given numbers of vehicles in the network. The identified best configurations are then used online by roadside access points (AP) according to estimated number of vehicles. Simulation results show that this adaptive method significantly outperforms a fixed control method. © 2011 Springer-Verlag.

Disabling a family? Emotional dilemmas experienced in becoming a parent of a child with learning disabilities

Becoming the parent of a child diagnosed with learning disabilities can have a dramatic impact. Chrissie Rogers, the author of this article, is both a lecturer in education studies at Keele University and the mother of a daughter who has learning disabilities. She argues here that the pressures on mothers to produce ‘perfect’ babies and to meet all their needs are immense. These pressures arise from both internalised norms and societal expectations and, in the face of these pressures, parents may feel shock, loss and disappointment. These feelings may lead, in turn, to denial, anxiety and conflict affecting both the parents and the professionals involved with the family. Drawing on a series of in-depth interviews and personal narratives, Chrissie Rogers makes a powerful case for the importance of support, whether that support is formal or informal. She suggests that, without the right levels of support and understanding, having a child with a diagnosis of learning disability can disable the whole family.

Networks of Evolutionary Processors (NEP) as Decision Support Systems

This paper presents the application of Networks of Evolutionary Processors to Decision Support Systems, precisely Knowledge-Driven DSS. Symbolic information and rule-based behavior in Networks of Evolutionary Processors turn out to be a great tool to obtain decisions based on objects present in the network. The non-deterministic and massive parallel way of operation results in NP-problem solving in linear time. A working NEP example is shown.

Indirect ties in knowledge networks:a social network analysis with ordered weighted averaging operators

This PhD thesis analyses networks of knowledge flows, focusing on the role of indirect ties in the knowledge transfer, knowledge accumulation and knowledge creation process. It extends and improves existing methods for mapping networks of knowledge flows in two different applications and contributes to two stream of research. To support the underlying idea of this thesis, which is finding an alternative method to rank indirect network ties to shed a new light on the dynamics of knowledge transfer, we apply Ordered Weighted Averaging (OWA) to two different network contexts. Knowledge flows in patent citation networks and a company supply chain network are analysed using Social Network Analysis (SNA) and the OWA operator. The OWA is used here for the first time (i) to rank indirect citations in patent networks, providing new insight into their role in transferring knowledge among network nodes; and to analyse a long chain of patent generations along 13 years; (ii) to rank indirect relations in a company supply chain network, to shed light on the role of indirectly connected individuals involved in the knowledge transfer and creation processes and to contribute to the literature on knowledge management in a supply chain. In doing so, indirect ties are measured and their role as means of knowledge transfer is shown. Thus, this thesis represents a first attempt to bridge the OWA and SNA fields and to show that the two methods can be used together to enrich the understanding of the role of indirectly connected nodes in a network. More specifically, the OWA scores enrich our understanding of knowledge evolution over time within complex networks. Future research can show the usefulness of OWA operator in different complex networks, such as the on-line social networks that consists of thousand of nodes.

A decision support system for waste collection management and its potential improvement with Radio-Frequency Identification Technology (RFID)

Due to vigorous globalisation and product proliferation in recent years, more waste has been produced by the soaring manufacturing activities. This has contributed to the significant need for an efficient waste management system to ensure, with all efforts, the waste is properly treated for recycling or disposed. This paper presents a Decision Support System (DSS) framework, based on Constraint Logic Programming (CLP), for the collection management of industrial waste (of all kinds) and discusses the potential employment of Radio-Frequency Identification Technology (RFID) to improve several critical procedures involved in managing waste collection. This paper also demonstrates a widely distributed and semi-structured network of waste producing enterprises (e.g. manufacturers) and waste processing enterprises (i.e. waste recycling/treatment stations) improving their operations planning by means of using the proposed DSS. The potential RFID applications to update and validate information in a continuous manner to bring value-added benefits to the waste collection business are also presented. © 2012 Inderscience Enterprises Ltd.

An empirical examination of emotional and cognitive responses to threat appeals

Advertising and other forms of communications are often used by government bodies, non-government organisations, and other institutions to try to influence the population to either a) reduce some form of harmful behaviour (e.g. smoking, drunk- driving) or b) increase some more healthy behaviour (e.g. eating healthily). It is common for these messages to be predicated on the chances of some negative event occurring if the individual does not either a) stop the harmful behaviour, or b) start / increase the healthy behaviour. This design of communication is referred to by many names in the relevant literature, but for the purposes of this thesis, will be termed a ‘threat appeal’. Despite their widespread use in the public sphere, and concerted academic interest since the 1950s, the effectiveness of threat appeals in delivering their objective remains unclear in many ways. In a detailed, chronological and thematic examination of the literature, two assumptions are uncovered that have either been upheld despite little evidence to support them, or received limited attention at all, in the literature. Specifically, a) that threat appeal characteristics can be conflated with their intended responses, and b) that a threat appeal always and necessarily evokes a fear response in the subject. A detailed examination of these assumptions underpins this thesis. The intention is to take as a point of departure the equivocality of empirical results, and deliver a novel approach with the objective of reducing the confusion that is evident in existing work. More specifically, the present thesis frames cognitive and emotional responses to threat appeals as part of a decision about future behaviour. To further develop theory, a conceptual framework is presented that outlines the role of anticipated and anticipatory emotions, alongside subjective probabilities, elaboration and immediate visceral emotions, resultant from manipulation of the intrinsic message characteristics of a threat appeal (namely, message direction, message frame and graphic image). In doing so, the spectrum of relevant literature is surveyed, and used to develop a theoretical model which serves to integrate key strands of theory into a coherent model. In particular, the emotional and cognitive responses to the threat appeal manipulations are hypothesised to influence behaviour intentions and expectations pertaining to future behaviour. Using data from a randomised experiment with a sample of 681 participants, the conceptual model was tested using analysis of covariance. The results for the conceptual framework were encouraging overall, and also with regard to the individual hypotheses. In particular, empirical results showed clearly that emotional responses to the intrinsic message characteristics are not restricted to fear, and that different responses to threat appeals were clearly attributed to specific intrinsic message characteristics. In addition, the inclusion of anticipated emotions alongside cognitive appraisals in the framework generated interesting results. Specifically, immediate emotions did not influence key response variables related to future behaviour, in support of questioning the assumption of the prominent role of fear in the response process that is so prevalent in existing literature. The findings, theoretical and practical implications, limitations and directions for future research are discussed.

Semantic enabled sensor network design

Wireless Sensor Network (WSN) systems have become more and more popular in our modern life. They have been widely used in many areas, such as smart homes/buildings, context-aware devices, military applications, etc. Despite the increasing usage, there is a lack of formal description and automated verification for WSN system design. In this paper, we present an approach to support the rigorous verification of WSN modeling using the Semantic Web technology We use Web Ontology Language (OWL) and Semantic Web Rule Language (SWRL) to define a meta-ontology for the modeling of WSN systems. Furthermore, we apply ontology reasoners to perform automated verification on customized WSN models and their instances. We demonstrate and evaluate our approach through a Light Control System (LCS) as the case study.

Understanding inter-firm networks and types of innovation in SMEs:a social network perspective

Innovation is one of the key drivers for gaining competitive advantages in any firms. Understanding knowledge transfer through inter-firm networks and its effects on types of innovation in SMEs is very important in improving SMEs innovation. This study examines relationships between characteristics of inter-firm knowledge transfer networks and types of innovation in SMEs. To achieve this, social network perspective is adopted to understand inter-firm knowledge transfer networks and its impact on innovation by investigating how and to what extend ego network characteristics are affecting types of innovation. Therefore, managers can develop the firms'network according to their strategies and requirements. First, a conceptual model and research hypotheses are proposed to establish the possible relationship between network properties and types of innovation. Three aspects of ego network are identified and adopted for hypotheses development: 1) structural properties which address the potential for resources and the context for the flow of resources, 2) relational properties which reflect the quality of resource flows, and 3) nodal properties which are about quality and variety of resources and capabilities of the ego partners. A questionnaire has been designed based on the hypotheses. Second, semistructured interviews with managers of five SMEs have been carried out, and a thematic qualitative analysis of these interviews has been performed. The interviews helped to revise the questionnaire and provided preliminary evidence to support the hypotheses. Insights from the preliminary investigation also helped to develop research plan for the next stage of this research.

Advanced predictive-analysis-based decision support for collaborative logistics networks

Purpose – The purpose of this paper is to examine challenges and potential of big data in heterogeneous business networks and relate these to an implemented logistics solution. Design/methodology/approach – The paper establishes an overview of challenges and opportunities of current significance in the area of big data, specifically in the context of transparency and processes in heterogeneous enterprise networks. Within this context, the paper presents how existing components and purpose-driven research were combined for a solution implemented in a nationwide network for less-than-truckload consignments. Findings – Aside from providing an extended overview of today’s big data situation, the findings have shown that technical means and methods available today can comprise a feasible process transparency solution in a large heterogeneous network where legacy practices, reporting lags and incomplete data exist, yet processes are sensitive to inadequate policy changes. Practical implications – The means introduced in the paper were found to be of utility value in improving process efficiency, transparency and planning in logistics networks. The particular system design choices in the presented solution allow an incremental introduction or evolution of resource handling practices, incorporating existing fragmentary, unstructured or tacit knowledge of experienced personnel into the theoretically founded overall concept. Originality/value – The paper extends previous high-level view on the potential of big data, and presents new applied research and development results in a logistics application.