Panel studies of new venture creation: a methods-focused review and suggestions for future research

Longitudinal panel studies of large, random samples of business start-ups captured at the pre-operational stage allow researchers to address core issues for entrepreneurship research, namely, the processes of creation of new business ventures as well as their antecedents and outcomes. Here, we perform a methods-orientated review of all 83 journal articles that have used this type of data set, our purpose being to assist users of current data sets as well as designers of new projects in making the best use of this innovative research approach. Our review reveals a number of methods issues that are largely particular to this type of research. We conclude that amidst exemplary contributions, much of the reviewed research has not adequately managed these methods challenges, nor has it made use of the full potential of this new research approach. Specifically, we identify and suggest remedies for context-specific and interrelated methods challenges relating to sample definition, choice of level of analysis, operationalization and conceptualization, use of longitudinal data and dealing with various types of problematic heterogeneity. In addition, we note that future research can make further strides towards full utilization of the advantages of the research approach through better matching (from either direction) between theories and the phenomena captured in the data, and by addressing some under-explored research questions for which the approach may be particularly fruitful.

Object identification based on 3D spatial models of construction sites

On obstacle-cluttered construction sites, understanding the motion characteristics of objects is important for anticipating collisions and preventing accidents. This study investigates algorithms for object identification applications that can be used by heavy equipment operators to effectively monitor congested local environment. The proposed framework contains algorithms for three-dimensional spatial modeling and image matching that are based on 3D images scanned by a high-frame rate range sensor. The preliminary results show that an occupancy grid spatial modeling algorithm can successfully build the most pertinent spatial information, and that an image matching algorithm is best able to identify which objects are in the scanned scene.

A forensic investigation of single human hair fibres using FTIR-ATR spectroscopy and chemometrics

Human hair fibres are ubiquitous in nature and are found frequently at crime scenes often as a result of exchange between the perpetrator, victim and/or the surroundings according to Locard's Principle. Therefore, hair fibre evidence can provide important information for crime investigation. For human hair evidence, the current forensic methods of analysis rely on comparisons of either hair morphology by microscopic examination or nuclear and mitochondrial DNA analyses. Unfortunately in some instances the utilisation of microscopy and DNA analyses are difficult and often not feasible. This dissertation is arguably the first comprehensive investigation aimed to compare, classify and identify the single human scalp hair fibres with the aid of FTIR-ATR spectroscopy in a forensic context. Spectra were collected from the hair of 66 subjects of Asian, Caucasian and African (i.e. African-type). The fibres ranged from untreated to variously mildly and heavily cosmetically treated hairs. The collected spectra reflected the physical and chemical nature of a hair from the near-surface particularly, the cuticle layer. In total, 550 spectra were acquired and processed to construct a relatively large database. To assist with the interpretation of the complex spectra from various types of human hair, Derivative Spectroscopy and Chemometric methods such as Principal Component Analysis (PCA), Fuzzy Clustering (FC) and Multi-Criteria Decision Making (MCDM) program; Preference Ranking Organisation Method for Enrichment Evaluation (PROMETHEE) and Geometrical Analysis for Interactive Aid (GAIA); were utilised. FTIR-ATR spectroscopy had two important advantages over to previous methods: (i) sample throughput and spectral collection were significantly improved (no physical flattening or microscope manipulations), and (ii) given the recent advances in FTIR-ATR instrument portability, there is real potential to transfer this work.s findings seamlessly to on-field applications. The "raw" spectra, spectral subtractions and second derivative spectra were compared to demonstrate the subtle differences in human hair. SEM images were used as corroborative evidence to demonstrate the surface topography of hair. It indicated that the condition of the cuticle surface could be of three types: untreated, mildly treated and treated hair. Extensive studies of potential spectral band regions responsible for matching and discrimination of various types of hair samples suggested the 1690-1500 cm^-1 IR spectral region was to be preferred in comparison with the commonly used 1750-800 cm^-1. The principal reason was the presence of the highly variable spectral profiles of cystine oxidation products (1200-1000 cm^-1), which contributed significantly to spectral scatter and hence, poor hair sample matching. In the preferred 1690-1500 cm^-1 region, conformational changes in the keratin protein attributed to the α-helical to β-sheet transitions in the Amide I and Amide II vibrations and played a significant role in matching and discrimination of the spectra and hence, the hair fibre samples. For gender comparison, the Amide II band is significant for differentiation. The results illustrated that the male hair spectra exhibit a more intense β-sheet vibration in the Amide II band at approximately 1511 cm^-1 whilst the female hair spectra displayed more intense α-helical vibration at 1520-1515cm^-1. In terms of chemical composition, female hair spectra exhibit greater intensity of the amino acid tryptophan (1554 cm^-1), aspartic and glutamic acid (1577 cm^-1). It was also observed that for the separation of samples based on racial differences, untreated Caucasian hair was discriminated from Asian hair as a result of having higher levels of the amino acid cystine and cysteic acid. However, when mildly or chemically treated, Asian and Caucasian hair fibres are similar, whereas African-type hair fibres are different. In terms of the investigation's novel contribution to the field of forensic science, it has allowed for the development of a novel, multifaceted, methodical protocol where previously none had existed. The protocol is a systematic method to rapidly investigate unknown or questioned single human hair FTIR-ATR spectra from different genders and racial origin, including fibres of different cosmetic treatments. Unknown or questioned spectra are first separated on the basis of chemical treatment i.e. untreated, mildly treated or chemically treated, genders, and racial origin i.e. Asian, Caucasian and African-type. The methodology has the potential to complement the current forensic analysis methods of fibre evidence (i.e. Microscopy and DNA), providing information on the morphological, genetic and structural levels.

The concealed difficulties of aligning CRE decisions to corporate goals

Realisation of the importance of real estate asset strategic decision making has inspired a burgeoning corporate real estate management (CREM) literature. Much of this criticises the poor alignment between strategic business direction and the ‘enabling’ physical environment. This is based on the understanding that corporate real estate assets represent the physical resource base that supports business, and can either complement or impede that business. In the hope of resolving this problem, CRE authors advocate a deeper integration of strategic and corporate real estate decisions. However this recommendation appears to be based on a relatively simplistic theoretical approach to organization where decision-making tends to be viewed as a rationally managed event rather than a complex process. Defining decision making as an isolated event has led to an uncritical acceptance of two basic assumptions: ubiquitous, conflict-free rationality and profit maximisation. These assumptions have encouraged prescriptive solutions that clearly lack the sophistication necessary to come to grips with the complexity of the built and organizational environment. Alternatively, approaching CREM decision making from a more sophisticated perspective, such as that of the “Carnegie School”, leads to conceptualise it as a ‘process’, creating room for bounded rationality, multiple goals, intra-organizational conflict, environmental matching, uncertainty avoidance and problem searching. It is reasonable to expect that such an approach will result in a better understanding of the organizational context, which will facilitate the creation of organizational objectives, assist with the formation of strategies, and ultimately will aid decision.

Entrepreneurial discovery and exploitation processes : sequence or symbiosis?

This study examined the effect that temporal order within the entrepreneurial discovery-exploitation process has on the outcomes of venture creation. Consistent with sequential theories of discovery-exploitation, the general flow of venture creation was found to be directed from discovery toward exploitation in a random sample of nascent ventures. However, venture creation attempts which specifically follow this sequence derive poor outcomes. Moreover, simultaneous discovery-exploitation was the most prevalent temporal order observed, and venture attempts that proceed in this manner more likely become operational. These findings suggest that venture creation is a multi-scale phenomenon that is at once directional in time, and simultaneously driven by symbiotically coupled discovery and exploitation.

Developing a research method to test a new first-order decision making model for the procurement of public sector major infrastructure

Given global demand for new infrastructure, governments face substantial challenges in funding new infrastructure and simultaneously delivering Value for Money (VfM). The paper begins with an update on a key development in a new early/first-order procurement decision making model that deploys production cost/benefit theory and theories concerning transaction costs from the New Institutional Economics, in order to identify a procurement mode that is likely to deliver the best ratio of production costs and transaction costs to production benefits, and therefore deliver superior VfM relative to alternative procurement modes. In doing so, the new procurement model is also able to address the uncertainty concerning the relative merits of Public-Private Partnerships (PPP) and non-PPP procurement approaches. The main aim of the paper is to develop competition as a dependent variable/proxy for VfM and a hypothesis (overarching proposition), as well as developing a research method to test the new procurement model. Competition reflects both production costs and benefits (absolute level of competition) and transaction costs (level of realised competition) and is a key proxy for VfM. Using competition as a proxy for VfM, the overarching proposition is given as: When the actual procurement mode matches the predicted (theoretical) procurement mode (informed by the new procurement model), then actual competition is expected to match potential competition (based on actual capacity). To collect data to test this proposition, the research method that is developed in this paper combines a survey and case study approach. More specifically, data collection instruments for the surveys to collect data on actual procurement, actual competition and potential competition are outlined. Finally, plans for analysing this survey data are briefly mentioned, along with noting the planned use of analytical pattern matching in deploying the new procurement model and in order to develop the predicted (theoretical) procurement mode.

Facial expression recognition using facial movement features

Facial expression is an important channel for human communication and can be applied in many real applications. One critical step for facial expression recognition (FER) is to accurately extract emotional features. Current approaches on FER in static images have not fully considered and utilized the features of facial element and muscle movements, which represent static and dynamic, as well as geometric and appearance characteristics of facial expressions. This paper proposes an approach to solve this limitation using ‘salient’ distance features, which are obtained by extracting patch-based 3D Gabor features, selecting the ‘salient’ patches, and performing patch matching operations. The experimental results demonstrate high correct recognition rate (CRR), significant performance improvements due to the consideration of facial element and muscle movements, promising results under face registration errors, and fast processing time. The comparison with the state-of-the-art performance confirms that the proposed approach achieves the highest CRR on the JAFFE database and is among the top performers on the Cohn-Kanade (CK) database.

Improving the performance of facial expression recognition using dynamic, subtle and regional features

Human facial expression is a complex process characterized of dynamic, subtle and regional emotional features. State-of-the-art approaches on facial expression recognition (FER) have not fully utilized this kind of features to improve the recognition performance. This paper proposes an approach to overcome this limitation using patch-based ‘salient’ Gabor features. A set of 3D patches are extracted to represent the subtle and regional features, and then inputted into patch matching operations for capturing the dynamic features. Experimental results show a significant performance improvement of the proposed approach due to the use of the dynamic features. Performance comparison with pervious work also confirms that the proposed approach achieves the highest CRR reported to date on the JAFFE database and a top-level performance on the Cohn-Kanade (CK) database.

Wide baseline correspondence extraction beyond local features

Robust, affine covariant, feature extractors provide a means to extract correspondences between images captured by widely separated cameras. Advances in wide baseline correspondence extraction require looking beyond the robust feature extraction and matching approach. This study examines new techniques of extracting correspondences that take advantage of information contained in affine feature matches. Methods of improving the accuracy of a set of putative matches, eliminating incorrect matches and extracting large numbers of additional correspondences are explored. It is assumed that knowledge of the camera geometry is not available and not immediately recoverable. The new techniques are evaluated by means of an epipolar geometry estimation task. It is shown that these methods enable the computation of camera geometry in many cases where existing feature extractors cannot produce sufficient numbers of accurate correspondences.

Information extraction from web services : a comparison of Tokenisation algorithms

Most web service discovery systems use keyword-based search algorithms and, although partially successful, sometimes fail to satisfy some users information needs. This has given rise to several semantics-based approaches that look to go beyond simple attribute matching and try to capture the semantics of services. However, the results reported in the literature vary and in many cases are worse than the results obtained by keyword-based systems. We believe the accuracy of the mechanisms used to extract tokens from the non-natural language sections of WSDL files directly affects the performance of these techniques, because some of them can be more sensitive to noise. In this paper three existing tokenization algorithms are evaluated and a new algorithm that outperforms all the algorithms found in the literature is introduced.

Determining operational measures from multi-camera surveillance systems using soft biometrics

CCTV and surveillance networks are increasingly being used for operational as well as security tasks. One emerging area of technology that lends itself to operational analytics is soft biometrics. Soft biometrics can be used to describe a person and detect them throughout a sparse multi-camera network. This enables them to be used to perform tasks such as determining the time taken to get from point to point, and the paths taken through an environment by detecting and matching people across disjoint views. However, in a busy environment where there are 100's if not 1000's of people such as an airport, attempting to monitor everyone is highly unrealistic. In this paper we propose an average soft biometric, that can be used to identity people who look distinct, and are thus suitable for monitoring through a large, sparse camera network. We demonstrate how an average soft biometric can be used to identify unique people to calculate operational measures such as the time taken to travel from point to point.

Expert science teacher's notion of scientific literacy

This paper will report on the way expert science teachers’ conceive of scientific literacy in their classrooms, the values related to scientific literacy they hold and how this conception and the underpinning values affect their teaching practice. Three perceived expert science teachers who teach both at senior and middle school levels and across the range of sub-disciplines (one senior biology, one senior chemistry and one senior physics) were interviewed about their understanding of scientific literacy and how this influenced their teaching practice. The three teachers were video recorded teaching a junior science class and a senior science class. The data were analysed to identify values that underpin their conceptions of science and science education. The analysis focussed on the matching of the verbalised conceptions and values with their practice of teaching science. This paper will report on these data.

Error analysis and attitude observability of a monocular GPS/visual odometry integrated navigation filter

In this paper, we present a method for the recovery of position and absolute attitude (including pitch, roll and yaw) using a novel fusion of monocular Visual Odometry and GPS measurements in a similar manner to a classic loosely-coupled GPS/INS error state navigation filter. The proposed filter does not require additional restrictions or assumptions such as platform-specific dynamics, map-matching, feature-tracking, visual loop-closing, gravity vector or additional sensors such as an IMU or magnetic compass. An observability analysis of the proposed filter is performed, showing that the scale factor, position and attitude errors are fully observable under acceleration that is non-parallel to velocity vector in the navigation frame. The observability properties of the proposed filter are demonstrated using numerical simulations. We conclude the article with an implementation of the proposed filter using real flight data collected from a Cessna 172 equipped with a downwards-looking camera and GPS, showing the feasibility of the algorithm in real-world conditions.

Negative determinant of Hessian features

Local image feature extractors that select local maxima of the determinant of Hessian function have been shown to perform well and are widely used. This paper introduces the negative local minima of the determinant of Hessian function for local feature extraction. The properties and scale-space behaviour of these features are examined and found to be desirable for feature extraction. It is shown how this new feature type can be implemented along with the existing local maxima approach at negligible extra processing cost. Applications to affine covariant feature extraction and sub-pixel precise corner extraction are demonstrated. Experimental results indicate that the new corner detector is more robust to image blur and noise than existing methods. It is also accurate for a broader range of corner geometries. An affine covariant feature extractor is implemented by combining the minima of the determinant of Hessian with existing scale and shape adaptation methods. This extractor can be implemented along side the existing Hessian maxima extractor simply by finding both minima and maxima during the initial extraction stage. The minima features increase the number of correspondences by two to four fold. The additional minima features are very distinct from the maxima features in descriptor space and do not make the matching process more ambiguous.

Towards an understanding of teacher judgement in the context of social moderation

Social moderation involves teachers gathering together to discuss their judgements of the quality of student work and to reach agreement regarding the standard awarded. This qualitative study conducted over a three-year period investigated the social practice of moderation and the influence on teachers’ judgements of students work. An initial survey of teachers’ understandings of moderation and standards, pre-interviews of teachers who participated in the moderation meetings, observations of these meetings with a particular focus on one teacher (focus teachers) comprised the data collection methods. Data analysis involved organising, matching, coding, identifying patterns and themes using a constant comparative method. Socio-cultural theories of learning and assessment underpinned the approach to data analysis and proved helpful in explaining the diverse influences on teachers’ judgements beyond the task criteria, and the progressive development of shared understandings through engaging in professional discussions of students’ work. The study revealed that the process is not clear and linear and is influenced by factors such as the representation of the standards and the knowledge base of the teachers.