The future of venture capital decision making : the impact of quantitative sourcing and machine learning on the VC Investment process

Investing in early-stage startups is a difficult endeavor. Venture Capitalists use heuristics and base their decisions on past experiences, which can lead to biases. Recently, Venture Capitalists are increasingly using artificial intelligence and quantitative sourcing to support their investment process, while others still rely on traditional investment mechanisms. This research investigates the usage and impact of artificial intelligence and machine learning throughout the venture investment cycle to make investment decisions. This dissertation is an exploratory study that employs a qualitative research approach in the form of semi-structured interviews with ten European Venture Capitalists. The results show that Venture Capitalists utilize machine learning and web scraper tools, particularly during the deal origination, firm-specific screening, and general screening stages of the investment process, to solve the identification and selection challenges. As a result, investment processes become more efficient and less biased, allowing for more time to be spent advising and mentoring portfolio startups. It adds to the existing literature on how artificial intelligence and data can augment existing investment mechanisms during the venture capital decision-making process.Investir em startups na sua fase inicial exige um elevado empenho. Os investidores de capital de risco baseiam as suas decisões em pesquisa e experiências passadas, o que pode levar a enviesamentos. Embora muitos investidores de capital de risco ainda utilizem mecanismos de investimento tradicionais, tem havido um aumento na utilização de inteligência artificial e sourcing quantitativo para apoiar o processo de investimento. Esta investigação estuda a utilização e impacto da inteligência artificial e de machine learning ao longo do ciclo de investimento de risco para tomar decisões de investimento. Esta dissertação é um estudo empírico que utiliza uma abordagem de investigação qualitativa sob a forma de entrevistas semi-estruturadas com dez empresas de investimento de capital de risco europeias. Os resultados mostram que os investidores de capital de risco utilizam machine learning e ferramentas de recolha de dados na web, em particular durante o início da oportunidade de negócio, a seleção específica da empresa, e fases gerais de análise do processo de investimento, para resolver os desafios de identificação e seleção. Consequentemente, os processos de investimento tornam-se mais eficientes e menos tendenciosos, permitindo que se utilize mais tempo a aconselhar e a orientar as empresas do portfolio. Este estudo complementa a literatura existente relativamente a como a inteligência artificial e os dados podem elevar os mecanismos de investimento existentes durante o processo de tomada de decisão de capital de risco

ALLOCATION OF IT DECISION RIGHTS IN MULTIBUSINESS ORGANIZATIONS: WHAT DECISIONS, WHO MAKES THEM, AND WHEN ARE THEY TAKEN?

Effective IT governance is an important requirement for strategic IT-based change. The extant literature focuses on which IT decisions should be governed and who is accountable for them. However, in multi-business organizations there is little theoretical guidance on which decisions should be made at the corporate and strategic business unit (SBU) levels, or when such decisions should be made as part of the corporate and SBU strategy processes. This paper draws on the strategic management literature to develop a theoretical framework for allocating IT decision rights between business and IT at the corporate and SBU levels. Importantly, the framework also unbundles corporate IT platform and SBU IT decision making across the corporate investment cycle. This is achieved by adopting a real options-based pricing investment model to reduce risk, uncertainty and complexity. The theoretical framework is illustrated with in-depth longitudinal case study and compared against existing normative IT governance prescriptions

Performing Under Pressure: IT Execution in a $1.4bn Business Transformation

This teaching case provides a practical illustration of the challenges in executing large-scale ITbased change. It describes how the Commonwealth Bank of Australia replaced its service and sales systems between 2003 and 2006 with the goal of collating a “single view of client”. The case is an exemplar of staged incremental development. The sponsor set up multiple work streams and ran them as independently as possible. Regular releases delivered incremental change to the business, incorporated lessons learned, and added further functionality. This had implications for architecture, software development, training, testing, and risk management. There were significant change management challenges. The case provides students with insights into program management in IT transformations, architecture, project management, software delivery lifecycles, risk management, logistics and IT infrastructure

Realising increased value from using knowledge management to improve customer relationship management in a retail banking environment : the case of Standard Bank in Malawi

Retail banks globally are under the threat of reducing profitability driven by a harsh operating environment. They are facing many challenges including greater competition from non-banking entities, stricter regulatory requirements, more knowledgeable and demanding customers and speed of innovation in terms of new products and services. Customer relationship management (CRM) remains a recognised source of competitive advantage but questions still abound about how it can be deployed effectively and for the benefit of both the customer and the bank. Knowledge management (KM), on the other hand, is also a proven source of competitive advantage and this study uncovers how KM can be used to enhance CRM in order to improve profitability at SBM. This study explored how KM can be deployed to enhance CRM as well as how this resulted in improvement of customer service and satisfaction indices as well as profitability, sales, customer retention and growth in customer base. The sales and customer service teams formed the primary subjects of this study and key productivity measures such as profitability; customer satisfaction as measured through NPS and CEBS, sales trends by segment and customer cross sell ratio by segment were tracked as the output. However, multi-disciplinary teams across Standard Bank in Malawi (SBM) as well as Standard Bank Group (SBG were involved in designing the solutions to customer issues identified. A framework that combines CRM and KM, the MKC Relationship Management was developed as part of this study through literature review. The MKC Relationship Management framework was then tested through cycles of action research where a plan was developed in the first cycle, tested in the second cycle and this was followed by planning for a third cycle. The results of each cycle were measured and tracked over a period of time before being reviewed for impact. The final planning cycle was undertaken to see how further improvements could be made to the framework. The study adopted a critical theory philosophy and the approach was deductive in nature because current theories of KM and CRM were applied to the specific situation at SBM. Qualitative data collection methods were used within the two cycles of AR which included: three in-depth interviews with the heads of the business units within PBB, two focus group discussions (with the business bankers as well as the service teams) and document analysis of past research and project documents were among the qualitative data collection methods used. The data collected from all these sources was analysed using the thematic analysis method. The study resulted in a contribution to knowledge which was both conceptual and practical in nature. The MKC Relationship Management framework contributed to the ever growing theoretical knowledge of how KM and CRM can be integrated. Apart from this, the practical contribution was in the form of increased profitability in SBM Retail Banking, a structured manner of resolving issues that were resulting in negative customer experience. In addition to this, a thematic analysis approach to data analysis was applied in a banking research in Malawi. Further, a home grown IT system known as the I-serve system was commissioned at SBM and put to use to drive business results. The use of social media was also tested in order to take advantage of this growing technological revolution to drive business results using the mobile phone as a catalyst of knowledge sharing. This was among the first formally studied researches into use of such media for business in Malawi

Innovation Hot Spots: the Case of the Computer Services Sector in the Region of Attica, Greece

Elaborating on the notion innovation hot spots, we examine the case of the computer services sector in the Region of Attica, Greece. Fast-growing, geographically and industrially clustered firms are becoming an increasingly important factor for innovation and regional development. As a result, innovation hot spots enjoy rapid growth, leading to job creation, knowledge expansion and, in the best cases, sustainable development. The most recent European Trend Chart Reports (2004 and 2005) present Greece as innovation leader in the computer services sector. Computer services are characterized by a high knowledge creation and knowledge diffusion intensity meaning that the hot spots exploiting such services position high on an innovation intensity scale. Consulting, implementation, operations management and support services enjoy similar growth since they are complementary industries forming the Attica IT innovation hot spot. The purpose of our research within this field is twofold. First, we present the conditions under which this innovation leadership has emerged and come to flourish. We argue that growth in the Region of Attica has been boosted by the Information Society Program, the Olympic Games and the necessity for modernizing Greek firms, which leads them to favor investments in new technologies. Moreover, the region presents a favorable macroeconomic environment, characterized by high rates of development, increase of consumption and investments. Second, we analyze and propose a framework for maintaining the dynamics in the region -and in innovation hot spots in general- as there is a significant risk of rise-and-fall patterns occurring, leading to former hot spots transforming into “blind spotsâ€, and core competencies developed turning into core rigidities and cultural lock-in.

Simulating mixed-phase Arctic stratus clouds: sensitivity to ice initiation mechanisms

The importance of Arctic mixed-phase clouds on radiation and the Arctic climate is well known. However, the development of mixed-phase cloud parameterization for use in large scale models is limited by lack of both related observations and numerical studies using multidimensional models with advanced microphysics that provide the basis for understanding the relative importance of different microphysical processes that take place in mixed-phase clouds. To improve the representation of mixed-phase cloud processes in the GISS GCM we use the GISS single-column model coupled to a bin resolved microphysics (BRM) scheme that was specially designed to simulate mixed-phase clouds and aerosol-cloud interactions. Using this model with the microphysical measurements obtained from the DOE ARM Mixed-Phase Arctic Cloud Experiment (MPACE) campaign in October 2004 at the North Slope of Alaska, we investigate the effect of ice initiation processes and Bergeron-Findeisen process (BFP) on glaciation time and longevity of single-layer stratiform mixed-phase clouds. We focus on observations taken during 9–10 October, which indicated the presence of a single-layer mixed-phase clouds. We performed several sets of 12-h simulations to examine model sensitivity to different ice initiation mechanisms and evaluate model output (hydrometeors' concentrations, contents, effective radii, precipitation fluxes, and radar reflectivity) against measurements from the MPACE Intensive Observing Period. Overall, the model qualitatively simulates ice crystal concentration and hydrometeors content, but it fails to predict quantitatively the effective radii of ice particles and their vertical profiles. In particular, the ice effective radii are overestimated by at least 50%. However, using the same definition as used for observations, the effective radii simulated and that observed were more comparable. We find that for the single-layer stratiform mixed-phase clouds simulated, process of ice phase initiation due to freezing of supercooled water in both saturated and subsaturated (w.r.t. water) environments is as important as primary ice crystal origination from water vapor. We also find that the BFP is a process mainly responsible for the rates of glaciation of simulated clouds. These glaciation rates cannot be adequately represented by a water-ice saturation adjustment scheme that only depends on temperature and liquid and solid hydrometeors' contents as is widely used in bulk microphysics schemes and are better represented by processes that also account for supersaturation changes as the hydrometeors grow

An Interoperable Spatio-Temporal Model for Archaeological Data Based on ISO Standard 19100

Archaeological data are characterized by both spatial and temporal dimensions that are often related to each other and are of particular interest during the interpretation process. For this reason, several attempts have been performed in recent years in order to develop a GIS tailored for archaeological data. However, despite the increasing use of information technologies in the archaeological domain, the actual situation is that any agency or research group independently develops its own local database and management application which is isolated from the others. Conversely, the sharing of information and the cooperation between different archaeological agencies or research groups can be particularly useful in order to support the interpretation process by using data discovered in similar situations w.r.t. spatio-temporal or thematic aspects. In the geographical domain, the INSPIRE initiative of European Union tries to support the development of a Spatial Data Infrastructure (SDI) through which several organizations, like public bodies or private companies, with overlapping goals can share data, resources, tools and competencies in an effective way. The aim of this paper is to lay the basis for the development of an Archaeological SDI starting from the experience acquired during the collaboration among several Italian organizations. In particular, the paper proposes a spatio-temporal conceptual model for archaeological data based on the ISO Standards of the 19100 family and promotes the use of the GeoUML methodology in order to put into practice such interoperability. The GeoUML methodology and tools have been enhanced in order to suite the archaeological domain and to automatically produce several useful documents, configuration files and codebase starting from the conceptual specification. The applicability of the spatio-temporal conceptual model and the usefulness of the produced tools have been tested in three different Italian contexts: Rome, Verona and Isola della Scala

To boardrooms and sustainability: the changing nature of segmentation

Market segmentation is the process by which customers in markets with some heterogeneity are grouped into smaller homogeneous segments of more ‘similar’ customers. A market segment is a group of individuals, groups or organisations sharing similar characteristics and buying behaviour that cause them to have relatively similar needs and purchasing behaviour. Segmentation is not a new concept: for six decades marketers have, in various guises, sought to break-down a market into sub-groups of users, each sharing common needs, buying behavior and marketing requirements. However, this approach to target market strategy development has been rejuvenated in the past few years. Various reasons account for this upsurge in the usage of segmentation, examination of which forms the focus of this white paper. Ready access to data enables faster creation of a segmentation and the testing of propositions to take to market. ‘Big data’ has made the re-thinking of target market segments and value propositions inevitable, desirable, faster and more flexible. The resulting information has presented companies with more topical and consumer-generated insights than ever before. However, many marketers, analytics directors and leadership teams feel over-whelmed by the sheer quantity and immediacy of such data. Analytical prowess in consultants and inside client organisations has benefited from a stepchange, using new heuristics and faster computing power, more topical data and stronger market insights. The approach to segmentation today is much smarter and has stretched well away from the days of limited data explored only with cluster analysis. The coverage and wealth of the solutions are unimaginable when compared to the practices of a few years ago. Then, typically between only six to ten segments were forced into segmentation solutions, so that an organisation could cater for these macro segments operationally as well as understand them intellectually. Now there is the advent of what is commonly recognised as micro segmentation, where the complexity of business operations and customer management requires highly granular thinking. In support of this development, traditional agency/consultancy roles have transitioned into in-house business teams led by data, campaign and business change planners. The challenge has shifted from developing a granular segmentation solution that describes all customers and prospects, into one of enabling an organisation to react to the granularity of the solution, deploying its resources to permit controlled and consistent one-to-one interaction within segments. So whilst the cost of delivering and maintaining the solution has reduced with technology advances, a new set of systems, costs and skills in channel and execution management is required to deliver on this promise. These new capabilities range from rich feature creative and content management solutions, tailored copy design and deployment tools, through to instant messaging middleware solutions that initiate multi-streams of activity in a variety of analytical engines and operational systems. Companies have recruited analytics and insight teams, often headed by senior personnel, such as an Insight Manager or Analytics Director. Indeed, the situations-vacant adverts for such personnel out-weigh posts for brand and marketing managers. Far more companies possess the in-house expertise necessary to help with segmentation analysis. Some organisations are also seeking to monetise one of the most regularly under-used latent business assets… data. Developing the capability and culture to bring data together from all corners of a business, the open market, commercial sources and business partners, is a step-change, often requiring a Chief Data Officer. This emerging role has also driven the professionalism of data exploration, using more varied and sophisticated statistical techniques. CEOs, CFOs and COOs increasingly are the sponsor of segmentation projects as well as the users of the resulting outputs, rather than CMOs. CEOs because recession has forced re-engineering of value propositions and the need to look after core customers; CFOs because segmentation leads to better and more prudent allocation of resources – especially NPD and marketing – around the most important sub-sets of a market; COOs because they need to better look after key customers and improve their satisfaction in service delivery. More and more it is recognised that with a new segmentation comes organisational realignment and change, so most business functions now have an interest in a segmentation project, not only the marketers. Largely as a result of the digital era and the growth of analytics, directors and company leadership teams are becoming used to receiving more extensive market intelligence and quickly updated customer insight, so leading to faster responses to market changes, customer issues, competitor moves and their own performance. This refreshing of insight and a leadership team’s reaction to this intelligence often result in there being more frequent modification of a target market strategy and segmentation decisions. So many projects set up to consider multi-channel strategy and offerings; digital marketing; customer relationship management; brand strategies; new product and service development; the re-thinking of value propositions, and so forth, now routinely commence with a segmentation piece in order to frame the ongoing work. Most organisations have deployed CRM systems and harnessed associated customer data. CRM first requires clarity in segment priorities. The insights from a CRM system help inform the segmentation agenda and steer how they engage with their important customers or prospects. The growth of CRM and its ensuing data have assisted the ongoing deployment of segmentation. One of the biggest changes for segmentation is the extent to which it is now deployed by practitioners in the public and not-for-profit sectors, who are harnessing what is termed social marketing, in order to develop and to execute more shrewdly their targeting, campaigns and messaging. For Marketing per se, the interest in the marketing toolkit from non-profit organisations, has been big news in recent years. At the very heart of the concept of social marketing is the market segmentation process. The extreme rise in the threat to security from global unrest, terrorism and crime has focused the minds of governments, security chiefs and their advisors. As a result, significant resources, intellectual capability, computing and data management have been brought to bear on the problem. The core of this work is the importance of identifying and profiling threats and so mitigating risk. In practice, much of this security and surveillance work harnesses the tools developed for market segmentation and the profiling of different consumer behaviours. This white paper presents the findings from interviews with leading exponents of segmentation and also the insights from a recent study of marketing practitioners relating to their current imperatives and foci. More extensive views of some of these ‘leading lights’ have been sought and are included here in order to showcase the latest developments and to help explain both the ongoing surge of segmentation and the issues under-pinning its practice. The principal trends and developments are thereby presented and discussed in this paper

Influence of Market Orientation on the Relationship Between Customer Relationship Management Practices and Performance of Large-Scale Manufacturing Firms in Kenya

The main objective of the study was to measure the influence of market orientation on the relationship between customer relationship management practices and firm performance of large-scale manufacturing firms in Kenya. The population of the study comprised large-scale manufacturing firms that were members of the Kenya Association of Manufacturers (KAM). A descriptive cross-sectional survey was used. The target respondents were three top managers in each firm, and aggregated single scores were computed to lessen single source response bias. Data was analyzed through descriptive statistics and regression analysis. The results revealed that market orientation was a strong statistical predictor of firm performance. In addition, the moderating effect of market orientation on the association between CRM practices and performance (F=9.138, P-value<0.05) was found to be statistically significant.  The study supported findings of previous studies on the influence of CRM practices on firm performance. In addition, the study found that both CRM practices and market orientation had a positive and significant influence on performance. Further, the findings of the study support the theoretical link between CRM practices, market orientation and performance. Acknowledgment I thank and appreciate almighty God for this opportunity, his grace and favor. I also extend my sincere gratitude to my University Supervisors; Prof. Justus Munyoki, Dr. Joseph Owino and Dr. James Njihia for their valuable guidance, support and encouragement during the writing and completion of my Ph.D thesis. I also thank all members of the University of Nairobi Business Administration who contributed in one way or the other to make the writing of my thesis a success. I would also like to thank sincerely all top and senior managers in large-scale manufacturing firms in Kenya who participated in this research.  Finally my sincere appreciation goes to my family members for supporting, encouraging and being there for me during the entire journey of pursuing my Ph.D. program. Keywords: customer relationship management, market orientation, performance, large-scale manufacturing firm