Sara M Denize

Competing interests : the challenge to collaboration in the public sector

Purpose – Managers working within organizations that are part of public–private collaborations comment on their “diabolical” nature and seek guidance as to their administration. Set in an organization involved in a public–private “service delivery contract”, the purpose of this paper is to report research into the collaboration and challenges experienced within the organization. It also seeks to consider to the significance of narrative and story‐telling in understanding these complexities.Design/methodology/approach – Data were collected using depth interviews and observation. The emerging discourse was analyzed using a machine‐based, lexigraphic tool to provide a framework for interpretive story analysis. The significance and value of each approach are illustrated as are the effective synergies between them.Findings – The paper identifies a number of different competing interests, including the needs of the clients vs the governmentally imposed goals and performance metrics of the organization. The limi...

Enterprise university as a digital ecosystem: Visual analysis of academic collaboration

The concept of “enterprise university” [1] emerged to denote the new kind of higher education institution that resembles many elements of corporate governance. It has a tighter interaction and integration in the environment, as has to take in account market factors, such as student fee incomes, soft budget allocations for special initiatives, including research funding, risk factors and others. Hence an enterprise university can be viewed as a digital ecosystem. An essential part for the survival of such system is collaboration between academics. This paper presents a visual analytics methodology for analysis of academic collaboration that is geared towards real-time performance. We introduce a simple collaboration index in order to depict slices of prominent collaborators and investigate the networked clusters them. We demonstrate the work on an integrated data set from real-world University. The technology is a key component in the extended business intelligence support for the senior executive teams in these new type of digital ecosystems.

Supporting Strategic Decision Making in an Enterprise University Through Detecting Patterns of Academic Collaboration

Collaborative networks are a topic, broadly researched from several perspectives, including the social network analysis (SNA). The organisations take advantage from the results of SNA to determine collaborative channels, information fusion through such channels and key participants or groups in the network. This work is focused on multi-facet analysis of academic collaboration, as it has been identified as a key factor in success and growth in the global educational market. The data sets include integrated data about different aspects of academic collaboration, including co-authorship, co-participation, co-supervision and other related data. We explore the concept of interestingness and its application to the field of network mining. Composing an appropriate interpretable set of interestingness measures will benefit decision makers in organisations in taking specific actions depending on the patterns in these measures. In this study we focus on interesting measures such as unexpectedness for academic networks and a collaborative score.

Researching The Structures and Processes of Collaborative Academic Networks

This paper considers collaborative structures in academic networks. Network analysis reveals surprising connections not reflected in institutions’ formal structures. Semantic analysis of stories of collaboration shows collaboration as critical to research and production of cutting-edge knowledge. Discussion of the value of multiple methods in researching collaboration concludes the paper.

Using Case-Based Research For Agent-Based Modelling

Abstract This chapter outlines a method for developing simulation code from case-based data using narrative sequence analysis. This analytical method allows researchers to systematically specify the ‘real-world’ behaviours and causal mechanisms that describe the research problem and translate this mechanism into simulation code. An illustrative example of the process used for code development from case-based data is detailed using a well-documented case of photovoltaic innovation. Narrative sequence analysis is used to analyse case data. Micro-sequences are identified and simplified. Each micro-sequence is presented first in pseudo-code and then in simulation code. This chapter demonstrates the coding process using Netlogo code. Narrative sequence analysis provides a rigorous and systematic approach to identifying the underlying mechanisms to be described when building simulation models. This analytical technique also provides necessary and sufficient information to write simulation code. This chapter addresses a current gap in the methodology literature by including case data within agent-based model building processes. It benefits B2B marketing researchers by outlining guiding processes and principles in the use of case-based data to build simulation models.

Perceptions, Attitudes and Luxury Brands

The Australian market for luxury brands has been largely overlooked, but in the first decade of the new millennium it has quietly developed into a success story for the luxury industry with “international luxury executives consistently remarking that Australia is a surprisingly sophisticated market“ [23].