Social Media Strategies in the Retail Sector: Analysis and Recommendations for Three Multi-National Retailers

During the last twenty years (1995-2015), the world of commerce has expanded beyond the traditional brick-and-mortar high street to a global shop front accessible to billions of users via the Worldwide Web (WWW). Consumers are now using the web to immerse themselves in virtual shop fronts, using Social Media (SM) to communicate and share product ideas with friends and family. Retail organisations recognise the need to develop and adapt their strategies to respond to the increasing use of SM. New goals must be set in order to identify how companies will integrate social media into current practices. This research aims to suggest an advisable and comprehensive SM strategy for companies operating in the global retail sector, based on an exploratory analysis of three multi-national retail organisations' existing SM strategies. This will be assessed in conjunction with a broader investigation into social media in the retail industry. From this, a strategy will be devised to improve internal and external communication as well as knowledge management through the use of social media. Findings suggest that the use of SM within the retail industry has dramatically improved collaboration and communication processes for organisations as they are now able to converse better with stakeholders and the tools are relatively simple to integrate and implement as they benefit one another.

A Social Networking Strategy for Improving Knowledge Management and Communication in the Travel Industry

In today’s technology-driven marketplace, the adoption and management of corporate and customer-facing Social Networking Sites (SNs) is often viewed as a key success factor for Travel Industry (TI) organisations. Knowledge management and the sharing of expertise and experiences through communication between internal and external stakeholders via social networks is an activity which TI organisations are aiming to exploit in order to improve the open sharing, retrieval, organisation and leveraging of knowledge. Through a study of currently-available literature relating to social networking adoption within the TI and a case study analysis of corporate social networking practices at three multi-national TI organisations (British Airways, Thomas Cook and Marriott Hotels), it may be observed that correlations exist between the development of social networking and the processes TI organisations now use to manage knowledge. We explore how these companies are currently utilizing SNs to improve knowledge management practices inside and outside of their organisational boundaries. From our analysis, lessons may emerge as to how TI companies are gaining competitive advantage through the use of social networking; a proposed strategy is identified to determine how TI organisations may make best use of social networks.

Service Quality in Arts Events: Operations Management Strategies for Effective Delivery

Recent debate on the quality of arts events has concentrated on the requirement to deliver against a complex range of political, social, and cultural criteria with an emphasis on the external partnerships that are forged. Yet those aspects of quality over which event organizers have more direct control have been accorded minor examination. The authors believe that operational effectiveness is key to service quality in the cultural context, and seek to demonstrate that a balanced consideration of both process and product is vital to fully deliver quality arts events. This article identifies areas of emergent research and practice and focuses on issues in the front-of-house environment where the breakdown of service quality is a real concern, using the experience of one UK not-for-profit arts organization as a case study to illustrate potential management responses.

A Two-Stage Online Approach for Collaborative Multi-Agent Planning under Uncertainty

In a team of multiple agents, the pursuance of a common goal is a defining characteristic. Since agents may have different capabilities, and effects of actions may be uncertain, a common goal can generally only be achieved through a careful cooperation between the different agents. In this work, we propose a novel two-stage planner that combines online planning at both team level and individual level through a subgoal delegation scheme. The proposal brings the advantages of online planning approaches to the multi-agent setting. A number of modifications are made to a classical UCT approximate algorithm to (i) adapt it to the application domains considered, (ii) reduce the branching factor in the underlying search process, and (iii) effectively manage uncertain information of action effects by using information fusion mechanisms. The proposed online multi-agent planner reduces the cost of planning and decreases the temporal cost of reaching a goal, while significantly increasing the chance of success of achieving the common goal. 

HRM and Innovation: A Multi-level Organizational Learning Perspective

Drawing on the 4I organizational learning framework (Crossan et al., 1999), this article develops a model to explain the multi-level and cross-level relationships between HRM practices and innovation. Individual, team, and organizational level learning stocks are theorized to explain how HRM practices affect innovation at a given level. Feed-forward and feedback learning flows explain how cross-level effects of HRM practices on innovation take place. In addition, we propose that HRM practices fostering individual, team, and organizational level learning should form a coherent system to facilitate the emergence of innovation. The article is concluded with discussions on its contributions and potential future research directions.

Methods, Models & Systems for Dealing with Some Business, Management & Engineering Problems

This keynote presentation will report some of our research work and experience on the development and applications of relevant methods, models, systems and simulation techniques in support of different types and various levels of decision making for business, management and engineering. In particular, the following topics will be covered. Modelling, multi-agent-based simulation and analysis of the allocation management of carbon dioxide emission permits in China (Nanfeng Liu & Shuliang Li Agent-based simulation of the dynamic evolution of enterprise carbon assets (Yin Zeng & Shuliang Li) A framework & system for extracting and representing project knowledge contexts using topic models and dynamic knowledge maps: a big data perspective (Jin Xu, Zheng Li, Shuliang Li & Yanyan Zhang) Open innovation: intelligent model, social media & complex adaptive system simulation (Shuliang Li & Jim Zheng Li) A framework, model and software prototype for modelling and simulation for deshopping behaviour and how companies respond (Shawkat Rahman & Shuliang Li) Integrating multiple agents, simulation, knowledge bases and fuzzy logic for international marketing decision making (Shuliang Li & Jim Zheng Li) A Web-based hybrid intelligent system for combined conventional, digital, mobile, social media and mobile marketing strategy formulation (Shuliang Li & Jim Zheng Li) A hybrid intelligent model for Web & social media dynamics, and evolutionary and adaptive branding (Shuliang Li) A hybrid paradigm for modelling, simulation and analysis of brand virality in social media (Shuliang Li & Jim Zheng Li) Network configuration management: attack paradigms and architectures for computer network survivability (Tero Karvinen & Shuliang Li)

Team Member’s Centrality, Cohesion, Conflict, and Performance in Multi-University Geographically Distributed Project Teams

This study examined team processes and outcomes among 12 multi-university distributed project teams from 11 universities during its early and late development stages over a 14-month project period. A longitudinal model of team interaction is presented and tested at the individual level to consider the extent to which both formal and informal network connections—measured as degree centrality—relate to changes in team members’ individual perceptions of cohesion and conflict in their teams, and their individual performance as a team member over time. The study showed a negative network centrality-cohesion relationship with significant temporal patterns, indicating that as team members perceive less degree centrality in distributed project teams, they report more team cohesion during the last four months of the project. We also found that changes in team cohesion from the first three months (i.e., early development stage) to the last four months (i.e., late development stage) of the project relate positively to changes in team member performance. Although degree centrality did not relate significantly to changes in team conflict over time, a strong inverse relationship was found between changes in team conflict and cohesion, suggesting that team conflict emphasizes a different but related aspect of how individuals view their experience with the team process. Changes in team conflict, however, did not relate to changes in team member performance. Ultimately, we showed that individuals, who are less central in the network and report higher levels of team cohesion, performed better in distributed teams over time.

Accounting for the Multi-Period Impact of Service When Determining Employee Requirements for Labor Scheduling

Providing good customer service, inexpensively, is a problem commonly faced by managers of service operations. To tackle this problem, managers must do four tasks: forecast customer demand for the service; translate these forecasts into employee requirements; develop a labor schedule that provides appropriate numbers of employees at appropriate times; and control the delivery of the service in real-time. This paper focuses upon the translation of forecasts of customer demand into employee requirements. Specifically, it presents and evaluates two methods for determining desired staffing levels. One of these methods is a traditional approach to the task, while the other, by using modified customer arrival rates, offers a better means of accounting for the multi-period impact of customer service. To calculate the modified arrival rates, the latter method reduces (increases) the actual customer arrival rate for a period to account for customers who arrived in the period (in earlier periods) but have some of their service performed in subsequent periods (in the period). In an experiment simulating 13824 service delivery environments, the new method demonstrated its superiority by serving 2.74% more customers within the specified waiting time limit while using 7.57% fewer labor hours.

Is Trading Behavior Stable Across Contexts? Evidence from Style and Multi-Style Investors

In this paper we study priming of identity within the context of inherent vs. contextual financial decision making. We use a sample of individual trading accounts in equity-style funds taken from one fund family to test the hypothesis that trading styles are inherent vs. contextual. Our sample contains investors who invest either in a growth fund, a value fund, or both. We document behavioral differences between growth fund investors and value fund investors. We find that their trades depend on past returns in different ways: growth fund investors tend towards momentum trading and value fund investors tend towards contrarian trading. These differences may be due to inherent clientele characteristics, including beliefs about market prices, specific personality traits and cognitive strategies that cause them to self-select into one or the other style. We use a sample of investors that trade in both types of funds to test this proposition. Consistent with the contextual hypothesis, we find that investors who hold both types of funds trade growth fund shares differently than value fund shares.

Unifying Services Marketing and Operations with Service Experience Management

One of the pioneer firms in the leisure cruise industry embarked on a bold idea in 2000 to offer an unregimented experience unlike most cruises. Despite the appeal of the concept from a marketing perspective, the service innovation posed operational challenges, many of which continue to undermine the firm’s competitive position. Using a multi-method empirical approach and interdisciplinary views that draw on research from marketing and operations management, the authors analyze this business case to identify challenges that service firms face when services are developed and managed from siloed functional perspectives. Based on their research findings and guided by the literature, the authors derive a service-systems model to aid service planning and management. The authors further highlight a new organizational form and function for services under the domain of service experience management that is positioned as a means to unify service operations and marketing for delivering on service promises. The authors offer direction for further research on service operations systems and service experience management.

Children with Learning Disabilities and Challenging Behaviour: Relationship between Parents and Professionals

This multi-perspectival Interpretive Phenomenological Analysis (IPA) study explored how people in the ‘networks of concern’ talked about how they tried to make sense of the challenging behaviours of four children with severe learning disabilities. The study also aimed to explore what affected relationships between people. The study focussed on 4 children through interviewing their mothers, their teachers and the Camhs Learning Disability team members who were working with them. Two fathers also joined part of the interviews. All interviews were conducted separately using a semi-structured approach. IPA allowed both a consideration of the participant’s lived experiences and ‘objects of concern’ and a deconstruction of the multiple contexts of people’s lives, with a particular focus on disability. The analysis rendered five themes: the importance of love and affection, the difficulties, and the differences of living with a challenging child, the importance of being able to make sense of the challenges and the value of good relationships between people. Findings were interpreted through the lens of CMM (Coordinated Management of Meaning), which facilitated a systemic deconstruction and reconstruction of the findings. The research found that making sense of the challenges was a key concern for parents. Sharing meanings were important for people’s relationships with each other, including employing diagnostic and behavioural narratives. The importance of context is also highlighted including a consideration of how societal views of disability have an influence on people in the ‘network of concern’ around the child. A range of systemic approaches, methods and techniques are suggested as one way of improving services to these children and their families. It is suggested that adopting a ‘both/and’ position is important in such work - both applying evidence based approaches and being alert to and exploring the different ways people try and make sense of the children’s challenges. Implications for practice included helping professionals be alert to their constructions and professional narratives, slowing the pace with families, staying close to the concerns of families and addressing network issues.

The Multi-Faceted Experience of Empathy in Intellectual Disability Settings: An IPA Study

This research thesis explored the concept of empathy. The specific purpose was to further understand the idea of empathy in relation to the experience of male support workers who provide residential care to adults with intellectual disabilities (ID) and challenging behaviour. The thesis aimed to provide some insights into how support workers develop and extract meaning from their experiences of relationships with clients and the impact of this on their own self-care, namely, self-compassion. Since personal accounts of experience were required, a qualitative methodology was employed, Interpretative Phenomenological Analysis (IPA) (Smith, 2004). This methodology was selected as it allows for the exploration and interpretation of idiographic lived experience and meaning making. 8 experienced support workers were interviewed using a semi structured interview. Four superordinate themes emerged from the data. These included: 1. Making sense of the others inner world; 2. Processes that enhance empathic practice; 3. Tensions and conflicts, and 4. Management of distressing feelings. Differing accounts of interpreting the needs of clients were identified which helped participants understand, make sense of their interpersonal experience and participate in their role. These included utilising academic knowledge and senses, particularly sight and hearing, which were seemingly complemented by a level of reflective practice. Additionally, to make sense of the experience of a client, they appeared to put themselves in their position, suggesting a form of empathy. Participants appeared to engage in a process of reflection on their relationships with clients, which helped them think about what they had learned about the person’s needs, moreover, this process enabled them to identify some of their own responses and feelings. However, participants seemed to struggle to recognise the occurrence or impact of distressing emotional experience and to express their feelings, possibly in response to a deep sense of responsibility and fear of transferring emotional distress to others. This dilemma of holding two potentially conflicting views of experience seemed to inhibit self-compassion. Although not specifically testing theories of empathy, from the overall findings, it could be suggested that empathy may be a dynamic, transient process that is influenced by reflexivity, values and context. The context in which participants discussed their practice, and situated within their accounts, suggested a sense of confusion and uncertainty. Consequently, it is suggested this impacted on how participants understood and related to clients, and to themselves. There were some specific implications for Counselling Psychology practice, mostly concerning training and supervision. These included recommendations for staff training and supervision, systemic organisational intervention, policy development, recommendations for revisions to models of specialist care frameworks and clinical training.

Sharing the vision: representing the matters of concern for design-led fledgling companies in Scotland

Design is being performed on an ever-increasing spectrum of complex practices arising in response to emerging markets and technologies, co-design, digital interaction, service design and cultures of innovation. This emerging notion of design has led to an expansive array of collaborative and facilitation skills to demonstrate and share how such methods can shape innovation. The meaning of these design things in practice can't be taken for granted as matters of fact, which raises a key challenge for design to represent its role through the contradictory nature of matters of concern. This paper explores an innovative, object-oriented approach within the field of design research, visually combining an actor-network theory framework with situational analysis, to report on the role of design for fledgling companies in Scotland, established and funded through the knowledge exchange hub Design in Action (DiA). Key findings and visual maps are presented from reflective discussions with actors from a selection of the businesses within DiA's portfolio. The suggestion is that any notions of strategic value, of engendering meaningful change, of sharing the vision of design, through design things, should be grounded in the reflexive interpretations of matters of concern that emerge.

BUILDING EFFICIENT AND COST-EFFECTIVE CLOUD-BASED BIG DATA MANAGEMENT SYSTEMS

In today’s big data world, data is being produced in massive volumes, at great velocity and from a variety of different sources such as mobile devices, sensors, a plethora of small devices hooked to the internet (Internet of Things), social networks, communication networks and many others. Interactive querying and large-scale analytics are being increasingly used to derive value out of this big data. A large portion of this data is being stored and processed in the Cloud due the several advantages provided by the Cloud such as scalability, elasticity, availability, low cost of ownership and the overall economies of scale. There is thus, a growing need for large-scale cloud-based data management systems that can support real-time ingest, storage and processing of large volumes of heterogeneous data. However, in the pay-as-you-go Cloud environment, the cost of analytics can grow linearly with the time and resources required. Reducing the cost of data analytics in the Cloud thus remains a primary challenge. In my dissertation research, I have focused on building efficient and cost-effective cloud-based data management systems for different application domains that are predominant in cloud computing environments. In the first part of my dissertation, I address the problem of reducing the cost of transactional workloads on relational databases to support database-as-a-service in the Cloud. The primary challenges in supporting such workloads include choosing how to partition the data across a large number of machines, minimizing the number of distributed transactions, providing high data availability, and tolerating failures gracefully. I have designed, built and evaluated SWORD, an end-to-end scalable online transaction processing system, that utilizes workload-aware data placement and replication to minimize the number of distributed transactions that incorporates a suite of novel techniques to significantly reduce the overheads incurred both during the initial placement of data, and during query execution at runtime. In the second part of my dissertation, I focus on sampling-based progressive analytics as a means to reduce the cost of data analytics in the relational domain. Sampling has been traditionally used by data scientists to get progressive answers to complex analytical tasks over large volumes of data. Typically, this involves manually extracting samples of increasing data size (progressive samples) for exploratory querying. This provides the data scientists with user control, repeatable semantics, and result provenance. However, such solutions result in tedious workflows that preclude the reuse of work across samples. On the other hand, existing approximate query processing systems report early results, but do not offer the above benefits for complex ad-hoc queries. I propose a new progressive data-parallel computation framework, NOW!, that provides support for progressive analytics over big data. In particular, NOW! enables progressive relational (SQL) query support in the Cloud using unique progress semantics that allow efficient and deterministic query processing over samples providing meaningful early results and provenance to data scientists. NOW! enables the provision of early results using significantly fewer resources thereby enabling a substantial reduction in the cost incurred during such analytics. Finally, I propose NSCALE, a system for efficient and cost-effective complex analytics on large-scale graph-structured data in the Cloud. The system is based on the key observation that a wide range of complex analysis tasks over graph data require processing and reasoning about a large number of multi-hop neighborhoods or subgraphs in the graph; examples include ego network analysis, motif counting in biological networks, finding social circles in social networks, personalized recommendations, link prediction, etc. These tasks are not well served by existing vertex-centric graph processing frameworks whose computation and execution models limit the user program to directly access the state of a single vertex, resulting in high execution overheads. Further, the lack of support for extracting the relevant portions of the graph that are of interest to an analysis task and loading it onto distributed memory leads to poor scalability. NSCALE allows users to write programs at the level of neighborhoods or subgraphs rather than at the level of vertices, and to declaratively specify the subgraphs of interest. It enables the efficient distributed execution of these neighborhood-centric complex analysis tasks over largescale graphs, while minimizing resource consumption and communication cost, thereby substantially reducing the overall cost of graph data analytics in the Cloud. The results of our extensive experimental evaluation of these prototypes with several real-world data sets and applications validate the effectiveness of our techniques which provide orders-of-magnitude reductions in the overheads of distributed data querying and analysis in the Cloud.