AI and digitalization in relationship management: Impact of adopting AI-embedded CRM system
Introduction
Customer relationship management (CRM) is considered a strategic macro process. It aims to ensure building and sustaining a profit-maximization portfolio concerning customer relationship with the firms (Saura, Palos-Sanchez, & Blanco-González, 2019). Many studies have been published on CRM in the B2C context, but explicit research on CRM in the B2B relationship context is still at a rudimentary stage (Irsic, 2017). B2B CRM activities help to analyze the customers’ needs by examining the multifarious customer data (Kim, Park, Dubinsky, & Chaiy, 2012). Some of the huge-sized databases for such B2B relationship management are Matrix Marketing, Ampliz Salesbuddy, Easy Leadz, Leadspace, and so on (Ampliz, 2020). Firms use these B2B databases for accurate and faster targeting opportunities and cross-channel integration, and for effective collaboration with vendors and partners. Analysis of such a huge volume of customer data is difficult for humans. Therefore, appropriately using technology like artificial intelligence (AI) is essential (Josiassen et al., 2014, Wang and Feng, 2012). This invites the need to study AI-integrated CRM systems in the B2B context (Harrigan et al., 2015, San-Martína et al., 2016). AI-integrated CRM systems are associated with automated decision-making capabilities, which also impact the operational efficiency of the firms in the B2B context, thus influencing satisfaction in the B2B relationship (Alshare and Lane, 2011, Hagen et al., 2020, Huang and Rust, 2018). With the help of AI-CRM, many routine activities can be automated to effectively reduce time compared to those conducted manually. Some of the routine tasks are automated forecasting, auto-processing data inputs, and retrieving and auto-determining the call list and response using NLP (Natural Language Processing) technology. Information transfer is the main ingredient of B2B relationship management (Michaelidou, Siamagka, & Christodoulides, 2011). It has to do with offering and receiving information, thus assisting firms to select and purchase B2B products or services (Agnihotri, Dingus, Hu, & Krush, 2016). There are several benefits that could be derived by using AI-CRM in B2B context. A few such benefits are automated partner selection, lead generation, appropriate segmentation, prioritization without manual intervention, and virtual assistance for vendor selection. An AI-integrated CRM system can help a firm automate its decision-making without human interference (Chatterjee, 2019). This hybrid system (AI-CRM system) can assist firms involved in B2B relationships to perform automated routine tasks and improve customization, segmentation, and prioritization of the acquired customer data. AI-CRM systems could eventually impact firms’ performance (Chatterjee et al., 2020, Gotteland et al., 2020). However, AI is an emerging technology. Its integration with CRM in the B2B context might pose problems for the employees of the organizations to quickly adapt to AI-CRM technology. This rapid acquisition of a new technology might cause technological turbulence (Song et al., 2005, Agnihotri et al., 2017). Conversely, the emergence of AI and ML systems in the B2B context has provided favorable support to the top management of the firms (Chatterjee et al., 2020, Thakur et al., 2016). Rapid and unpredictable change in technology raises some issues with users, which is conceptualized as technology turbulence (Song et al., 2005). To use the new system, firms are required to quickly upgrade the necessary arrangements. This concept gives rise to the sense of technology turbulence (Ullah, Iqbal, & Shams, 2020). In the context of the present study, introducing AI-CRM systems in firms is found to be initially hindered by the users on various pretexts, which conforms to status quo bias (SQB) theory (Samuelson & Zeckhauser, 1988). The difference between just becoming automated and using AI in B2B CRM is that AI can recommend best available partners for a particular initiative, can provide virtual assistance for any vendor- or partner-related queries, can guide the team to plan and strategize a pragmatic roadmap towards improving lead generation, and can optimize opportunities, which could ultimately help the firm realize increased revenue. Nevertheless, there are no studies on how AI-CRM adoption for B2B relationship management impacts on automated decision making and how it influences the firms’ operational efficiency, thus affecting firm performance. Nor is there any study on the moderating effects of technological turbulence and leadership support on firm performance in the context of an AI-integrated CRM system in B2B relationship management. To fill this gap, the aims of this study are as follows.
- [i].
To examine the impact of an AI-embedded CRM system on firm performance.
- [ii].
To determine how an AI-embedded CRM system impacts B2B relationship management.
- [iii].
To investigate the impact of ‘technology turbulence’ and ‘leadership support’ as moderators towards adoption of AI embedded CRM system for B2B relationship management.
Section snippets
Literature review
A huge volume of customer data can be effectively analyzed without requiring human interference by using an AI-integrated CRM system (Ferraris et al., 2017, Heide et al., 2007). In the B2B context, firms can use inputs from analyzing customer data to strengthen the CRM system (Herhausen et al., 2020, Kumar et al., 2019, Al-Omoush et al., 2021). B2B relationship marketing is interpreted as ‘the marketing of goods and services to commercial enterprises, governments, and other non-profit
Theoretical background
In the context of AI-CRM adoption for B2B relationship management, the firms need to emphasize how to utilize the available resources, especially various information of customers (Agnihotri et al., 2016, Chatterjee et al., 2020, Davenport et al., 2020). In analyzing customer data with AI technology, the CRM system for B2B relationship management should improve firm performance. This requires firms to make best use of available resources. In this scenario, the management of the firm needs to
Research methodology
To validate the proposed conceptual model, the partial least squares (PLS) structural equation modeling (SEM) technique was preferred. In terms of the observation of Hair, Howard, and Nitzle (2020), we note that the PLS-SEM technique is considered the most popular composite method for estimating structural equation modeling (SEM). This process comprises two parts. First is the research model in which we estimate the loadings of the items, and the average variance extracted, composite
Measurement instrument and discriminant validity test
The loading factor (LF) of each instrument (item) was measured to assess the convergent validity of each item. To measure the validity, consistency, reliability, and multicollinearity defect, average variance extracted (AVE), Cronbach’s alpha (α), composite reliability (CR), and variance inflation factor (VIF) of each construct was measured. The values of all these parameters were found within allowable range. The estimate of measurement instruments is shown in Table 2.
On estimation, it was
Theoretical contribution
This study makes several theoretical contributions to the extent literature. Several studies have analyzed how an AI-integrated CRM system effects firm performance (Chatterjee, 2019, Chatterjee et al., 2020). However, this is the first to have analyzed the effects an AI-integrated CRM system has on firm performance in the B2B context. This is also the first study to consider the effects of both technology turbulence and leadership support, as moderators, on firm performance when using an AI-CRM
Conclusion, limitations of the study with direction for future scope
This study has focused on how adoption of an AI-integrated CRM system in the B2B context can influence firm performance. This study has developed a model based on this concept. While developing the model, we emphasized the importance of the B2B relationship in the context of using an AI-integrated CRM system amongst the firms involved. The model has achieved success, as its predictive power is as high as 73%. We have focused on improving the B2B relationship management in the dynamic
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgement
None.
Dr. Sheshadri Chatterjee is a post-doctoral research scholar at Indian Institute of Technology Kharagpur, India. He has completed PhD from Indian Institute of Technology Delhi, India. He is having work experience in different multinational organizations such as Microsoft Corporation, Hewlett Packard Company, IBM and so on. Sheshadri has published research articles in several reputed journals such as Government Information Quarterly, Information Technology & People, Journal of Digital Policy,
References (117)
- et al.
Social media: Influencing customer satisfaction in B2B sales
Industrial Marketing Management
(2016) - et al.
Examining the role of sales-based CRM technology and social media use on post-sale service behaviors in India
Journal of Business Research
(2017) - et al.
The determinants of social CRM entrepreneurship: An institutional perspective
Journal of Business Research
(2021) - et al.
Organisational, technical and data quality factors in CRM adoption — SMEs perspective
Industrial Marketing Management
(2011) - et al.
How does CRM technology transform into organizational performance? A mediating role of marketing capability
Journal of Business Research
(2010) - et al.
Implementation effects in the relationship between CRM and its performance
Journal of Business Research
(2018) - et al.
Modelling CRM in a social media age
Australasian Marketing Journal (AMJ)
(2015) - et al.
The digital marketing capabilities gap
Industrial Marketing Management
(2020) - et al.
CRM and the bottom line: Do all CRM dimensions affect firm performance?
International Journal of Hospitality Management
(2014) - et al.
A process-oriented perspective on customer relationship management and organizational performance: An empirical investigation
Industrial Marketing Management
(2010)
Organizational characteristics and the CRM adoption process
Journal of Business research
Technological advancements and B2B international trade: A bibliometric analysis and review of industrial marketing research
Industrial Marketing Management
The impact of supply chain management practices on competitive advantage and organizational performance
Omega: The International Journal of Management Science
The adoption of technological innovations in a B2B context and its impact on firm performance: An ethical leadership perspective
Industrial Marketing Management
Business angel early stage decision making
Journal of Business Venturing
Usage, barriers and measurement of social media marketing: An exploratory investigation of small and medium B2B brands
Industrial Marketing Management
Reflections on ‘social media: Influencing customer satisfaction in B2B sales’ and a research agenda
Industrial Marketing Management
The future of marketing
International Journal of Research in Marketing
Challenges of CRM implementation in business- to-business markets: A contingency perspective
Journal of Personal Selling & Sales Management
Why PLS-SEM is suitable for complex modelling? An empirical illustration in big data analytics quality
Production Planning & Control
Predicting student-perceived learning outcomes and satisfaction in ERP courses: An empirical investigation
Communications of the Association for Information Systems
Estimating nonresponse bias in mail surveys
Journal of Marketing Research
In pursuit of enhanced customer retention management: Review, key issues, and future directions
Customer Needs and Solutions
Online reviews as a feedback mechanism for hotel CRM systems
Anatolia
Survey response rate levels and trends in organizational research
Human Relations
Ten steps in scale development and reporting: A guide for researchers
Communication Methods and Measures
From the editors: Common method variance in international business research
Journal of International Business Studies
Business-to-Business and Institutional Marketing, New marketing strategies: Evolving flexible processes to fit market circumstance
Security and privacy issues in E-Commerce: A proposed guidelines to mitigate the risk
IEEE International Advance Computing Conference.
Impact of AI regulation on intention to use robots: From citizens and government perspective
International Journal of Intelligent Unmanned Systems
Adoption of ubiquitous customer relationship management (uCRM) in enterprise: leadership support and technological competence as moderators
Journal of Relationship Marketing
Adoption of AI-integrated CRM system by Indian industry: From security and privacy perspective
Information and Computer Security, In Press
Are CRM systems ready for AI integration? A conceptual framework of organizational readiness for effective AI-CRM integration
The Bottom Line
Establishing rigor in mail survey procedures in international business research
Journal of World Business
Customer relationship management and firm performance
Journal of Information Technology
Sobre modas y realidades: CRM o el nuevo marketing digital
Nueva Econ. y Empres
How artificial intelligence will change the future of marketing
Journal of the Academy of Marketing Science
How and when do big data investments pay off? The role of marketing affordances and service innovation
Journal of the Academy of Marketing Science
An empirical examination of customers’ adoption of m-banking in India
Journal of Marketing Intelligence & Planning
Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organizations
International Journal of Production Economics
Dynamic capabilities and organizational performance: A meta-analytic evaluation and extension
Journal of Management Studies
Open innovation in multinational companies' subsidiaries: The role of internal and external knowledge
European Journal of International Management
Evaluating structural equation models with unobservable variables and measurement error
Journal of Marketing Research
The predictive sample reuse method with applications
Journal of the American Statistical Association
Performance outcomes of behavioral attributes in buyer supplier relationships
Journal of Business and Industrial Marketing
The impact of CRM 2.0 on customer insight
Journal of Business & Industrial Marketing
How can machine learning aid behavioral marketing research?
Marketing Letters, Marketing Letters
Cited by (22)
Artificial intelligence capabilities, open innovation, and business performance – Empirical insights from multinational B2B companies
2024, Industrial Marketing ManagementIs humility in leadership a promoter of employee voice? A moderated mediation model
2024, European Management JournalArtificial intelligence - partner relationships management for climate management in B2B firms to achieve sustainable competitiveness
2023, Industrial Marketing ManagementHow does artificial intelligence impact human resources performance. evidence from a healthcare institution in the United Arab Emirates
2023, Journal of Innovation and KnowledgeAdoption of AI integrated partner relationship management (AI-PRM) in B2B sales channels: Exploratory study
2023, Industrial Marketing Management
Dr. Sheshadri Chatterjee is a post-doctoral research scholar at Indian Institute of Technology Kharagpur, India. He has completed PhD from Indian Institute of Technology Delhi, India. He is having work experience in different multinational organizations such as Microsoft Corporation, Hewlett Packard Company, IBM and so on. Sheshadri has published research articles in several reputed journals such as Government Information Quarterly, Information Technology & People, Journal of Digital Policy, Regulation and Governance and so on. Sheshadri is also a certified project management professional, PMP from Project Management Institute (PMI), USA and completed PRINCE2, OGC, UK and ITIL v3 UK. He can be contacted at: [email protected].
Dr Ranjan Chaudhuri is a Professor of Marketing at IIM Ranchi, Jharkhand, India. He was a Fulbright Fellow to USA in 2012. Dr. Chaudhuri also served as an Associate Professor at NITIE, Mumbai and Vinod Gupta School of Management, Indian Institute of Technology, Kharagpur and at the Department of Management Studies, Indian Institute of Technology Delhi. Dr Chaudhuri has over eighteen years of industrial, teaching and research experience. Dr Chaudhuri’s teaching and research interests are in the areas of Business-to-Business Marketing, Global Marketing and Retail Management.
Demetris Vrontis is a Professor and Executive Dean at the University of Nicosia in Cyprus. He is the Founding Editor and Editor in Chief of the EuroMed Journal of Business and the Associate Editor in Chief of the International Marketing Review. He is also the Founder and President of the EuroMed Research Business Institute (http://emrbi.org). Professor Vrontis has widely published, in over 200 refereed journal articles and 40 books in the areas of business management, marketing, human resource management, innovation and entrepreneurship. He is a fellow member and certified Chartered Marketer of the Chartered Institute of Marketing and a Chartered Business and Chartered Marketing Consultant. He also serves as a consultant and member of board of directors to a number of international companies. Further details about Professor Vrontis can be found at: http://unic.academia.edu/DemetrisVrontis