Understanding corporate governance, strategic management and firm performance : as evidenced from the boardroom : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Management at Massey University, Manawatu, New Zealand

Researchers with an interest in corporate performance have increasingly shifted their attention over recent decades from the study of the chief executive to the board of directors. A large body of knowledge has now been published, including correlations between variables of interest, theories, conceptual models and rich descriptions of normative practice. However, substantive evidence to explain how boards actually exert influence over firm performance from the boardroom is yet to appear. That the board’s ability to exert such influence has not been adequately described—let alone explained in any detail—is a significant knowledge gap in the literature, one to which this research seeks to contribute. The aim of this research is to investigate corporate governance, strategic management and firm performance from the perspective of the boardroom. A longitudinal multiple-case study approach was used. Primary data was collected from direct observations of the boards of two large highgrowth companies in New Zealand. Secondary data sources included interviews with the chairmen and chief executives, and board and company documents. An iterative approach to analysis was utilised from which a deep understanding of board involvement in strategic management was developed. The analysis revealed insights leading to the development of two models—a collaborative form of board–management interaction, and a mechanism-based model of the governance–performance relationship. The research makes contributions to governance research by extending specific early and largely normative contributions. The board’s active engagement in strategic management (especially strategy development, strategic decision-making and monitoring of strategy implementation) appears to be significant. This is achieved via the harmonious activation of five underlying attributes. While no explicit or predictable relationship between board interventions and subsequent firm performance was discovered, the findings provide insight into the contingent nature of the board’s ability to exert influence from and beyond the boardroom

Let Me Give You My Card : A Study of Evolving Business Protocols in the Information Age

Non peer reviewedFinal Accepted Versio

Accelerated Field‐Cycling MRI using the Keyhole Technique

Peer reviewedPublisher PD

Construction and performance of a novel capture-mark-release moth trap

Mark-recapture studies can provide important information about moth movement as well as habitat preference across a landscape, but to date, such studies tend to be species-specific or require labor-intensive methodologies. To address this challenge, we designed a capture-mark-release-trap (CMRT) featuring a cooling unit attached to a black light trap. The CMRT captures and incapacitates moths throughout the night until the morning, when they can be marked on-site and released. Moths captured with the CMRT during summer of 2016 had a recapture rate of 1.6%, similar to those of previous studies. Importantly, because moths are immobilized by the CMRT, they can be handled and marked with ease, reducing the opportunities to damage specimens prior to release. The CMRT trap can capture a wide array of moth species and may facilitate an increase in the monitoring of moth movement across landscapes

Noise-tolerant approximate blocking for dynamic real-time entity resolution

Entity resolution is the process of identifying records in one or multiple data sources that represent the same real-world entity. This process needs to deal with noisy data that contain for example wrong pronunciation or spelling errors. Many real world applications require rapid responses for entity queries on dynamic datasets. This brings challenges to existing approaches which are mainly aimed at the batch matching of records in static data. Locality sensitive hashing (LSH) is an approximate blocking approach that hashes objects within a certain distance into the same block with high probability. How to make approximate blocking approaches scalable to large datasets and effective for entity resolution in real-time remains an open question. Targeting this problem, we propose a noise-tolerant approximate blocking approach to index records based on their distance ranges using LSH and sorting trees within large sized hash blocks. Experiments conducted on both synthetic and real-world datasets show the effectiveness of the proposed approach