Artificial Intelligence in Managing Market Relationships

Daskou, Sofia; Mangina, Eleni

doi:10.1300/j366v02n01_06

Cited by 13 publications

(15 citation statements)

References 31 publications

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“…food supply agents) can be monitored from a multi-agent software system, by using intelligent specialised software agents, which will simulate the procedure of data analysis to extract useful information for the relationships between commercial partners. The key aspects of this work, based on the previous research in the field (Daskou & Mangina, 2003), are:…”

Section: Introductionmentioning

confidence: 99%

The changing role of information technology in food and beverage logistics management: beverage network optimisation using intelligent agent technology

Mangina

Vlachos

2005

Journal of Food Engineering

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Section: Introductionmentioning

confidence: 99%

The changing role of information technology in food and beverage logistics management: beverage network optimisation using intelligent agent technology

Mangina

Vlachos

2005

Journal of Food Engineering

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“…According to Daskou and Mangina (2003), conceptual frameworks are structures which scholars believe can best summarise the natural progression of the phenomenon investigated.…”

Section: Conceptual Framework For Ai In B2b Marketingmentioning

confidence: 99%

“…Their article systematically discusses the implementation of AI in relation to three different kinds of market knowledge (i.e., user knowledge, external marketing knowledge and customer knowledge) within a B2B marketing environment. Daskou and Mangina (2003) formulate an unique framework of a new AI system designed for integrating customer surveys and a database of customer loyalty programs. This model can help companies to establish an information-based connection with both their retailers and their consumers.…”

Section: Conceptual Framework For Ai In B2b Marketingmentioning

confidence: 99%

“…Furthermore, no study has analysed trends in research on the application of AI for B2B marketing innovation or provided suggestions for directions in future research. call for further work to explore the challenges in the application of AI for B2B marketing innovation, especially in relation to the workforce to support businesses in developing their innovative practices (Daskou and Mangina, 2003;Paschen et al, 2020a;Fish et al, 1995). In light of the above, the present study sought to address two research questions:…”

Section: Introductionmentioning

confidence: 99%

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Artificial intelligence in business-to-business marketing: a bibliometric analysis of current research status, development and future directions

Han

Lam

Zhan

et al. 2021

IMDS

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PurposeAlthough the value of artificial intelligence (AI) has been acknowledged by companies, the literature shows challenges concerning AI-enabled business-to-business (B2B) marketing innovation, as well as the diversity of roles AI can play in this regard. Accordingly, this study investigates the approaches that AI can be used for enabling B2B marketing innovation.Design/methodology/approachApplying a bibliometric research method, this study systematically investigates the literature regarding AI-enabled B2B marketing. It synthesises state-of-the-art knowledge from 221 journal articles published between 1990 and 2021.FindingsApart from offering specific information regarding the most influential authors and most frequently cited articles, the study further categorises the use of AI for innovation in B2B marketing into five domains, identifying the main trends in the literature and suggesting directions for future research.Practical implicationsThrough the five identified domains, practitioners can assess their current use of AI and identify their future needs in the relevant domains in order to make appropriate decisions on how to invest in AI. Thus, the research enables companies to realise their digital marketing innovation strategies through AI.Originality/valueThe research represents one of the first large-scale reviews of relevant literature on AI in B2B marketing by (1) obtaining and comparing the most influential works based on a series of analyses; (2) identifying five domains of research into how AI can be used for facilitating B2B marketing innovation and (3) classifying relevant articles into five different time periods in order to identify both past trends and future directions in this specific field.

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“…As a result, it is expected that the socialbot industry will be worth $1.25 billion by 2025 (Grand View Research, 2019). There are further benefits to using AI as it enables brands, retailers and companies to simultaneously process data and use information about individual customers (Daskou and Mangina, 2003), which can enhance segmentation and targeted marketing. Additionally, many brands are starting to use SNS as a sales platform with 40% of business owners using these channels to generate profits (Arnold, 2018), indicating a suspected shift from e-commerce to social shopping (e.g.…”

mentioning

confidence: 99%

Introducing the socialbot: a novel touchpoint along the young adult customer journey

Wilson-Nash

Goode

Currie

2020

EJM

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Purpose The purpose of this paper is to contribute to the special issue theme by exploring customer response to automated relationship management tactics on social media channels. Design/methodology/approach A total of 17 in-depth interviews of young adults, ranging from the age of 19 to 26, were conducted. From this, customer journey maps were compiled incorporating socialbots as a valuable touch point along the service delivery cycle. Findings The research frames the socialbot as a valued customer service agent to young adults with some favouring this over telephone and email communication methods. Younger consumers respond positively to the quick resolution offered by the socialbot mechanism with most acknowledging that the bot is only able to manage simplified requests. Human-to-human customer relationship management is preferential when the query reaches critical mass. Research limitations/implications Socialbots on Facebook Messenger provided the research context for this study; therefore, other platforms and owned website bots should be considered in future studies. Practical implications This research identifies the younger generation as a key target market for the development of customer service-related bots. Originality/value To the best of the authors’ knowledge, this is the first study to examine the socialbot as an automated touch point in the customer journey and contributes knowledge to the growing body of literature focussed on artificial intelligence in customer service. Moreover, it provides valuable qualitative insights into how socialbots influence the customer experience and related outcome measures.

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Artificial Intelligence in Managing Market Relationships

Cited by 13 publications

References 31 publications

The changing role of information technology in food and beverage logistics management: beverage network optimisation using intelligent agent technology

The changing role of information technology in food and beverage logistics management: beverage network optimisation using intelligent agent technology

Artificial intelligence in business-to-business marketing: a bibliometric analysis of current research status, development and future directions

Introducing the socialbot: a novel touchpoint along the young adult customer journey

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