The Impact of AI on Business, Economics and Innovation

Echeberria, Ana Landeta

doi:10.1007/978-3-030-88241-9_3

Cited by 3 publications

(4 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Section: Literature Reviewmentioning

confidence: 99%

“…Vast and powerful AI advancements [13] opened promising utilization avenues in the business domain [3], [14]. The last decades witnessed an accelerated pace of reliance on AIbased solutions in making knowledge-based decisions [14], [15]. Marketing research [16], [17] and attempts to model credit scoring [18] surveys are examples on the importance and potential of AI-based solutions in business domain.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Marketing research [16], [17] and attempts to model credit scoring [18] surveys are examples on the importance and potential of AI-based solutions in business domain. Telemarketing in particular is still one of the business domains that require more domain-specific solutions [14]; the clients usually tend to be interested more in human-based interactions over computer-generated communications [2]. Automated bots or computer-generated marketing content is still behind expectations and indirect marketing may fail to convince the clients to value the marketing offers [2].…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Modeling the Telemarketing Process Using Genetic Algorithms and Extreme Boosting: Feature Selection and Cost-Sensitive Analytical Approach

2023

View full text Add to dashboard Cite

Currently, almost all direct marketing activities take place virtually rather than in person, weakening interpersonal skills at an alarming pace. Furthermore, businesses have been striving to sense and foster the tendency of their clients to accept a marketing offer. The digital transformation and the increased virtual presence forced firms to seek novel marketing research approaches. This research aims at leveraging the power of telemarketing data in modeling the willingness of clients to make a term deposit and finding the most significant characteristics of the clients. Real-world data from a Portuguese bank and national socioeconomic metrics are used to model the telemarketing decision-making process. This research makes two key contributions. First, propose a novel genetic algorithm-based classifier to select the best discriminating features and tune classifier parameters simultaneously. Second, build an explainable prediction model. The best-generated classification models were intensively validated using 50 times repeated 10-fold stratified cross-validation and the selected features have been analyzed. The models significantly outperform the related works in terms of class of interest accuracy, they attained an average of 89.07% and 0.059 in terms of geometric mean and type I error respectively. The model is expected to maximize the potential profit margin at the least possible cost and provide more insights to support marketing decision-making.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Modeling the Telemarketing Process Using Genetic Algorithms and Extreme Boosting: Feature Selection and Cost-Sensitive Analytical Approach

2023

View full text Add to dashboard Cite

show abstract

“…Today, we can see advancements in assistance systems, but the need for a quantum leap to reach fully autonomous vehicles persists. Google started the development of self-driving vehicles with an artificial intelligence (AI) approach in 2009 [6]. Over the last few years, they have presented promising results with their supervised training of deep neural networks [7].…”

Section: A Current Statementioning

confidence: 99%

Validation of vehicles with automated features through the detection of behavioral differences between humans and machines

Schwarz

Rolland

Johnston

2022

2022 IEEE 28th International Conference on Engineering, Technology and Innovation (ICE/ITMC) &Amp; 31st International Associati

View full text Add to dashboard Cite

The development of automated vehicles has not achieved the desired success over the past decade, despite numerous technological and methodological successes. Currently, no production-ready autonomous vehicle on the market can operate without a human driver in a wide range of regions and environmental conditions. Due to the high complexity of the systems and the varied traffic scenarios, development, and validation are challenging and timeconsuming. With the aid of simulation technology, good validation in a scalable form can be performed during development. However, the current standards for traffic safety validation require a final validation under real conditions. In this work, a framework called Shadow System (SS) is presented, which independently captures the control commands of the human driver and the vehicle. This unique approach enables the detection of problems in automated systems by identifying behavioral differences. Up to a defined safety range allows the human driver and the vehicle to execute control commands without affecting each other. This makes it possible, for example, to determine how a human driver steers the vehicle compared to the automated system in a given traffic situation. The developed method enables safe use under real conditions, as control can be taken over directly by the human driver at any time. Scenarios identified as critical will provide information about the safety of the system during the validation process.

show abstract

How Artificial Intelligence Can Leverage Relationship Marketing Strategies

Rosário,

Casaca

2024

Advances in Marketing, Customer Relationship Management, and E-Services

View full text Add to dashboard Cite

Increased competition and consumer demand for personalized marketing messages and content have led to the increased adoption of relationship marketing. Through a systematic bibliometric literature review, this study aims not only to demonstrate how leveraging Artificial Intelligence can improve relationship marketing strategies but also to address the various ethical considerations and challenges of using AI in relationship marketing. The review includes 22 articles published up to 2024 in the Scopus® database, presenting up-to-date knowledge on the topic. The research findings demonstrate that the integration of Artificial Intelligence into relationship marketing strategies offers both theoretical insights into consumer behavior and practical tools for enhancing customer engagement, satisfaction, and loyalty. However, businesses need to approach Artificial Intelligence implementation thoughtfully, considering both the opportunities and challenges it presents.

show abstract

The Impact of AI on Business, Economics and Innovation

Cited by 3 publications

References 61 publications

Modeling the Telemarketing Process Using Genetic Algorithms and Extreme Boosting: Feature Selection and Cost-Sensitive Analytical Approach

Modeling the Telemarketing Process Using Genetic Algorithms and Extreme Boosting: Feature Selection and Cost-Sensitive Analytical Approach

Validation of vehicles with automated features through the detection of behavioral differences between humans and machines

How Artificial Intelligence Can Leverage Relationship Marketing Strategies

Contact Info

Product

Resources

About