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The purpose of this paper is to propose an algorithm that is based on the ant colony optimization (ACO) metaheuristic for producing harmonized melodies. ACO is a nature inspired metaheuristic where a colony of ants searches for an optimum of a function. The algorithm works in two stages. In the first stage it creates a melody. The obtained melody is then harmonized according to the rules of baroque harmony in the second stage. A multi‐objective version of the algorithm is also proposed, where each tier is optimized as a separate objective.

The ACO metaheuristic is adapted to graphs representing notes and chords. Desirability of a sequence of notes is measured by conformance to compositional rules. The fitness of a melody is evaluated with five equally weighted rules governing smoothness of the melody curve, its contour, tendency tone resolution, tone colors and the pitch of the final note. Harmonization is guided by six rules, grouped into three tiers of two rules each. These rules cover chord arrangement, voice distance, voice leading, harmonic progression, smoothness, and chord resolution. Rules of a tier do not score unless those of the previous tier yield high values.

The proposed algorithm improves on the only other existing musical ACO by adding the notion of harmony and by evolving voices codependently. The output is comparable to different types of other existing algorithms (genetic algorithm, rule‐based search algorithm) in the field. The multi‐objective variant significantly enhances solution quality and convergence speed, which makes extensions of the system for real time performance realistic.

This algorithm is the first ACO algorithm proposed for the problem of melody creation and harmonization.

This paper seeks to develop a theory and a practical qualitative modeling tool that accurately predicts the probability that an industrial sales lead will convert to a booking based on observable lead characteristics and one that can be used by a typical sales manager.

Past lead characterization models were reviewed and gaps and weaknesses examined. A qualitative model of lead characterization and qualification was then developed based upon the determinants of customer purchase decisions.

In total, 16 commonly available lead characteristics that represent proxies for the eight determinants of industrial customer purchase decisions were identified. These were adapted to a scoring model that enables sales and marketing personnel to assess the probability that a lead will convert to a booking.

Different companies use different criteria and different weighting factors for making purchase decisions; therefore, no single quantitative lead characterization model will be accurate for all companies. The proposed model has not yet been formally field validated, but that is planned for future work.

The qualitative model proposed herein is more comprehensive than previous models and should thus prove more accurate. This should prove useful to sales and marketing managers, who need to forecast bookings and sales, optimally allocate resources, and structure marketing and promotion efforts for maximum return.

Several lead characterization models have been proposed in the past, but none are based upon sound theory and none have been field validated. Use of this work should significantly increase selling effectiveness as well as the accuracy of sales projections.

Applications powered by artificial intelligence (AI) and machine learning (ML) have become a crucial factor for success in modern sales organizations. This chapter investigates how Salesforce achieves scalable AI for businesses of all sizes and explores sales applications of AI and machine learning that are most common across industries. It is divided into three sections. The first section gives an introduction to AI and machine learning. The second section shows how data and automated machine learning models provide the foundation for AI applications and explains how Salesforce achieves scalable AI and machine learning for business applications. The third section demonstrates how AI applications impact the modern sales organization and the work of sales representatives. AI does not replace humans; it allows sales organizations to better engage with prospects and customers. Sales representatives using AI outperform their counterparts that rely purely on traditional methods.

The purpose of this paper is to explore the various application of artificial intelligence (AI) to social media and digital advertising professionals and agencies to specialize to an advanced degree and maintain collaboration and creativity to bring a better return on investment.

Digital marketers are still oblivious to the importance of AI application, while some others simply do not know how to implement it. AI is currently acting as a significant disruption in digital and social media marketing worldwide.

Based on the literature review, the paper identifies the various AI applications in the field of digital media marketing.

This paper can serve as a useful guide for social media marketers to implement AI applications to impact digital marketing strategies better.

The purpose of this chapter is to examine the corporate water reporting of the selected South African listed food producers with regard to the activities of measuring, managing, engaging with their stakeholders, and disclosing of their water risks.

This chapter examined the sustainability and integrated reports of 14 food producer companies on the Johannesburg Stock Exchange (JSE), for the years 2013 and 2017. The company reports were examined using the Ceres Aqua Gauge™ as the framework.

The findings of this study are that there were improvements in water disclosure from 2013 to 2017. Most companies are disclosing the basic water reporting requirements. However, critical areas around stakeholder engagement and supply chain water management were found to be lacking.

This research contributes to the body of knowledge around water disclosure and increases the awareness of water scarcity and poor water quality in South Africa. Furthermore, the study highlights that the food producers could be doing a lot more with regard to water sustainability in their businesses and the country.

Research at the intersection of neurotoxicology, cognitive neuroscience, genetics, and behavior – “toxicogenomics” – promises to improve understanding of behavior and public policies. Many toxicants modify neurotransmitters with effects depending on genotype, development, and experience. For example, lead and manganese downregulate dopaminergic or serotonergic function, weaken behavioral inhibition, and are positively correlated with hyperactivity (ADHD), substance abuse, or violent crime. Within this context, the chapter argues that neurotoxicology holds great promise for improving public policies in areas such as criminal justice and education. After discussing research methods, the chapter surveys previous work in neurotoxicology as it pertains to educational outcomes and to crime. Data are summarized linking: (1) children's blood lead levels with poor educational performance; (2) hair and blood levels of heavy metals with violent crime; and (3) uptake of multiple toxicants with pollution from abandoned mines. The chapter concludes by drawing lessons for ongoing research agendas at the intersection of neurotoxins, behavior, and policy.

This paper investigates the information dilemma in negotiations: if negotiators reveal information about their priorities and preferences, more efficient agreements may be reached but the shared information may be used strategically by the other negotiator, to the revealers' disadvantage. We present a theoretical model that focuses on the characteristics of the negotiators, the structure of the negotiation, and the available incentives; it predicts that experienced negotiators will out‐perform naive negotiators on distributive (competitive) tasks, especially when they have information about their counterpart's preferences and the incentives are high—unless the task is primarily integrative, in which case information will contribute to the negotiators maximizing joint gain. Two experiments (one small, one large) showed that the revelation of one's preferences was costly and that experienced negotialors outperformed their naive counterparts by a wide margin, particularly when the task and issues were distributive and incentives were large. Our results help to identify the underlying dynamics of the information dilemma and lead to a discussion of the connections between information and social dilemmas and the potential for avoiding inefficiencies.

Through the use of netnography, the purpose of this paper is to investigate the interaction of 192 inside sales agents who collaborate within discussion forums to create communities of practice (CoPs). Drawing on situated learning theory and inside salespersons’ discussion forums, this study showcases how inside sales agents use CoPs to better their sales activity knowledge. This paper discovered how inside sales agents reach outside their organization to seek information within their mesosystem to positively impact sales activity knowledge outcomes.

This study used the use of netnography to follow the online community from afar, observing and capturing the essence of interaction without intruding on the conversation. Data from two inside sales professional discussion forums, 192 inside sales agents, produced nearly 67,161 words or 496 double-spaced pages.

This research demonstrates the power of CoPs. Inside sales agents seek information outside their company, within their mesosystem, to gain knowledge to improve sales activities. As boundary spanners, inside sales agents are not restricted, but rather, proactively find ways to consistently keep learning.

Organizations can and should implement internal CoPs to allow the exchange between sales agents in a more controlled matter. Furthermore, sales managers should proactively seek extern CoPs and introduced them to their organization as supplemental training.

The use of netnography is limited in sales literature. The research provides support for the use of netnography as it provides researchers firsthand access to multiple sales roles. This unadulterated access provides sales researchers the opportunity to study the many facets of sales, with pure, genuine data.

Reports a study to investigate the applicability of situational leadership theory within the Republic of China (Taiwan). The study was conducted in a large construction company with a broad diversity of managerial responsibilities. The LEAD‐Self instrument was given to a large sample of managers, to determine their perception of their own leadership style. Employees completed the LEAD‐Other questionnaire, to give their perceptions of their manager. Productivity scores were also established for each manager using measures of absenteeism, turnover, overall profitability and quality of work. The results indicated that the situational leadership theory does have applicability in Taiwan; there was a good match between the self‐perception of leaders and perceptions by peers, superiors and subordinates and between the LEAD‐Self score and LEAD‐Other; also that leadership adaptability was directly related to productivity as measured by absenteeism, turnover rates, profitability and, to a lesser extent, quality.