{"id":14472,"date":"2025-03-09T03:26:48","date_gmt":"2025-03-09T07:26:48","guid":{"rendered":"https:\/\/www.servsig.org\/wordpress\/?p=14472"},"modified":"2025-03-09T03:26:49","modified_gmt":"2025-03-09T07:26:49","slug":"cfp-imm-ai-enabled-service-recovery","status":"publish","type":"post","link":"https:\/\/www.servsig.org\/wordpress\/2025\/03\/cfp-imm-ai-enabled-service-recovery\/","title":{"rendered":"CfP IMM: AI-Enabled Service Recovery"},"content":{"rendered":"
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Special Issue in Industrial Marketing Management<\/em>.<\/p>\n\n\n\n

Engaging Industrial Customers with AI-Enabled Service Recovery<\/strong><\/p>\n\n\n\n

Guest Co-Editors: Lages C, Hollebeek L, Sands S & Boukis A<\/p>\n\n\n\n

Deadline: 1 March 2026<\/strong>
<\/p>\n\n\n\n

Industrial service failures represent \u201cany type of error, mistake, deficiency, or problem occurring during the provision of [an industrial] service\u201d (Koc, 2017, p. 1). Service failures should be promptly addressed through service recovery, \u201cthe organizational actions of seeking and dealing with a failure in service delivery\u201d (Van Vaerenbergh & Orsingher, 2016, p. 328).<\/p>\n\n\n\n

While service recovery has traditionally transpired through human efforts (e.g., employee\u2019s courtesy to customers; Mostafa et al., 2015; Silva et al., 2020), these efforts are increasingly being supplemented or replaced with artificial intelligence (AI)-enabled tools, including chatbots, service robots, or generative AI applications (Ameen et al., 2025; Kushwaha et al., 2021). These AI-enabled service recovery tools can \u201cinterpret external data, learn from such data, and use those learnings to [resolve industrial service failure] through flexible adaptation\u201d (Kaplan & Haenlein, 2019, p. 15). More importantly, AI-enabled industrial service recovery can offer unique benefits (vs. traditional service recovery) by providing more efficient, more accurate, more consistent, and\/or more timely service recovery solutions (Agnihotri & Bhattacharya, 2024). For example, industrial firms are increasingly adopting Salesforce\u2019s AI-enabled Einstein platform that draws on predictive analytics to prevent service failure (Salesforce, 2024), thus reflecting their proactive industrial service recovery effort, while IBM\u2019s Watson uses natural language processing (NLP) to detect service issues and their root causes (IBM, 2024).<\/p>\n\n\n\n

However, while acumen of AI-enabled service recovery is emerging in the literature, prior studies have predominantly focused on business-to-consumer (e.g., Agnihotri & Bhattacharya, 2024) rather than business-to-business or industrial recovery contexts (e.g., Baliga et al., 2021; Sands et al., 2022), revealing an important gap in the literature. Moreover, scarce research exists on how emerging AI-enabled industrial service recovery applications may amend, complement, or revolutionize traditional service recovery efforts (Agnihotri & Bhattacharya, 2024; Dong et al., 2016), affecting the nature and role of customers\u2019 service recovery engagement (Van Vaerenbergh et al., 2018; Ameen et al., 2025). Specifically, while industrial firms are investing extensively in AI-enabled service recovery (Baliga et al., 2021), the effectiveness, management, and performance of these tools remain nebulous, requiring enhanced insight into and accountability of these investments. For example, though research suggests the promising role of the AI-enabled service recovery journey (Ameen et al., 2025) or its capacity to boost customer engagement, important caveats also surround industrial firms\u2019 AI adoption for service recovery purposes (Pantano et al., 2024; Keegan et al., 2023).<\/p>\n\n\n\n

In light of these challenges for industrial firms, this Special Issue explores the role of AI-enabled service recovery in engaging industrial customers. It seeks to integrate the to date disparate literature streams on AI-enabled service recovery (e.g., Ameen et al., 2025) and industrial customers\u2019 engagement (e.g., Ferdous et al., 2024; Hollebeek, 2019). To this end, we solicit state-of-the-art submissions that explore the role of AI-enabled service recovery in influencing industrial customers\u2019 cognitive, emotional, and behavioral engagement with the firm\u2019s recovery efforts. Submissions should offer a significant original contribution to the industrial marketing, AI-enabled service recovery, and\/or customer engagement bodies of literature.<\/p>\n\n\n\n

Sample Topics<\/strong><\/p>\n\n\n\n

Given the still emerging state of the AI-enabled service recovery domain and the need for more theoretically robust and methodologically diverse approaches, this Special Issue invites both conceptual and empirical contributions that have the potential to advance research in these areas. We welcome conceptual, qualitative, quantitative, or mixed-method as well as methodological contributions that shed light on issues including, but not limited to, the following:
– What is the differential impact of AI-enabled (vs. human) service recovery on industrial customers\u2019 engagement?
– How does adopting a hybrid (AI- and human-based) service recovery journey impact customer engagement?
– How do different AI tools (e.g., chatbots) affect cognitive, emotional, and behavioral engagement outcomes in industrial service recovery journeys?
– What key factors may facilitate (vs. inhibit) the effect of AI-enabled service recovery on industrial customers\u2019 positive engagement with the recovery effort, the brand, the firm, and\/or other stakeholders of the firm while minimizing their negative engagement?
– How may reactive (vs. proactive) AI-enabled service recovery impact industrial customers\u2019 and other stakeholders\u2019 engagement with the recovery effort, the brand, and the firm?
– How does the reliance on AI-enabled service recovery impact industrial employees\u2019 engagement, skill development, motivation, and retention?
– How can predictive analytics in AI-enabled service platforms mitigate potential service failures before they occur or optimize customers\u2019 engagement with industrial firms\u2019 proactive (vs. reactive) service recovery efforts?
– How do specific AI applications (e.g., machine learning, deep learning, NLP; predictive vs. generative AI; mechanical, thinking, or feeling AI; Huang & Rust, 2021) impact B2B service recovery journeys?
– What AI features (e.g., speed, accuracy, tone of voice, or response personalization) drive the effectiveness of AI-enabled service recovery journeys in addressing industrial service failures?
– What metrics should industrial firms use to assess the performance of AI-enabled service recovery tactics and tools (e.g., to measure the return on their investment in AI-enabled service recovery)?
– How do industrial firms ensure AI\u2019s ethical and sustainable use in service recovery while maintaining or enhancing their customers\u2019 engagement?
– What mechanisms and strategies can be implemented to ensure transparency and accountability of AI-enabled service recovery in industrial firms, and how might these affect customer engagement?<\/p>\n\n\n\n

Preparation and submission of paper and review process <\/strong><\/p>\n\n\n\n

Papers submitted must not have been published, accepted for publication, or presently be under consideration for publication elsewhere. Submissions should be about 6,000-8,000 words in length. Copies should be uploaded on Industrial Marketing Management\u2019s<\/em> submission system<\/a> by using the dropdown box for the special issue on VSI: AI-enabled Service Recovery<\/strong>. All papers will be handled according to the guidelines (Kadic-Maglajlic et al., 2023) for guest editing of special issues of Industrial Marketing Management<\/em>.<\/p>\n\n\n\n

Authors are advised to refer to the\u00a0Guide for Authors<\/a>\u00a0prior to submission. Papers that do not comply with the Guide for Authors or are poorly written will be desk rejected. Manuscripts within the scope of the special issue (as described above) and for which there is a reasonable chance of conditional acceptance after no more than two rounds of revisions will enter the double-blind review process.<\/p>\n\n\n\n

Important dates<\/strong><\/p>\n\n\n\n

Submission opens: January 1st, 2026
Deadline for submission: March 1st, 2026<\/strong><\/p>\n\n\n\n

Guest editors<\/strong><\/p>\n\n\n\n

Cristiana R. Lages<\/a>, Ph.D. (University of Minho, Portugal)
Linda D. Hollebeek<\/a>, Ph.D. (Sunway University, Vilnius University, Tallinn University of Technology, Umea University, University of Johannesburg)
Sean Sands<\/a>, Ph.D. (Swinburne University of Technology, Australia)
Achilleas Boukis<\/a>, Ph.D. (University of Birmingham, UK)<\/p>\n\n\n\n

More info:<\/p>\n\n\n\n