Exploring entrepreneurial pivoting and the factors that trigger pivots by tech startups

Technology entrepreneurship is an emerging domain in the field of entrepreneurship and the practice-oriented method called the Lean Startup approach (LSA) has made a big impact in this area. However, many technology startups continue to have survivability issues. This study focuses on understanding the theory of entrepreneurial pivoting and its associated factors. In this study, we have collected secondary data comprising 80 tech startups to validate the different types of pivots they pursued by the companies and the factors that triggered the pivoting. The most common pivots among these were found to be customer segment pivot and customer need pivot

Investigating the Entrepreneurial Pivoting Experience of UK-based Technology Start-ups

Technology start-ups pivot to create and sustain the value proposition. This research study focuses on understanding the phenomenon of entrepreneurial pivoting of tech start-ups, including the type of pivots, factors that cause pivoting and impact of technology maturity on pivoting. The study has adopted the qualitative research method, and interviews have been conducted with high-tech entrepreneurs across the United Kingdom. The study was designed to establish the correlation between the factors that trigger pivoting and the types of pivot pursued by the tech start-ups. From the preliminary analysis of interviews, we have validated the existing types of pivots and the factors that trigger pivoting from the literature. We have also identified two new pivots and two new factors that cause pivoting. The exploratory study has practical significance to the work for tech entrepreneurs and broader stakeholders that have an interest in the performance and sustainability of tech start-ups

User concerns about Facebook: Are they important

Previous studies investigated various issues and concerns related to Facebook, but lacks to compare between the importance of these concerns. This study contributes in filling this research gap by (i) consolidating the key issues and concerns and confirming their importance to Facebook users, (ii) examining the relative importance ranking of these issues and concerns, and (iii) answering the question of why people keep using Facebook despite of all the concerns and issues. To fulfil the requirements of the examination, a mixed method research approach was carried out. A Web-based questionnaire was first rolled out on Facebook to solicit the needed responses from its users. The analysis results statistically verified the importance of all the issues and concerns albeit being perceived differently in importance. Privacy concerns were rated the highest among all the issues, whereas issues related to the design aspects were rated the lowest. An open-ended question followed the questionnaire, and the qualitative analysis of its replies confirmed that Facebook is an indispensable global channel of communication that cannot be easily ignored and, with the nonexistence of a better alternative, the benefits of keep using Facebook way overshadow any issues users perceive about it

Nurturing Women Entrepreneurship, UK and Bahrain perspectives

The outcomes of two studies, conducted independently, in Bahrain and UK are presented in this workshop. The premise of both research studies were to address under representation of women entrepreneurship and to identify factors influencing the low uptake of entrepreneurship amongst women. The challenges facing women entrepreneurs as identified from the studies will be discussed and comparisons will be made where possible between the two results

Predictive Ensemble Modelling: An Experimental Comparison of Boosting Implementation Methods

This paper presents the empirical comparison of boosting implementation by reweighting and resampling methods. The goal of this paper is to determine which of the two methods performs better. In the study, we used four algorithms namely: Decision Stump, Neural Network, Random Forest and Support Vector Machine as base classifiers and AdaBoost as a technique to develop various ensemble models. We applied 10-fold cross validation method in measuring and evaluating the performance metrics of the models. The results show that in both methods the average of the correctly classified and incorrectly classified are relatively the same. However, average values of the RMSE in both methods are insignificantly different. The results further show that the two methods are independent of the datasets and the base classier used. Additionally, we found that the complexity of the chosen ensemble technique and boosting method does not necessarily lead to better performance

Prediction of Breast Cancer Survivability using Ensemble Algorithms

In this paper we propose new ensemble cancer survivability prediction models based three variants of AdaBoost algorithm to extend the application range of ensemble methods. In our approach to address the problem of low efficiency and slow speed we use Random Forest, Radial Basis Function and Neural Network algorithms as base learners and AdaBoostM1, Real AdaBoost and MultiBoostAB as ensemble techniques. AdaBoost is a technique that iteratively trains its base classifiers to generate committee of strong classifiers to improve their performance and prediction accuracy. There has been major research in ensemble modeling in statistics, medicine, technology and artificial intelligence in the last three decades. This might be because of the effectiveness and reliability of the technique in helping medical and other professionals in diagnosis and incident predictions. However, there is a need to improve the accuracy of the existing models address current challenges. In this paper we use state of the art Wisconsin breast cancer dataset in training and testing the proposed hybrid models. The performance of the models was evaluated using the following performance metrics: Accuracy, RMSE, TP Rate, FP Rate, Precision and ROC Area. The results of our study shows that MBAB-RF and AdaM1-RF models have the same accuracy prediction of 97% and RA + ANN has the worst prediction accuracy of 88%. Additionally we found that all ANN models requires more time to train its committee of classifiers compared to RFB models that requires the least time despite the fact that RBF is a family of ANN algorithm

A Qualitative Research Study of the Tech Startup Journey through Entrepreneurial Pivoting

Purpose – As part of the entrepreneurial journey, high-tech entrepreneurs are faced with the need to develop a competitive value proposition and leverage emerging technology to strengthen the value proposition. Entrepreneurial pivoting can be adopted to address this requirement since it enables the startup to validate and refine the company’s strategy and business model. Therefore, this research study provides an empirical investigation of the pivoting concept explained in the context of the lean startup approach (LSA) to improve our understanding of the entrepreneurial journey for high-tech entrepreneurs. Methodology – A qualitative research method was conducted by interviewing thirty high-tech entrepreneurs across the United Kingdom to validate the theories behind the LSA and identify new insights on entrepreneurial pivoting. Findings – The research study has validated the existing types of pivots and identified two new pivots (giving 16 in total). The study validated the existing factors that trigger a tech startup to change its direction and identified three new factors (giving 14 in total). The research also determined that there can be a domino effect in pivoting and the value proposition can be created and sustained through pivoting. Originality – This study provides empirical evidence on pivots and the factors associated with pivots. Furthermore, it helps in understanding the influence of the phases of technology entrepreneurship on pivoting. The study also discusses the challenges faced by tech startups while pursuing pivots, the domino effects in pivoting and has found evidence that pivoting eventually leads to achieving the desired results

Enhancing Ensemble Prediction Accuracy of Breast Cancer Survivability and Diabetes Diagnostic using optimized EKF-RBFN trained prototypes, The 10th International Conference on Soft Computing and Pattern Recognition

We are in a machine learning age where several predictive applications that are life dependent are made by machines and robotic devices that relies on ensemble decision making algorithms. These have attracted many researchers and led to the development of an algorithm that is based on the integration of EKF, RBF networks and AdaBoost as an ensemble model to improve prediction accuracy. Firstly, EKF is used to optimize the slow training speed and improve the efficiency of the RBF network training parameters. Secondly, AdaBoost is applied to generate and combine RBFN-EKF weak predictors to form a strong predictor. Breast cancer survivability and diabetes datasets used were obtained from the UCI repository. Results are presented on the proposed model as applied to Breast cancer survivability and Diabetes diagnostic predictive problems. The model outputs an accuracy of 96% when EKF-RBFN is applied as a base classifier compare to 94% when Decision Stump is applied and AdaBoost as an ensemble technique in both examples. The output accuracy of ensemble AdaBoostM1-Random Forest and standalone Random Forest models is 97% respectively. The study has gone some way towards enhancing our knowledge and improving the prediction accuracy through the amalgamation of EKF, RBFN and AdaBoost algorithms as an ensemble model