Why, how, what for? : motivations, actions and expectations in habitual entrepreneurship

Principal topic: Is habitual entrepreneurship different? Answering this is important to the field, however there is little systematic evidence, thus far. We addresses this by examining the role experience plays at three possible points of difference: motivations, actions and expectations; and by comparing those currently in the process of starting a business with those who have recent success in business creation. Firstly, we assess the balance of opportunity versus necessity motivation, internally versus externally stimulated decision processes and future growth aspirations. Literature suggests novices are more likely motivated to nascency out of necessity, and favour a manageable business size, while habitual entrepreneurs are more likely motivated by internally stimulated or idea driven processes. Secondly, we examine actions undertaken by successful experienced founders during gestation, contrasting ‘information collection’ and ‘opportunity definition’. Drawing on prior research we expect novices more likely to have enacted ‘information search’ while habitual entrepreneurs enact ‘opportunity definition’. Thirdly, we examine perceptions of venture success, where findings on overconfidence suggest that habitual entrepreneurs expect a higher chance of success for their ventures, while inexperience leads novices to underestimate the difficulty of entrepreneurial survival. Method: Empirical evidence to test these conjectures was drawn from a screened random sample of over 1100 Australian nascent and newly started business ventures. This information was collected during 2007/8 using a telephone survey. Results and Implications: Why do habitual entrepreneurs keep coming back? Findings suggest that while the pursuit of opportunity is shared by novice and experienced entrepreneur alike, consideration of repeat entrepreneurship may be motivated by a desire for growth. While idea driven motivations might not delineate a distinction during nascency, it does seem to be a factor contributing to the success of young firms. This warrants further research. How do habitual entrepreneurs behave differently? It seems they act to clearly define market opportunities as a matter of priority during venture gestation. What effect does entrepreneurial experience have on future expectations? Clearly a sense of realism is drawn over the difficulties that might be faced, and accords more circumspect judgements of venture survival. This finding informs practitioners considering entrepreneurship for the first time.

La théorie démocratique et La Physique Théorique

Le présent essai soutient, un peu le long d'une ligne simmelienne, que la théorie démocratique peut produire des théories pratiques et universelles, comme celles développées en physique théorique. Le raisonnement qui sous-tend cet essai est de montrer que la théorie de la «démocratie de base" peut-être vrai par le faite si on la comparer à la Relative Spécifique d’Einstein portant spécifiquement sur les paramètres de symétrie, l'unification, la simplicité et l'utilité. Ces paramètres sont ce qui fait qu’une théorie en physique comme ont la rencontre s’adapte non seulement aux connaissances actuelles, mais aussi de produire des chemins vers l'essai (application). Comme la théorie de la «démocratie de base » peut satisfaire ces mêmes paramètres, il pourrait trancher le débat relatif à la définition de la démocratie. Ceci sera d'abord soutenu pour discuter de ce qui est la théorie de la «démocratie de base» et pourquoi cela diffère des travaux précédents, en deuxième lieu, en expliquant les paramètres choisis (comme pour quoi ceux-ci et pas à d'autres confirment ou échouent les théories) et, troisièmement, en comparant comment la relativité et la théorie de la «démocratie de base » peut correspondre aux paramètres.

Toward a more robust facial expression recognition in occluded images using randomly sampled Gabor based templates

Occlusion is a big challenge for facial expression recognition (FER) in real-world situations. Previous FER efforts to address occlusion suffer from loss of appearance features and are largely limited to a few occlusion types and single testing strategy. This paper presents a robust approach for FER in occluded images and addresses these issues. A set of Gabor based templates is extracted from images in the gallery using a Monte Carlo algorithm. These templates are converted into distance features using template matching. The resulting feature vectors are robust to occlusion. Occluded eyes and mouth regions and randomly places occlusion patches are used for testing. Two testing strategies analyze the effects of these occlusions on the overall recognition performance as well as each facial expression. Experimental results on the Cohn-Kanade database confirm the high robustness of our approach and provide useful insights about the effects of occlusion on FER. Performance is also compared with previous approaches.

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.

Evaluation of texture and geometry for dimensional facial expression recognition

Facial expression recognition (FER) algorithms mainly focus on classification into a small discrete set of emotions or representation of emotions using facial action units (AUs). Dimensional representation of emotions as continuous values in an arousal-valence space is relatively less investigated. It is not fully known whether fusion of geometric and texture features will result in better dimensional representation of spontaneous emotions. Moreover, the performance of many previously proposed approaches to dimensional representation has not been evaluated thoroughly on publicly available databases. To address these limitations, this paper presents an evaluation framework for dimensional representation of spontaneous facial expressions using texture and geometric features. SIFT, Gabor and LBP features are extracted around facial fiducial points and fused with FAP distance features. The CFS algorithm is adopted for discriminative texture feature selection. Experimental results evaluated on the publicly accessible NVIE database demonstrate that fusion of texture and geometry does not lead to a much better performance than using texture alone, but does result in a significant performance improvement over geometry alone. LBP features perform the best when fused with geometric features. Distributions of arousal and valence for different emotions obtained via the feature extraction process are compared with those obtained from subjective ground truth values assigned by viewers. Predicted valence is found to have a more similar distribution to ground truth than arousal in terms of covariance or Bhattacharya distance, but it shows a greater distance between the means.

Introduction : queering paradigms, interrogating agendas

The introduction to the first volume of Queering Paradigms suggested that to queer a paradigm is to of fer a challenge to “the hetero/homonormative and gender binarist assumptions of any given academic discourse.” As queer subjects defy the “seduction of identity by exclusion,” and celebrate “the whole potential of sexuality and gender fluidity and diversity,” any attempt to understand them through the lenses offered by standard discourse is destined to fail (Scherer 2010: 2). “Queer” is not simply a synonym for Lesbian, Gay, Bisexual, Transgender, Intersex and Questioning/Queer (LGBTIQ) subjects, as common use might suggest. Rather, it ought to be read as a reference to all who defy being pigeon-holed, pushed to the margins, or being pressured to adopt common social narratives regarding gender and sexuality.

Discovering the best feature extraction and selection algorithms for spontaneous facial expression recognition

Feature extraction and selection are critical processes in developing facial expression recognition (FER) systems. While many algorithms have been proposed for these processes, direct comparison between texture, geometry and their fusion, as well as between multiple selection algorithms has not been found for spontaneous FER. This paper addresses this issue by proposing a unified framework for a comparative study on the widely used texture (LBP, Gabor and SIFT) and geometric (FAP) features, using Adaboost, mRMR and SVM feature selection algorithms. Our experiments on the Feedtum and NVIE databases demonstrate the benefits of fusing geometric and texture features, where SIFT+FAP shows the best performance, while mRMR outperforms Adaboost and SVM. In terms of computational time, LBP and Gabor perform better than SIFT. The optimal combination of SIFT+FAP+mRMR also exhibits a state-of-the-art performance.

Towards spontaneous facial expression recognition in real-world video

Facial expression is an important channel of human social communication. Facial expression recognition (FER) aims to perceive and understand emotional states of humans based on information in the face. Building robust and high performance FER systems that can work in real-world video is still a challenging task, due to the various unpredictable facial variations and complicated exterior environmental conditions, as well as the difficulty of choosing a suitable type of feature descriptor for extracting discriminative facial information. Facial variations caused by factors such as pose, age, gender, race and occlusion, can exert profound influence on the robustness, while a suitable feature descriptor largely determines the performance. Most present attention on FER has been paid to addressing variations in pose and illumination. No approach has been reported on handling face localization errors and relatively few on overcoming facial occlusions, although the significant impact of these two variations on the performance has been proved and highlighted in many previous studies. Many texture and geometric features have been previously proposed for FER. However, few comparison studies have been conducted to explore the performance differences between different features and examine the performance improvement arisen from fusion of texture and geometry, especially on data with spontaneous emotions. The majority of existing approaches are evaluated on databases with posed or induced facial expressions collected in laboratory environments, whereas little attention has been paid on recognizing naturalistic facial expressions on real-world data. This thesis investigates techniques for building robust and high performance FER systems based on a number of established feature sets. It comprises of contributions towards three main objectives: (1) Robustness to face localization errors and facial occlusions. An approach is proposed to handle face localization errors and facial occlusions using Gabor based templates. Template extraction algorithms are designed to collect a pool of local template features and template matching is then performed to covert these templates into distances, which are robust to localization errors and occlusions. (2) Improvement of performance through feature comparison, selection and fusion. A comparative framework is presented to compare the performance between different features and different feature selection algorithms, and examine the performance improvement arising from fusion of texture and geometry. The framework is evaluated for both discrete and dimensional expression recognition on spontaneous data. (3) Evaluation of performance in the context of real-world applications. A system is selected and applied into discriminating posed versus spontaneous expressions and recognizing naturalistic facial expressions. A database is collected from real-world recordings and is used to explore feature differences between standard database images and real-world images, as well as between real-world images and real-world video frames. The performance evaluations are based on the JAFFE, CK, Feedtum, NVIE, Semaine and self-collected QUT databases. The results demonstrate high robustness of the proposed approach to the simulated localization errors and occlusions. Texture and geometry have different contributions to the performance of discrete and dimensional expression recognition, as well as posed versus spontaneous emotion discrimination. These investigations provide useful insights into enhancing robustness and achieving high performance of FER systems, and putting them into real-world applications.

Engagement, persistence, progress and success as theoretically distinct aspects of business creation processes

Starting from the vantage point that explaining success at creating a venture should be the unique contribution—or at least one unique contribution—of entrepreneurship research, we argue that this success construct has not yet been adequately defined an operationalized. We thus offer suggestions for more precise conceptualization and measurement of this central construct. Rather than regarding various success proxies used in prior research as poor operationalizations of success we argue that they represent other important aspects of the venture creation process: engagement, persistence and progress. We hold that in order to attain a better understanding of venture creation these constructs also need to be theoretically defined. Further, their respective drivers need to be theorized and tested separately. We suggest theoretical definitions of each. We then develop and test hypotheses concerning how human capital, venture idea novelty and business planning has different impact on the different assessments of the process represented by engagement, persistence, progress and success. The results largely confirm the stated hypotheses, suggesting that the conceptual and empirical approach we are suggesting is a path towards improved understanding of the central entrepreneurship phenomenon of new venture creation.

Business planning, idea change, flexibility and performance: the best of both worlds?

The relationships between business planning and performance have divided the entrepreneurship research community for decades (Brinckmann et al, 2010). One side of this debate is the assumption that business plans may lock the firm in a specific direction early on, impede the firm to adapt to the changing market conditions (Dencker et al., 2009) and eventually, cause escalation of commitments by introducing rigidity (Vesper, 1993). Conversely, feedback received from the production and presentation of business plans may also lead the firm to take corrective actions. However, the mechanisms underlying the relationships between changes in business ideas, business plans and the performance of nascent firms are still largely unknown. While too many business idea changes may confuse stakeholders, exhaust the firm’s resources and hinder the undergoing legitimization process, some flexibility during the early stages of the venture may be beneficial to cope with the uncertainties surrounding new venture creation (Knight, 1921; March, 1982; Stinchcombe, 1965; Weick, 1979). Previous research has emphasized adaptability and flexibility as key success factors through effectual logic and interaction with the market (Sarasvathy, 2001; 2007) or improvisation and trial-and-error (Miner et al, 2001). However, those studies did not specifically investigate the role of business planning. Our objective is to reconcile those seemingly opposing views (flexibility versus rigidity) by undertaking a more fine-grained analysis at the relationships between business planning and changes in business ideas on a large longitudinal sample of nascent firms.

The underlying rationale for planning: do nascent and young firms do what they say they do?

Previous studies investigating the relationships between business planning and performance have led to inconclusive results (Brinckmann et al., 2010; Delmar & Shane, 2003; Frese, 2009; Gruber, 2007; Honig & Karlsson, 2004). Institutional theory argues that firms develop business plans as an answer to external and internal pressures to gain legitimization (Delmar & Shane, 2004) and funding from different stakeholders (Karlsson & Honig, 2009). Action theory suggests that planning will pave the new venture creation journey by providing milestones and a program to implement (Frese, 2009). However, studies with an institutional perspective imply that nascent firms are either conforming to or looking for the benefits of these external or internal pressures (Karlsson & Honig, 2009) while action theory assumes that the plans will be implemented (Frese, 2009). This paper attempts to (i) investigate if the intended uses of the business plans provided by nascent and young firms match their actual uses during their venture creation process and (ii) to examine how the types of uses of business plans impact the firms’ outcomes over three years.

The moderating role of learning on competitive advantage : longitudinal evidence from young firms

The question of how young firms reconcile the absence of well-established learning routines arising from the “liabilities of newness” with the “learning advantages of newness” has received scant attention in entrepreneurship. While older firms follow established learning routines and sometimes face problems in overcoming inertia, young firms with lower levels of inertia are better poised to explore, search and test unique avenues for their products and services. The process of learning and capability development as well as establishing uniqueness in their product offerings is an important part not only in the early stages of firm growth, but also in firm survival. Given their inexperience, for young firms, these learning processes are iterative and include contrasting learning loops that sometimes progress and at other times digress from initially perceived unique ideas. Such processes are embedded within capabilities that the firm develops and nurtures. Based on this premise and adopting a capabilities-based view, we examine how strategic networks and environmental knowledge affects uniqueness- mediated performance in young firms. We identify firms with digressive learning strategies based on their self-assessment of learning and compare them with other firms to demonstrate a differential effect on performance.

Client adaptiveness in entrepreneurship and its effect on sustained competitive advantage in project-orientated service firms

Important differences exist in how service firms operate in comparison with manufacturing firms (c.f. Johne & Storey, 1998; Tether, 2002). Despite these significant differences, not much is known whether these differences extrapolate to entrepreneurship in the services industry. This study seeks to address this gap by investigating how value creation occurs when project-oriented firms1 adopt client adaptiveness as part of their entrepreneurial posture. Specifically, we examine the effect of client adaptiveness on sustained competitive advantage. Client adaptiveness is conceptualized as the extent to which an organization engages in identifying and responding to perceived client needs and wants which reflects the service firm’s propensity to dynamically synchronize with the project/client requirements.

Facial expression recognition experiments with data from television broadcasts and the world wide web

Facial expression recognition (FER) systems must ultimately work on real data in uncontrolled environments although most research studies have been conducted on lab-based data with posed or evoked facial expressions obtained in pre-set laboratory environments. It is very difficult to obtain data in real-world situations because privacy laws prevent unauthorized capture and use of video from events such as funerals, birthday parties, marriages etc. It is a challenge to acquire such data on a scale large enough for benchmarking algorithms. Although video obtained from TV or movies or postings on the World Wide Web may also contain ‘acted’ emotions and facial expressions, they may be more ‘realistic’ than lab-based data currently used by most researchers. Or is it? One way of testing this is to compare feature distributions and FER performance. This paper describes a database that has been collected from television broadcasts and the World Wide Web containing a range of environmental and facial variations expected in real conditions and uses it to answer this question. A fully automatic system that uses a fusion based approach for FER on such data is introduced for performance evaluation. Performance improvements arising from the fusion of point-based texture and geometry features, and the robustness to image scale variations are experimentally evaluated on this image and video dataset. Differences in FER performance between lab-based and realistic data, between different feature sets, and between different train-test data splits are investigated.