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<p>   </p> <p>M&A activity is significant in today’s economy, as well as the need for M&A deals to be successful. Human resourcing is a necessary component in executing M&A deals and without it, anticipated growth through expected synergies cannot be achieved. What was particularly sparse in the extant literature of human resources support for M&A was the research into developing a function supporting M&A activities. The purpose of this research was to gather data of previously completed M&A deals at a business to examine if a correlation exists between the available M&A deal data and the number of human resources that were hired for integration. This study used existing data from previous M&A deals to develop a model for predicting the ideal number of human resources required to complete integration activities for future M&A deals. In a case wise diagnostic of the resulting model, 28 of the 31 previous M&A deals were correctly predicted by the model for the needed number of contractors. These findings answered the research question posed by this study, and these resulted in the creation of a multiple regression model with statistically significant coefficients for future M&A deals. A process model was developed and may be useful for businesses, by providing a methodology to leverage its own historical data to predict human resource needs during M&A integrations. This study provided businesses pursuing M&A a quantitative process for intentional planning to ensure that there are dedicated human resources to support the business strategy and outcomes. </p> <p>  </p>

Although interest in Performance Measurement and Management Systems (PMMS) and their application in various organizations has grown substantially, they are mainly developed for larger companies, and practitioners find it challenging to implement these systems in Small and Medium-sized Enterprises (SMEs). SME characteristics such as limited budget, lack of experience and knowledge, and pace of change usually make it difficult for managers to adopt and use traditional PMMS. One of the solutions to the lack of applicability of PMMS in the SME context is provided by integrating traditional PMMS with Business Intelligence (BI) and Business Analytics (BA) tools and technologies. The main goal of this thesis is to design and evaluate a Performance Management System based on Business Intelligence (PMS-BI) artifact that contains frameworks, architectures, tools and guidelines to help SMEs evaluate their current Performance Management System (PMS), select the most suitable PMS-BI system components, and design their own PMS based on BI. This artifact also guides them throughout the year by providing tools to assess their PMS-BI and maintain it. For this thesis, I applied a Design Science Research (DSR) methodology to collect the relevant information needed in the design step, design and develop the artifact, and evaluate it. I used the case study approach in the design's information collection and artifact evaluation phases. The final artifact consists of an executive document that guides the SME managers to design their own PMS-BI and helps them have a perception of its efficiency. In the data collection phase, I systematically reviewed the relevant literature to understand best practices, then interviewed eleven participants from seven SMEs to find out the gaps and requirements regarding PMS in SMEs. These participants are directly responsible for the PMS in their organization. Then by combining this information, I extracted the PMS-BI features. In the creative process of system design, I used these features to develop the artifact. Then, I presented the results to the same participants in the form of an executive summary. Finally, I used a “usability questionnaire” to understand participants' opinions about the artifact and document their feedback. My research fills the gap in the application of DSR in the PMS artifact development. This thesis helps researchers understand the application of DSR in designing and developing PMS artifacts for SMEs. In addition, the final artifact can be used by SMEs to develop and apply their own PMS-BI.

Artificial intelligence (AI) continues to gain traction and is increasingly reshaping the media and marketing communications field. While significant research has been conducted on the impact of AI in other fields, there is little empirical evidence on how AI is affecting the customer journey. The present study explored both organizations’ current use of AI tools and how customer perceptions about AI affect AI usage and adoption through the lens of diffusion of innovation theory. The research was conducted using mixed-method qualitative research. In-depth interviews and a case study content analysis were conducted to collect and analyze the data. The results suggest that consumer perceptions about AI impact levels of adoption when AI is recognized, but there appears to be cognitive dissonance regarding what constitutes AI and complicit acceptance of some of its benefits. Companies in different geographical locations have different levels of AI adoption along the diffusion of innovation stages.

Today’s dynamic and increasingly competitive market had expanded complexities for global businesses pressuring companies to start leveraging on Big Data solutions in order to sustain the global competitions by becoming more data-driven in managing their supply chains.The main purpose of this study is twofold, 1) to explore the implications of applying analytics designing supply chain networks, 2) to investigate the link between operational and strategic management levels when making strategic decisions using Analytics.Qualitative methods have been applied for this study to gain a greater understanding of the Supply Chain Analytics phenomenon. An inductive approach in form of interviews, was performed in order to gain new empirical data. Fifteen semi-structured interviews were conducted with professional individuals who hold managerial roles such as project managers, consultants, and end-users within the fields of Supply Chain Management and Big Data Analytics. The received empirical information was later analyzed using the thematic analysis method.The main findings in this thesis relatively contradicts with previous studies and existing literature in terms of connotations, definitions and applications of the three main types of Analytics. Furthermore, the findings present new approaches and perspectives that advanced analytics apply on both strategic and operational management levels that are shaping supply chain network designs.

A proliferation of social movements and a large group of young, politically active individuals have pushed corporate firms to pay more attention to increase resource allocation to corporate social responsibility. As of now, corporate social responsibility refers to a wide definition of general efforts made by firms to support society through social impacts. A review of current literature on corporate social responsibility reveals a gap in the way the quality is calculated as well as does not take into account the influence of social movements in the past couple of years. This paper assesses the specific social and political justice positions that firms have taken and whether these positions have had an effect on financial performance given the more actively conscious consumer base. Tobin’s Q is used to quantify these effects and show that positive corporate social responsibility increases financial performance, supporting the hypothesis, while negative corporate social responsibility does not significantly affect financial performance. Future research recommends a closer look at the industry differences in the subject as well as a clearer definition of the variance between the different issues that constitute corporate social responsibility.

As humans, we tackle many problems in complex societies and manage the complexities of networked social systems. Cognition and sociability are two vital human capabilities that improve social life and complex social interactions. Adding these features to smart devices makes them capable of managing complex and networked Internet of Things (IoT) settings. Cognitive and social devices can improve their relationships and connections with other devices and people to better serve human needs. Nowadays, researchers are investigating two future generations of IoT: social IoT (SIoT) and cognitive IoT (CIoT). This study develops a new framework for IoT, called CSIoT, by using complexity science concepts and by integrating social and cognitive IoT concepts. This framework uses a new mechanism to leverage the friendships between devices to address service management, privacy, and security. The framework addresses network navigability, resilience, and heterogeneity between devices in IoT settings. This study uses a new simulation tool for evaluating the new CSIoT framework and evaluates the privacy-preserving ability of CSIoT using the new simulation tool. To address different CSIoT security and privacy issues, this study also proposes a blockchain-based CSIoT. The evaluation results show that CSIoT can effectively preserve the privacy and the blockchain-based CSIoT performs effectively in addressing different privacy and security issues.

Context: Organizations strive to improve their business processes, and adaptive business processes have recently attracted much attention in that context. However, much research in that area has a narrow focus and does not consider a comprehensive view of the organization and its goals. In addition, Business Intelligence-based monitoring methods are useful for business process improvement but they often present information in a format that is not entirely suited for decision making. Objectives: The main objectives of this thesis are to provide: • A framework to model goals, processes, performance, situations, and improvement patterns using one modeling notation, in an iterative and incremental manner; • A method for the modeling and analysis of cause-effect relationships between indicators used to measure goal satisfaction; and • A technique allowing the detection of undesirable, sub-optimal conditions and the application of improvement patterns to the context Method: We develop an iterative framework based on the User Requirements Notation (URN) for modeling, monitoring and improving business organizations and their business processes. In addition, we introduce a formula-based evaluation algorithm allowing better analysis of the relationships between the business performance model elements (namely indicators). Furthermore, we use a profiled version of the Aspect-oriented URN (AoURN) with extensions (Business Process Pattern profile), for detecting undesirable conditions and for business process adaptation. We validate the novelty and feasibility of our approach by performing a systematic literature review, by assessing it against Zellner’ mandatory elements of a method, by developing tool support, by performing a pilot experiment and by using real-life examples from different sectors (healthcare and retail). Results: The two examples show that through the framework’s iterative approach, organizations at different levels of maturity in their business improvement journey can benefit from the framework. Furthermore, our systematic literature review shows that although there are existing works that enable our vision, most of them have a narrow focus and do not cover the three organization views that are of interest in this research. AoURN allows analysts to find repeated patterns in a context and bundle goal, performance and process models as a self-contained unit. AoURN hence enables the modeling of complex circumstances together with analysis techniques for what-if analysis and process adaptation, all using a unified and integrated modeling language. Finally, the pilot experiment suggests that, with some level of documentation and training, users who are already familiar with URN can use the profiled AoURN provided in this thesis as well as the discussed improvement patterns.

The Balanced Scorecard for the CrossFit Affiliate will provide unprepared or inexperienced managers with the necessary information to make informed operational decisions. The Balanced Scorecard incorporates financial data, customer satisfaction, internal operations, and future growth to create a series of cause-and-effect relationships that illustrate how decisions in one aspect of a business affect others. The Balanced Scorecard particularly benefits inexperienced managers leading small businesses. As overall small business growth increased, the boutique fitness industry expanded. The CrossFit brand rose in conjunction, but because corporate leaders insist on a hands-off licensing model, affiliate owners do not receive assistance from CrossFit corporate headquarters. In addition, the high frequency of affiliate owners without business education or management experience contributes to the likelihood of failure. Until CrossFit corporate actively guides its licensees, owners need a tool to promote business success. The Balanced Scorecard for the CrossFit Affiliate, comprised of data from one successful and four unsuccessful affiliates, will assist affiliate managers. Profitability, market share, retention, and the introduction of new services are the main drivers of the Balanced Scorecard and augment the analysis of relationships between the financial, customer, internal, and growth perspectives.

For manufacturing firms, having knowledge about customers is very important, in particular for the developers and designers of new products. A way in which software can help to build an information channel between the customers and the firm is through Customer Relationship Management (CRM) and Business Intelligence (BI) solutions. Customers’ data are captured into the Customer Relationship Management solution while Business Intelligence analyses them and provide clear processed information to the developers and designers of new products. In this study we have researched if this process occurs in the industry, if and how it can be improved and what advantages it could bring to manufacturing firms. We have carried out the data collection by interviewing experts in four companies, three software companies that provide Business Intelligence solutions and one manufacturing firm. We found out that those software solutions are not used to directly connect developers and designers to customers’ data, and that there are no specific technical obstacles that prevents this, if not managerial reasons rooted in everyday practice. We also uncovered facts that would help to make this process more efficient and make customers’ data even more relevant to development.