The field of strategic management addresses challenges that firms encounter in an attempt to remain competitive. The ability to explain variation in firm success through examination of knowledge flows has become a prominent focus of research in the strategic management literature. Specifically, researchers have sought to further examine how firms convert knowledge, a phenomenon conceptualized as absorptive capacity. Absorptive capacity is the firm’s ability to acquire, assimilate, transform, and exploit knowledge. Few studies have captured the richness and multi-dimensionality of absorptive capacity, and it remains to be understood how the dimensions of the phenomenon convert knowledge. Furthermore, how absorptive capacity influences the firm remains to be understood. To address these research gaps, this dissertation seeks to (1) determine how absorptive capacity converts knowledge, and (2) determine how absorptive capacity influences firm capabilities. The research questions are investigated using structural modeling techniques to analyze data collected from software-industry firms. The findings offer contributions to the absorptive capacity and capability literatures. For example, absorptive capacity is hypothesized to consist of complex relationships among its internal dimensions. However, findings of this study suggest the relationships among the dimensions are linear in nature. This finding is in line with the theoretical foundations of and early literature on absorptive capacity but contrary to recent conceptualizations, which suggests relationships among the dimensions are more closely related to the theoretical origins of absorptive capacity. Additionally, to examine how absorptive capacity influences the firm, a capability-based perspective is used to hypothesize the influence of absorptive capacity on firm capabilities. Findings suggest absorptive capacity positively influences each dimension of firm capabilities (e.g., operational, customer, and innovation capabilities); thus, absorptive capacity influences the firm by altering firm capabilities. Given the richness of the findings, numerous fields are likely to benefit from this investigation. Through an examination of absorptive capacity and capabilities, this study contributes to the understanding of the absorptive capacity phenomenon and offers insight into how the phenomenon influences the firm. Furthermore, practical implications are offered for managers interested in enhancing firm competitiveness.

According to eMarketer, the total advertising spend in US alone was estimated to be over $238 billion. Firms invest large amounts of money in advertising to promote and inform consumers about their products and services, as well as to persuade them to purchase. The broad theme of advertising has been examined from many different angles in the marketing literature, ranging from empirically measuring effects of TV ads on sales to analytically characterizing the key economic forces stemming from enhanced targetability in online advertising. The purpose of my dissertation is to study some of the key questions which remain unaddressed in the advertising literature. In the first essay, I examine firms' choices of advertising content in a competitive setting. I demonstrate that competitive forces sometimes induces firms to choose advertising content that shifts consumers' perception of product quality. While this strategy hurts firms in a monopoly setting, it increases their profits under competition because it may increase the utility of their offering in comparison with the competing offering. In the second essay, I investigate the optimal mechanism for selling online ads in a learning environment. Specifically, I show that when ad sellers, such as Google, design their ad auctions, it is optimal for them to favor new advertisers in the auction in order to expedite learning their ad performance. In the third essay, I study the impact of tracking consumers' Internet activities on the online advertising ecosystem in the presence of regulations that, motivated by privacy concerns, endow consumers with the choice to have their online activity be tracked or not. I find that when ad effectiveness is intermediate, fewer ads are shown to opt-in consumers, who can be tracked and have their funnel stages inferred by advertisers, than to opt-out consumers, who cannot be tracked. In this case, consumers trade-off the benefit of seeing fewer ads by opting-in to tracking (positive instrumental value of privacy) with the disutility they feel from giving up their privacy (intrinsic cost of privacy). Overall, these findings shed light on novel strategic forces that provide guidance for marketers' advertising decisions in three distinct contexts.

This thesis consists of three papers in optimization and revenue management over complex networks: Robust Linear Control in Transmission Systems, Online Learning and Optimization Under a New Linear-Threshold Model with Negative Influence, and Revenue Management with Complementarity Products. This thesis contributes to analytical methods for optimization problems in complex networks, namely, power network, social network and product network. In Chapter 2, we describe a robust multiperiod transmission planning model including renewables and batteries, where battery output is used to partly offset renewable output deviations from forecast. A central element is a nonconvex battery operation model plus a robust model of forecast errors and a linear control scheme. Even though the problem is nonconvex we provide an efficient and theoretically valid algorithm that effectively solves cases on large transmission systems. In Chapter 3, we propose a new class of Linear Threshold Model-based information-diffusion model that incorporates the formation and spread of negative attitude. We call such models negativity-aware. We show that in these models, the expected positive influence is a monotone sub-modular function of the seed set. Thus we can use a greedy algorithm to construct a solution with constant approximation guarantee when the objective is to select a seed set of fixed size to maximize positive influence. Our models are flexible enough to account for both the features of local users and the features of the information being propagated in the diffusion. We analyze an online-learning setting for a multi-round influence-maximization problem, where an agent is actively learning the diffusion parameters over time while trying to maximize total cumulative positive influence. We develop a class of online learning algorithms and provide the theoretical upper bound on the regret. In Chapter 4, we propose a tractable information-diffusion-based framework to capture complementary relationships among products. Using this framework, we investigate how various revenue-management decisions can be optimized. In particular, we prove that several fundamental problems involving complementary products, such as promotional pricing, product recommendation, and category planning, can be formulated as sub-modular maximization problems, and can be solved by tractable greedy algorithms with guarantees on the quality of the solutions. We validate our model using a dataset that contains product reviews and metadata from Amazon from May 1996 to July 2014. We also analyze an online-learning setting for revenue-maximization with complementary products. In this setting, we assume that the retailer has access only to sales observations. That is, she can only observe whether a product is purchased from her. This assumption leads to diffusion models with novel node-level feedback, in contrast to classical models that have edge-level feedback. We conduct confidence region analysis on the maximum likelihood estimator for our models, develop online-learning algorithms, and analyze their performance in both theoretical and practical perspectives.

Two major objectives of organisations are to achieve firm performance and to maintain a competitive advantage; strategies to achieve these objectives differ widely. Research at the entrepreneurship and marketing interface investigates the application of both dimensions on firm activities, processes and behaviour to achieve different performance parameters. In the field of entrepreneurial marketing research two key approaches are discussed: a market-driven and a market-driving approach. Market-driven approaches, though applied by many organisations, are less successful in allowing organisations to outperform others and create long-term competitive advantage. Market-driving, on the other hand, is considered to contribute to enduring competitive advantage. Current research indicates that the construct of market driving and the factors that influence it are not well understood. The purpose of this study is therefore to measure market driving and determine firm-internal factors that influence an organisation’s market-driving ability in the South African healthcare industry. In this research, constructs drawn from the literature study were used to formulate the conceptual framework and statistical model. The empirical part of the study used a fully structured telephonic questionnaire and the respondents were managers in organisations in the South African healthcare industry. Data analysis employed structural equation modelling. The results indicate that market driving can reliably be measured by three activities: market sensing; influencing customer preferences; and alliance formation. Entrepreneurial behaviour, strategic orientation and entrepreneurial capital have a more positive impact on market-driving ability than corporate entrepreneurial management. The study demonstrated that market-driving ability significantly benefits firm performance and relative competitive strength. The study provides a solid basis for future research in the field. Moreover, the results of the study can be applied by organisations in a three-step process. First, organisations can assess their current level of market driving. Second, they can assess influencing factors, and finally identify areas for improvement. Through continuous reassessment organisations can work on their market-driving ability to achieve their organisational objectives. / Thesis (PhD)--University of Pretoria, 2011. / Business Management / unrestricted

In recent years, diversification has become a common strategy used by companies in emerging markets. It is believed that diversification operations could help firms get better performance and gain higher profits from a larger internal market. However, contradictory results reveal that diversification empirically hurts firm value and other studies show the relationship between diversification and firm performance is complicated that should be studied in separate industries. The opinion is inconclusive on this topic. This study developed a performance index to see how diversification impact on various perspectives of firm performance. Conclusions as follow. International diversification has a positive correlation with firm performance in several aspects whereas industrial diversification helps firms’ developing ability. However, due to the unavailability of long-term data, we can not rule out the possibility that well-performed firms go for international diversification. Besides, The relationship between diversification and firm performance affected by different industries. The agricultural and natural resource firms tend to exceed manufacturing firms in the efficiency aspects whereas manufacturing companies tend to have advantages in the sustainability aspect compared to service firms. There is also evidence showing that the largest shareholders’ holdings rates have a positive impact on firm performance and state-owned rate has a negative relation with firm performance.

As of 2021, nearly 538 million adults and children live with diabetes mellitus worldwide, with projections for 2045 estimated at 645 million adults globally. In the United States, there are 34 million adults with diabetes and an additional 88 million with prediabetes. The economic costs are staggering, primarily due to management of acute or chronic complications. As a chronic condition requiring daily self-care, the psychosocial burden of diabetes is significant. Therefore, diabetes self-management education is recommended as a standard of care for all people with diabetes. The Association of Diabetes Care and Education Specialists (ADCES) has created a framework of education incorporating seven self-care behaviors, each with several knowledge, skill, and barrier resolution outcome measures. However, research has suggested that diabetes self-management and support services are not utilized sufficiently. YouTube™ with a reach of over 2 billion users is a potential medium to reach more people with diabetes. At the present time, there is a paucity of research describing the source and content of the most widely viewed videos on diabetes selfcare. This study aimed to help fill that gap. Specific aims of the study included: (a) describe characteristics of widely viewed YouTube™ videos on Diabetes Self-Care concerning length, date posted, source, speaker(s), format, and number of views; (b) describe the content of the most widely viewed YouTube™ videos on diabetes self-management education and support, categorized by the ADCES7 Self-Care™ behaviors; and (c) examine the source of videos in relation to number of views. The researcher used a YouTube™ Application Programming Interface to retrieve video URLs along with meta data such as source, duration, date posted, and view counts. Data were sorted by URL and view count, duplicates removed, and screened for inclusion and exclusion criteria. The top 100 videos by view count were used as the sample in the study. A codebook developed for this study categorized the upload source, speaker, format, and seven content categories. Descriptive analyses were conducted to understand the most viewed sources and the content categories likely and not likely to be mentioned. Collectively, these 100 videos were viewed 146,405,133 times, with an average duration of 12.2 minutes. Most of the videos (N = 77) were uploaded between 2017 and 2021. Results indicated that the two most popular sources for videos were Professionals and Corporations together uploading 72 videos and garnering 77% of cumulative views. In contrast, government agencies uploaded 1 video (<1% of cumulative views). Professionals was the most common protagonist (N = 42) when a speaker could be identified. Talk by professional received 34.09% of cumulative views, almost as much as Animation with voice (35.95%). The content areas most mentioned were Background on Diabetes, focusing on factors affecting blood sugar and ADCES7 Self-Care BehaviorsTM, especially Healthy Eating. Reversal of Diabetes was broached in 18 videos with 23.13% of cumulative views. Prevention Strategies for Communities was not mentioned at all, and Prevention Strategies for Individuals garnered less than 4% of cumulative views. YouTube™ is a popular source of online information for people with diabetes. As such, it presents an excellent avenue to raise awareness of prediabetes and dissemination of diabetes self-management education. Significant opportunity exists for government and advocacy agencies to increase their presence on YouTube™ in terms of viewership, while presenting meaningful and credible information. Recommendations for population and public health initiatives as well as future research and practice were presented to utilize the power of YouTube™ as a medium to expand the reach of diabetes self-management education and support.

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This research study is aimed at finding empirical evidence to support a finding from an initial study that corporate governance disclosure is linked with financial performance for JSE listed companies. The study made use of a scorecard previously designed for the initial study to rate the governance disclosure of the various companies. 74 companies from the eight major sectors of the JSE were selected for the sample and financial data for the review period was extracted from MacGregor BFA. Governance was rated based on annual reports and any other information within the public domain. The financial performance measures used were, CAGR using opening and closing share prices, price to book and price/earnings ratio. Using mean disclosure scores, two portfolios were created to compare financial performance, the high and low disclosure portfolios. A simple correlation analysis was then conducted to assess the relationship of governance disclosure with the three different financial measures. The findings indicate a negative correlation between governance disclosure and returns whilst a positive correlat ion is established between governance and firm valuations. Copyright / Dissertation (MBA)--University of Pretoria, 2010. / Gordon Institute of Business Science (GIBS) / unrestricted

This research investigated whether earnings management of listed companies, have increased or decreased since the implementation of King II in 2003. This study assessed the extent of earnings management for certain sectors and for large and mid cap companies. The discretionary component of total accruals was used as proxy for earnings management – calculated by using the Modified Jones Model. The top-100 JSE-listed firms by market capitalisation were assessed, excluding the Financial, Mining and Resource Sectors. It was found that discretionary accruals has increased since 1996 and peaked in 2005 (see graph Figure 13). It was concluded with 89% certainty that discretionary accruals during “2003-2009” were higher than during “1996-2002”. Hence, the research suggests that accrual based earnings management is likely to have increased since 2003. However, the results were not statistically significant. Also, the cause of this increase could be due to many factors (not necessarily due to King II). Discretionary accruals were found to be higher for mid cap companies and for the retail sector. However, the analysis was not statistically significant. Discretionary accruals have fluctuated significantly over time and amongst companies. In order to identify which companies and sectors manage earnings the most, a more detailed micro-level investigation, using multiple detection models, is required. Copyright / Dissertation (MBA)--University of Pretoria, 2010. / Gordon Institute of Business Science (GIBS) / unrestricted

The rise and growth of online social networks have spurred tremendous changes in our understanding of human behavior. Social scientists and companies have devised new tools to analyze the vast amounts of data obtained from these networks. Such advances have had two major consequences. First, it has allowed firms to significantly improve their segmentation and targeting strategies. Second, it also modified how problems are conceptualized. For example, books, academic papers, or webpages are now being studied under methods developed for social network analysis. This dissertation contributes to both applications. Essays 1 and 2 describe efficient targeting strategies in situations where access to information or computing power is costly. Although existing “seeding” methods have been quite successful in social networks, they often do not account for firms' limited computing power or assume that firms are omniscient. Essay 3 focuses on the art industry by conceptualizing paintings as items connected to each other in a network through their visual similarities. Indeed, we still do not perfectly understand what makes art financially valuable and even major auction houses are at awe when paintings are sold at prices multiple times higher than what they expected. In particular, we aim to quantify how an art piece's visual features and historical importance may impact prices and assess how auction houses and their marketing efforts may modify how art is evaluated and valued. This dissertation has three essays. In the first essay, we analyze how the friendship paradox, which states that your friends have more friends than you, may be generalized to situations where relationships are asymmetric. Indeed, the result assumes symmetric relations: if two people are friends, then each is the other's friend. For social networks that satisfy this assumption (e.g., Facebook), the friendship paradox implies that firms can potentially achieve faster and more widespread diffusion of information by seeding it with the friends of a group of people than with people in the group itself. We generalize the result to allow one-sided (leader/follower) relations and examine the implications for seeding in social networks where messages can be sent only by a leader to his/her followers. We obtain necessary and sufficient conditions under which the highest number of followers is obtained by seeding with (1) leaders, (2) followers, and (3) individuals chosen by ignoring the distinction between leaders and followers. We examine the seeding implications of the results for a subset of Twitter users. The second essay furthers our understanding of the friendship paradox and relates it to beta centrality and eigenvector centrality. We generalize the results to asymmetric relations, define two beta centrality measures and relate them to the singular vectors of the associated directed graph. Our first generalization shows that the expected number of k removed friends is no smaller than the expected number of k-1 removed friends when k is an even number. Such a relation does not necessarily exist when k is an odd number. As k increases to infinity, the limiting value of the expected number of k removed friends converges to the largest eigenvalue of the associated undirected graph. We interpret beta centrality to be a weighted sum of an infinite series of the numbers of k removed friends. It approaches eigenvector centrality when the weighting parameter becomes arbitrarily close to the inverse of the limiting value of the expected number of k removed friends. We further generalize these results to asymmetric relations (say, between followers and leaders) that can be represented by directed graphs. We show that the last person in a randomly selected alternating sequence of 2k+1 leaders and followers (followers and leaders) has no fewer followers (leaders) than the last person in a randomly selected alternating sequence of 2k followers and leaders (leaders and followers). As k increases to infinity, the expected number of leaders of the last person in a randomly selected sequence of 2k alternating leaders and followers converges to a value proportional to the largest singular value of the associated directed graph. Similarly, the expected number of followers of the last person in a randomly selected sequence of 2k alternating followers and leaders converges to a (different) value proportional to the largest singular value of the associated directed graph. We show that there is a reciprocal relation between the limiting expected values of leaders and followers. We generalize beta centrality to asymmetric relations and relate the limiting values of beta centrality scores for followers and leaders to the singular vectors of the associated directed graph. The third essay focuses on the art market. Auction houses hold auctions regularly throughout the year. However, once or twice a year, art investors and wealthy consumers attend highly selective marquee events: day and evening sales. Those carefully designed and highly marketed events often generate a lot of excitement for connoisseurs as most paintings get sold for tremendous amounts of money. But what makes those paintings special? We investigate how art is evaluated across those three types of auctions. Specifically, we build a deep learning model to summarize the paintings into a low dimensional representation space where each factor encodes a specific feature of the paintings’ aesthetics and further utilize those components to create “network” variables that will determine how influential and creative a painting is. We use those predictors in hedonic regression models to study how art returns differs across the three types of sales and subsequently analyze whether the paintings are evaluated differently. In particular, we find that paintings sold in evening sales generated an annualized return of 14.33% in the period 1999-2018 - more than three times the returns of paintings sold in regular or day auctions. Finally, we adopt a propensity score matching approach to create a homogeneous population of paintings - based on their likelihood to be auctioned in an evening sale - to assess the causal impact of being featured in an evening sale and find that such highlight increases a painting's price by almost $6 million.