Business Analytics for Sales Pipeline Management in the Software Industry: A Machine Learning Perspective

This study proposes a model designed to help sales representatives in the software industry to manage the complex sales pipeline. By integrating business analytics in the form of machine learning into lead and opportunity management, data-driven qualification support reduces the high degree of arbitrariness caused by professional expertise and experiences. Through the case study of a software provider, we developed an artifact consisting of three models to map the end-to-end sales pipeline process using real business data from the company’s CRM system. The results show a superiority of the CatBoost and Random Forest algorithm over other supervised classifiers such as Support Vector Machine, XGBoost, and Decision Tree as the baseline. The study also reveals that the probability of either winning or losing a sales deal in the early lead stage is more difficult to predict than analyzing the lead and opportunity phases separately. Furthermore, an explanation functionality for individual predictions is provided

Statistical inference of the mechanisms driving collective cell movement

Numerous biological processes, many impacting on human health, rely on collective cell movement. We develop nine candidate models, based on advection-diffusion partial differential equations, to describe various alternative mechanisms that may drive cell movement. The parameters of these models were inferred from one-dimensional projections of laboratory observations of Dictyostelium discoideum cells by sampling from the posterior distribution using the delayed rejection adaptive Metropolis algorithm (DRAM). The best model was selected using the Widely Applicable Information Criterion (WAIC). We conclude that cell movement in our study system was driven both by a self-generated gradient in an attractant that the cells could deplete locally, and by chemical interactions between the cells

Comparison of bioassays to biotype grape phylloxera (Daktulosphaira vitifoliae Fitch) on Vitis ssp.

Grape phylloxera biotypes exist throughout viticultural regions causing substantial economic losses. In the past different biotyping assays were employed to determine host adaption and potential harm of phylloxera strains or field populations. Standardised and efficient laboratory assays are required to define biotypes according to their aggressivity as well as to make accurate pest management and quarantine decisions. We aim to provide information on the consistency of the three most commonly used assays to accurately identify grape phylloxera biotype. Two phylloxera biotypes (A, C) were tested on two host plants (rootstock 'Teleki 5C' V. berlandieri x V. riparia and V. vinifera 'Riesling') using three assays: Simple isolation chamber, excised root bioassay and aseptic dual culture bioassay. Insect number, life table and plant-based response parameters (root galling) were compared. The simple isolation chamber and aseptic dual culture bioassay produced consistent results, whereas the excised root bioassay did not. We demonstrated that biotype results depend on whether the technique used is tuberosity- or nodosity-based. Pest management decision based on a single assay may inaccurately assess the phylloxera aggressivity potential. Thus, we recommend using two assay types which allows comparison of both root gall types

Organizational Readiness Concept for AI: A Quantitative Analysis of a Multi-stage Adoption Process from the Perspective of Data Scientists

Artificial intelligence (AI) is reshaping the business world in ways that enable organizations to create business value and reinvent their business models. Despite the great potential, organizations have difficulties in moving beyond the pilot stage and fully adopting AI applications. To better understand how organizations can implement AI into their core practices, we examine the impact of organizational readiness factors along the adoption process of AI through a quantitative research design. By integrating the organizational readiness factors into the multi-stage adoption process of AI, we unpack the interdependencies between these two literature streams. Due to the multi-faceted nature of organizations, we investigate the differentiating and opposing effects of the organizational readiness factors on the initiation, adoption, and routinization stages of AI

Uncovering Cultural Differences in Organizational Readiness for Artificial Intelligence: A Comparison between Germany and the United States

Artificial Intelligence (AI) transforms the business world by enabling organizations to leverage new business opportunities through its unique capabilities of self-learning and autonomous decision-making. To unlock the disruptive potential of AI, organizations seek to implement AI applications throughout their business landscape. However, from a cross-cultural perspective, national culture can influence the way organizations implement AI applications. To better understand cross-cultural differences on AI adoption, our study combines Hofstede’s national cultural framework with the organizational readiness concept for AI. We examined the moderating role of Hofstede’s national cultural dimensions on the organizational readiness factors of AI-process fit, financial resources, upskilling, collaborative work, and data quality. By conducting a multi-group analysis, we aim to identify national cultural differences between Germany and the US in AI adoption

Adoption of Artificial Intelligence in an Organizational Context: Analysis of the Factors Influencing the Adoption and Decision-Making Process

The emergence of Artificial Intelligence (AI) shifts the business environment to such an extent that this general-purpose technology (GPT) is prevalent in a wide range of industries, evolves through constant advancements, and stimulates complementary innovations. By implementing AI applications in their business practices, organizations primarily benefit from improved business process automation, valuable cognitive insights, and enhanced cognitive engagements. Despite this great potential, organizations encounter difficulties in adopting AI as they struggle to adjust to corresponding complex organizational changes. The tendency for organizations to face challenges when implementing AI applications indicates that AI adoption is far from trivial. The complex organizational change generated by AI adoption could emerge from intelligent agents’ learning and autonomy capabilities. While AI simulates human intelligence in perception, reasoning, learning, and interaction, organizations’ decision-making processes might change as human decision-making power shifts to AI. Furthermore, viewing AI adoption as a multi-stage rather than a single-stage process divides this complex change into the initiation, adoption, and routinization stages. Thus, AI adoption does not necessarily imply that AI applications are fully incorporated into enterprise-wide business practices; they could be at certain adoption stages or only in individual business functions. To address these complex organizational changes, this thesis seeks to examine the dynamics surrounding AI adoption at the organizational level. Based on four empirical research papers, this thesis presents the factors that influence AI adoption and reveals the impact of AI on the decision-making process. These research papers have been published in peer-reviewed conference proceedings. The first part of this thesis describes the factors that influence AI adoption in organizations. Based on the technology-organization-environment (TOE) framework, the findings of the qualitative study are consistent with previous innovation studies showing that generic factors, such as compatibility, top management, and data protection, affect AI adoption. In addition to the generic factors, the study also reveals that specific factors, such as data quality, ethical guidelines, and collaborative work, are of particular importance in the AI context. However, given these technological, organizational, and environmental factors, national cultural differences may occur as described by Hofstede’s national cultural framework. Factors are validated using a quantitative research design throughout the adoption process to account for the complexity of AI adoption. By considering the initiation, adoption, and routinization stages, differentiating and opposing effects on AI adoption are identified. The second part of this thesis addresses AI’s impact on the decision-making process in recruiting and marketing and sales. The experimental study shows that AI can ensure procedural justice in the candidate selection process. The findings indicate that the rule of consistency increases when recruiters are assisted by a CV recommender system. In marketing and sales, AI can support the decision-making process to identify promising prospects. By developing classification models in lead-and-opportunity management, the predictive performances of various machine learning algorithms are presented. This thesis outlines a variety of factors that involve generic and AI-specific considerations, national cultural perspectives, and a multi-stage process view to account for the complex organizational changes AI adoption entails. By focusing on recruiting as well as marketing and sales, it emphasizes AI’s impact on organizations’ decision-making processes

Hopefulness Among Non‐U.S.‐Born Latino Youth and Young Adults

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/88034/1/j.1744-6171.2011.00307.x.pd

Incentivizing Education: Seeing Schoolwork as an Investment, Not a Chore

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/89933/1/destin__oyserman__2010.pd

The moderating effects of religiosity on the relationship between stressful life events and delinquent behavior

Previous research has shown that many forms of strain are positively related to delinquency. Evidence also suggests that religiosity buffers the effects of strain on offending, but this issue requires further research. Using data from a national sample of adolescents, this study examined whether or not religiosity conditioned the relationship between strain and delinquency. This study also looked at the ability of social support, self-esteem, and depression to moderate the influence of strain on delinquent behavior. The findings here lend support to general strain theory in that strain had a direct positive effect on delinquency, yet there was little evidence that the relationship was moderated by religiosity or other conditioning variables. The roles of moderating variables on strain across genders were also considered. Originally published in Journal of Criminal Justice Vol. 36, No. 6 2008