A neural network approach to predicting price negotiation outcomes in business-to-business contexts

Price premiums are a key profit driver for long-term business relationships. For sellers in business-to-business (B2B) relationships, it is important to have appropriate strategies to negotiate price increases without trading off the relationships with their buyers. This paper aims to understand the annual price negotiation processes of companies by predicting whether a seller’s reservation price, target price, and initial offer positively affect the price negotiation outcome between the sellers and buyers. Data from 284 B2B relationships of a chemicals supplier based in Germany was used to examine our research model. In order to capture the non-linear decisions that are involved in price negotiations and to address collinearity among negotiations’ determinants, neural network analysis was used to predict the factors that influence price negotiation outcome. The neural network model was then compared with the results from regression analysis. Compared to regression analysis, the neural network has a lower standard error, and it showed that target price played a more important role in B2B price negotiations. The neural network was also able measure non-linear, non-compensatory decisions that are involved in price negotiations. The results imply that neural networks should be more widely used by researchers to address the threats that multi-collinearity poses. For companies, the results imply that price targets should be actively managed, e.g. through clear financial aims or through seminars aiming to help sales personnel to establish more challenging negotiation aims

Sartorial symbols of social class elicit class-consistent behavioral and physiological responses: a dyadic approach.

Social rank in human and nonhuman animals is signaled by a variety of behaviors and phenotypes. In this research, we examined whether a sartorial manipulation of social class would engender class-consistent behavior and physiology during dyadic interactions. Male participants donned clothing that signaled either upper-class (business-suit) or lower-class (sweatpants) rank prior to engaging in a modified negotiation task with another participant unaware of the clothing manipulation. Wearing upper-class, compared to lower-class, clothing induced dominance--measured in terms of negotiation profits and concessions, and testosterone levels--in participants. Upper-class clothing also elicited increased vigilance in perceivers of these symbols: Relative to perceiving lower-class symbols, perceiving upper-class symbols increased vagal withdrawal, reduced perceptions of social power, and catalyzed physiological contagion such that perceivers' sympathetic nervous system activation followed that of the upper-class target. Discussion focuses on the dyadic process of social class signaling within social interactions

Agent-Based Computational Economics

Agent-based computational economics (ACE) is the computational study of economies modeled as evolving systems of autonomous interacting agents. Starting from initial conditions, specified by the modeler, the computational economy evolves over time as its constituent agents repeatedly interact with each other and learn from these interactions. ACE is therefore a bottom-up culture-dish approach to the study of economic systems. This study discusses the key characteristics and goals of the ACE methodology. Eight currently active research areas are highlighted for concrete illustration. Potential advantages and disadvantages of the ACE methodology are considered, along with open questions and possible directions for future research.Agent-based computational economics; Autonomous agents; Interaction networks; Learning; Evolution; Mechanism design; Computational economics; Object-oriented programming.

Contemplating Mindfulness at Work: An Integrative Review

Mindfulness research activity is surging within organizational science. Emerging evidence across multiple fields suggests that mindfulness is fundamentally connected to many aspects of workplace functioning, but this knowledge base has not been systematically integrated to date. This review coalesces the burgeoning body of mindfulness scholarship into a framework to guide mainstream management research investigating a broad range of constructs. The framework identifies how mindfulness influences attention, with downstream effects on functional domains of cognition, emotion, behavior, and physiology. Ultimately, these domains impact key workplace outcomes, including performance, relationships, and well-being. Consideration of the evidence on mindfulness at work stimulates important questions and challenges key assumptions within management science, generating an agenda for future research

Stock market forecasting using artificial neural networks

Forecasting events has always been of great interest for human beings. The basic examples of this process are forecasting the weather and environmental disasters. To forecast is the process of collecting information in order to complete and expand them suitably for future. Today, globalization of economic and competes in this regard for observing investors and recognition of profit making and trusting markets, such as currency and stock market, which are highly complex, is now one of the most important umbrages of investors. For forecasting in capital markets such as stock or currency, there exist different methods, like, regression, time series, genetics algorithm and fundamental analysis. From non-liner methods which might be used in different forecasting bases are Artificial Neural Networks ANN. ANN are one of the newest inventions of mankind which are used in variety of different scientific fields. Use of investors of technology and computer algorithms for forecasting has caused more profit and better business opportunities. ANN is a part of dynamic systems which by processing on data of time series, drive the roles and science of these data and register it with the structure of the network. This system is based on computational intelligence which copies the human’s mind feature in processing. In this survey, besides discussing the ANN for analyzing and processing data and also studying new methods, it is concluded that ANN are an appropriate model for forecasting capital markets such as stock and currency

Stock market forecasting using artificial neural networks

Forecasting events has always been of great interest for human beings. The basic examples of this process are forecasting the weather and environmental disasters. To forecast is the process of collecting information in order to complete and expand them suitably for future. Today, globalization of economic and competes in this regard for observing investors and recognition of profit making and trusting markets, such as currency and stock market, which are highly complex, is now one of the most important umbrages of investors. For forecasting in capital markets such as stock or currency, there exist different methods, like, regression, time series, genetics algorithm and fundamental analysis. From non-liner methods which might be used in different forecasting bases are Artificial Neural Networks ANN. ANN are one of the newest inventions of mankind which are used in variety of different scientific fields. Use of investors of technology and computer algorithms for forecasting has caused more profit and better business opportunities. ANN is a part of dynamic systems which by processing on data of time series, drive the roles and science of these data and register it with the structure of the network. This system is based on computational intelligence which copies the human’s mind feature in processing. In this survey, besides discussing the ANN for analyzing and processing data and also studying new methods, it is concluded that ANN are an appropriate model for forecasting capital markets such as stock and currency

Conversation Analytics: Can Machines Read between the Lines in Real-Time Strategic Conversations?

Strategic conversations involve one party with an informational advantage and the other with an interest in the information. This paper proposes machine-learning based measures to quantify the degrees of evasiveness and incoherence of the informed party during real-time strategic conversations. The specific empirical context is the questions and answers (Q&A) part of earnings conference calls during which managers endure high pressure as they face analysts’ scrutinizing questions. Being reluctant to disclose adverse information, managers may resort to evasive answers and sometimes respond less coherently due to increased cognitive load. Using data from the earnings calls of the S&P 500 companies from 2006 to 2018, we show that the proposed measures predict worse next-quarter earnings. Moreover, the stock market perceives incoherence as a negative signal. This paper contributes methodologically by developing two novel machine-powered measures to automatically evaluate behavioral cues during real-time strategic conversations. The proposed analytical tools are particularly beneficial to resource-constrained and informationally disadvantaged parties such as retail investors who may not be able to effectively trade on signals buried deep in unstructured conversational data

Predictive Contracting

This Article examines how contract drafters can use data on contract outcomes to inform contract design. Building on recent developments in contract data collection and analysis, the Article proposes “predictive contracting,” a new method of contracting in which contract drafters can design contracts using a technology system that helps predict the connections between contract terms and outcomes. Predictive contracting will be powered by machine learning and draw on contract data obtained from integrated contract management systems, natural language processing, and computable contracts. The Article makes both theoretical and practical contributions to the contracts literature. On a theoretical level, predictive contracting can lead to greater customization, increased innovation, more complete contract design, more effective balancing of front-end and back-end costs, better risk assessment and allocation, and more accurate term pricing for negotiation. On a practical level, predictive contracting has the potential to significantly alter the role of transactional lawyers by providing them with access to previously unavailable information on the statistical connections between contract terms and outcomes. In addition to these theoretical and practical contributions, the Article also anticipates and addresses limitations and risks of predictive contracting, including technical constraints, concerns regarding data privacy and confidentiality, the regulation of the unauthorized practice of law and the potential for exacerbating information inequality