Thought leadership is a term that has been around for more than a decade. Little research has been done on exactly what thought leadership is or how to become a thought leader. Yet the business press is full or articles touting the importance of becoming a thought leader along with a variety of benefits. Additionally, social media has become an increasingly important part of any marketing strategy. This paper begins by developing a typology of business to business CEO social media presence, it clearly defines the three key attributes of a thought leader and concludes with identifying which attributes are the most important in CEO thought leadership. Chief Executive Officers (CEOs) are generally expected to represent the public face of the company, and their leadership is critical to success in product-service markets. Social media platforms offer CEOs the opportunity to benefit their companies by demonstrating leadership, communicating ideas, and motivating others, often with a personalized touch. Yet, many CEOs, particularly in business-to-business (B2B) firms, are relatively new to social media and do not have a clear social media strategy. A typology of B2B CEO social media presence was developed by utilizing cluster analysis to analyze a cross-section of B2B CEOs’ social media activity. Results shows that the Reluctants, constituting 74% of the sample, have little or no social media presence. Of CEOs with some social media presence, the main types are LinkedIn Leveragers who have a substantial LinkedIn presence only, and TweetStars, who are active only on Twitter. A CEO who is a thought leader is a business leader who communicates ideas in a way that motivates others to develop them and is recognized by others outside of their organization. Using the three attributes of thought leadership – communication, motivation of others, and public recognition, a survey was conducted to identify which of these attributes were the most important in increasing perceived thought leadership. Using choice based conjoint analysis to test the level of perceived thought leadership, public recognition followed closely by motivation were most important in contributing to perceived thought leadership. The typology developed in this paper leads to the development of a set of empirical propositions for future research. Insights gained from this analysis can help companies and their CEOs make informed decisions on their social media options and strategies. The identification of what attributes are most important to increase levels of perceived thought leadership lays the foundation for additional research. Recommendations offered in this paper can help companies and CEOs invest their resources in a marketing strategy appropriate to their goals. / Business Administration/Marketing

Article 1 As forest ownership continues to change, forestry must change to be relevant to its new constituency and client base. Market segmentation can help in this task. There is no such thing as an average forest owner. This study assessed the motivations and forest practices of 661 new owners of forested lands ranging in size between 2 and 50 acres. The study focused on rapidly growing counties in Virginia. Cluster analysis techniques were used to identify six market segments: Absentee Investors, Young Families, Forest Planners, Preservationists, Farmers, And Professionals. Only the smallest market segment (Absentee Investors, n = 26) reflects motivations and forest management interests that somewhat resemble "traditional" forest landowners. The results suggest that "lifestyle" concerns are the major motivations of these new owners and seemingly determine receptivity to professional forestry advice. This analysis helps understand these differently motivated segments and suggests possible marketing strategies professional foresters can use to "sell" forestry and active forest management. Article 2 Land managers increasingly are seeking to promote management of private forestland that transcends political and ownership boundaries. Descriptive analyses were used to characterize new landowners' intentions to participate in active management, both within individual property boundaries and in cooperation with neighboring landowners. The study also describes obstacles that these new owners perceive constrain their participation in active management. Further analysis explores potential differences in these variables related to amount of land owned, attitudes about private property rights, trust in forestry professionals, and attitudes about clearcutting and harvesting practices. The results suggest that private property rights are not an insurmountable problem to ecosystem management efforts. The forestry profession, however, seems to suffer from an invisibility problem among the population of new landowners. The very audience that ecosystem management programs target (owners of fewer than 20 acres of forestland) perceives itself to be least relevant to the message of cooperation. In fact, the biggest obstacle identified was that these new landowners have never thought about participating in active management, either within or across property boundaries. / Ph. D.

Converging demographic, societal, and political conditions are raising concerns among educational policy makers regarding Virginia's capacity to meet the demand for high quality instructional personnel. The variables affecting demand include shifts in student enrollments, efforts to meet Virginia Standards of Accreditation, retirement rate, efforts to increase diversity in instructional positions, efforts to reduce staffing ratios, increased technology in the classroom, legislative mandates, competition for instructional personnel, salary and other quality of life issues, rising licensure standards, and non-public school pupil enrollment. This research is a quantitative study that combines descriptive and correlational research methods. One purpose of this study is to aggregate and summarize data from Virginia school districts that will provide important information for educational policy makers. The second purpose is to create a paradigm that will quantify and rank order the variables that affect the demand for educators in Virginia. The third purpose of this study is to place school districts into groupings according to variables that influence demand for instructional personnel. The k-means cluster analysis procedure was utilized for this purpose. The Virginia Public School Systems' Instructional Personnel Profile: 1999-2000, a survey commissioned by the Virginia Department of Education, was sent to the 132 Virginia public school districts. A total of 126 school districts responded. This survey provided the data used in this study. This survey was developed because there is no uniform, statewide system to collect demographic data for PreK-12 instructional personnel in Virginia. The results find that Virginia is experiencing shortages of instructional personnel. Special education, mathematics, science, and technology endorsement areas are expected to experience the most critical shortages. Competition from other Virginia school districts, retirement, efforts to reduce teacher to pupil ratios, and salaries are reported as the variables that most influence demand for personnel. Virginia public school districts are clustered into two groups using the k-means cluster analysis procedure. / Ph. D.

Cluster analysis refers to a type of statistical method designed to identify homogeneous groups within complex, multivariate data sets. In this study two newly developed genetic cluster analysis algorithms, GENCLUS and GENCLUS+, were validated by comparing their performance against that of three popular clustering techniques (Ward's method, K-means w/ random seeds, K-means w/Ward's centroids) and in an elaborate Monte Carlo study. Additionally, the ability of GENCLUS+ to determine the correct number of clusters was compared against that of three conventional procedures (Calinski and Harabasz, C-index, trace W). GENCLUS and GENCLUS+ achieved Rand recovery values slightly inferior to those of conventional methods. However, GENCLUS+ appeared to perform better than conventional methods in an empirical analysis, and genetic method solutions appear to possess high internal cohesion and external isolation. The mixed results are interpreted as an indication of a discrepancy between cluster theory and conventional data generation techniques. / Ph. D.

Non-timber forest products (NTFPs) have been harvested in the wild for generations in Appalachia. Demand for forest farmed raw material and transparent supply chains is growing, which has increased attention on the role of NTFPs in regional livelihoods. We conducted an embedded case study to understand contemporary NTFP harvest, perceptions of community-based development of NTFP livelihood opportunities, and the extent to which forest landowners are interested forest farming. One case study focused on Grayson County, Virginia and included semi-structured interviews with 16 key stakeholders. Interviews explored motivations, species preferences, and uses of NTFPs among individuals and then perceived assets, obstacles, and desired strategies for NTFP livelihood development within the community. Through qualitative analysis we found financial benefits, engagement with nature, and personal preferences (personal fulfillment, learning and creativity, and lifestyle) were key motivators. Newcomers to Appalachia were more likely to balance monetary, environmental, and lifestyle motivations, and multigenerational residents focused more on financial motivations and to a lesser degree lifestyle. We used the community capitals framework to analyze the community focused data and found references to natural, human, and cultural capital as both an asset and an obstacle. Financial capital was a top-obstacle whereas social capital was a top asset. Strategies focused on social, human, and financial capital investments such as social networking, educational programming, tax incentive programs, and local fundraising. The regional case study surveyed via mail those who own 5 or more acres of forestland in 14 Southwest Virginia Appalachian counties to understand extent to which they are interested in forest farming or leasing land for forest farming. We had a response rate of 28.9% and found 45% of forest landowners, owning 47% of the forestland, were interested in forest farming. Those that were likely to lease their land accounted for 36% of all respondents and owned 43% of the forestland. Further, those who were interested did not differ based on demographic and land characteristics. Our study reveals the contemporary state of NTFP livelihoods combines markets sales with broader homesteading objectives and that lifestyle and environmental motivators are an increasing focus as newcomers take roots in the region. Further, communities may be able to draw upon the cultural and natural capital around NTFPs as well as the strong social capital often present in rural communities to further invest in social networking, education, financial incentives, and funding to support NTFP livelihood development. Finally, forest farming and leasing of land for this practice is of considerable and broad appeal to forestland owners in Southwest Virginia, which may indicate possibilities for a critical mass to supply a growing demand for sustainably sourced and quality NTFP raw materials. / Doctor of Philosophy / In recent decades Appalachia has experienced socioeconomic challenges with lack of employment opportunities, high poverty levels and the resulting outmigration of residents, especially youth, in search of work. At the same time newcomers are migrating into the area drawn by the culture and natural environment, which is shifting the social fabric of the region. It is in this new context that communities are asked to develop livelihood opportunities using what is available to them. Non-timber forest products (NTFPs) have been harvested in the wild for generations in Appalachia and offer one avenue of possibility, especially as the market has begun to support higher prices for raw materials that meet the increasing consumer demand for sustainability and quality. Within these new dynamics we set out to understand contemporary uses of NTFPs in Appalachia, and what motivates people to work with these species, as well as community perceptions about how to develop NTFP livelihood opportunities, and the extent to which Appalachian residents are interested in forest farming (the cultivation or stewardship of NTFPs in an existing forest). Our study reveals the contemporary state of NTFP livelihoods combines markets sales with broader homesteading objectives and that lifestyle and environmental motivators are an increasing focus as newcomers take roots in the region. Further, communities may be able to draw upon the cultural and natural capital around NTFPs as well as the strong social and human capital often present in rural communities to further invest in social networking, education, financial incentives, and funding to support NTFP livelihood development. Finally, forest farming and leasing of land for this practice is of considerable and broad appeal to forestland owners in Southwest Virginia, which may indicate possibilities for a critical mass to supply a growing demand for sustainably sourced and quality NTFP raw materials.

License plate recognition/identification (LPR/I) applies image processing and character recognition technology to identify vehicles by automatically reading their license plates. In the United States, however, each state has its own standard-issue plates, plus several optional styles, which are referred to as special license plates or varieties. There is a clear absence of standardization and multi-colored, complex backgrounds are becoming more frequent in license plates. Commercially available optical character recognition (OCR) systems generally fail when confronted with textured or poorly contrasted backgrounds, therefore creating the need for proper image segmentation prior to classification. The image segmentation problem in LPR is examined in two stages: license plate region detection and license plate character extraction from background. Three different approaches for license plate detection in a scene are presented: region distance from eigenspace, border location by edge detection and the Hough transform, and text detection by spectral analysis. The experiments for character segmentation involve the RGB, HSV/HSI and 1976 CIE L*a*b* color spaces as well as their Karhunen-Loéve transforms. The segmentation techniques applied include multivariate hierarchical agglomerative clustering and minimum-variance color quantization. The trade-off between accuracy and computational expense is used to select a final reliable algorithm for license plate detection and character segmentation. The spectral analysis approach together with the K-L L*a*b* transformed color quantization are found experimentally as the best alternatives for the two identified image segmentation stages for US license plate recognition. / Master of Science

Yes / The linear economic business model was deemed unsustainable, necessitating the emergence of the circular economy (CE) business model. Due to resource scarcity, increasing population, and high food waste levels, the food sector has been facing significant sustainability challenges. Small and medium-sized enterprises (SMEs), particularly those in the food sector, are making efforts to become more sustainable and to adopt new business models such as the CE, but adoption rates remain low. Industry 4.0 and its associated technological applications have the potential to enable CE implementation and boost business competitiveness. In the context of emerging economies facing significant resource scarcity constraints and limited technology availability, CE principles need to be adapted. CE could create a new job economy in emerging economies, bringing scale and a competitive advantage. This study explores the enablers of and barriers to Industry 4.0 adoption for CE implementation in fruit and vegetable SMEs in India from a resource-based perspective. The purpose is to develop an evidence-based framework to help inform theory and practice about CE implementation by SMEs in emerging economies. Fifteen semi-structured interviews were conducted with experts in food SMEs. The interview transcripts were first subjected to thematic analysis. The analysis was then complemented with sentiment and emotion analyses. Subsequently, hierarchical cluster analysis, k-means analysis, and linear projection analysis were performed. Among others, the findings suggest that Industry 4.0 plays a key role in implementing CE in SMEs in emerging economies such as India. However, there are specific enablers and barriers that need to be considered by SMEs to develop the resources and capabilities needed for CE competitive advantage.

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Cette thèse est consacrée au problème de la comparaison de deux partitions non strictes (floues/probabilistes, possibilistes) d’un même ensemble d’individus en plusieurs clusters. Sa résolution repose sur la définition formelle de mesures de concordance reprenant les principes des mesures historiques développées pour la comparaison de partitions strictes et trouve son application dans des domaines variés tels que la biologie, le traitement d’images, la classification automatique. Selon qu’elles s’attachent à observer les relations entre les individus décrites par chacune des partitions ou à quantifier les similitudes entre les clusters qui composent ces partitions, nous distinguons deux grandes familles de mesures pour lesquelles la notion même d’accord entre partitions diffère, et proposons d’en caractériser les représentants selon un même ensemble de propriétés formelles et informelles. De ce point de vue, les mesures sont aussi qualifiées selon la nature des partitions comparées. Une étude des multiples constructions sur lesquelles reposent les mesures de la littérature vient compléter notre taxonomie. Nous proposons trois nouvelles mesures de comparaison non strictes tirant profit de l’état de l’art. La première est une extension d’une approche stricte tandis que les deux autres reposent sur des approches dite natives, l’une orientée individus, l’autre orientée clusters, spécifiquement conçues pour la comparaison de partitions non strictes. Nos propositions sont comparées à celles de la littérature selon un plan d’expérience choisi pour couvrir les divers aspects de la problématique. Les résultats présentés montrent l’intérêt des propositions pour le thème de recherche qu’est la comparaison de partitions. Enfin, nous ouvrons de nouvelles perspectives en proposant les prémisses d’un cadre qui unifie les principales mesures non strictes orientées individus. / This thesis is dedicated to the problem of comparing two soft (fuzzy/ probabilistic, possibilistic) partitions of a same set of individuals into several clusters. Its solution stands on the formal definition of concordance measures based on the principles of historical measures developped for comparing strict partitions and can be used invarious fields such as biology, image processing and clustering. Depending on whether they focus on the observation of the relations between the individuals described by each partition or on the quantization of the similarities between the clusters composing those partitions, we distinguish two main families for which the very notion of concordance between partitions differs, and we propose to characterize their representatives according to a same set of formal and informal properties. From that point of view, the measures are also qualified according to the nature of the compared partitions. A study of the multiple constructions on which the measures of the literature lie completes our taxonomy. We propose three new soft comparison measures taking benefits of the state of art. The first one is an extension of a strict approach, while the two others lie on native approaches, one individual-wise oriented, the other cluster-wise, both specifically defined to compare soft partitions. Our propositions are compared to the existing measures of the literature according to a set of experimentations chosen to cover the various issues of the problem. The given results clearly show how relevant our measures are. Finally we open new perspectives by proposing the premises of a new framework unifying most of the individual-wise oriented measures.

This research determines whether data quality mining can be used to describe, monitor and evaluate the scope and impact of semantic data quality problems in the learner enrolment data on the National Learners’ Records Database. Previous data quality mining work has focused on anomaly detection and has assumed that the data quality aspect being measured exists as a data value in the data set being mined. The method for this research is quantitative in that the data mining techniques and model that are best suited for semantic data quality deficiencies are identified and then applied to the data. The research determines that unsupervised data mining techniques that allow for weighted analysis of the data would be most suitable for the data mining of semantic data deficiencies. Further, the academic Knowledge Discovery in Databases model needs to be amended when applied to data mining semantic data quality deficiencies. / School of Computing / M. Tech. (Information Technology)