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Journal of Business Research 156 (2023) 113525 Contents lists available at ScienceDirect Journal of Business R...

Journal of Business Research 156 (2023) 113525 Contents lists available at ScienceDirect Journal of Business Research journal homepage: www.elsevier.com/locate/jbusres Technology readiness of B2B firms and AI-based customer relationship management capability for enhancing social sustainability performance Muhammad Sabbir Rahman a, Surajit Bag b, Shivam Gupta c, Uthayasankar Sivarajah d, * a School of Business and Economics, Department of Marketing and International Business, North South University, Bashundhara, Dhaka 1229, Bangladesh b Institute of Management Technology, Ghaziabad, Raj Nagar, Ghaziabad, Uttar Pradesh 201001, India c Department of Information Systems, Supply Chain Management & Decision Support, NEOMA Business School, 59 Rue Pierre Taittinger, 51100 Reims, France d School of Management, University of Bradford, Richmond Road, Bradford BD7 1DP, United Kingdom A R T I C L E I N F O A B S T R A C T Keywords: This study contributes to the extant literature by empirically investigating the influence of Business-to-Business B2B (B2B) firms’ technology readiness on information technology capability and artificial intelligence-based Artificial Intelligence customer relationship management (AI-CRM) and finally, on relationship performance and social sustainabil­ Firm Technology Readiness ity performance. We leverage primary data from 217 samples and examine the firm’s social sustainability per­ Digitalization CRM-Capability formance. Drawing on the paradigm of dynamic capability view, we found that a B2B firm’s technology readiness Relationship Performance has a positive relationship with information and communication technology and AI-CRM capability. Information and communication technology capability also has a positive and significant relationship with AI-CRM capa­ bility. B2B firms’ relationship performance has a significant and positive relationship with social sustainability performance. A key finding of this study is that a B2B firm’s information and communication technology capability mediates between technology readiness and AI-CRM capability. Additionally, industry dynamism also moderates the link between information and communication technology capability and AI-CRM capability. 1. Introduction et al., 1997). According to Lee (2004), firms must adapt to technological changes or they will not survive in long run. Therefore, industrial firms In this fourth industrial revolution era, data is the key to achieving must check their technological readiness for fourth industrial revolution success, and these data are generally large data sets mostly gathered in technologies (Samaranayake et al., 2017). Technology readiness re­ unstructured form (Dubey et al., 2020). Businesses have made signifi­ quires the input of several key resources to further develop information cant progress in the last decade with the introduction of the fourth in­ and communication technology capability (ICT) (Gupta & George, dustrial revolution (I4.0) (Telukdarie et al., 2018). I4.0 technologies 2016). improve information sharing and supply chain visibility (Gunasekaran Moreover, in this fast-changing world, customer preferences and et al., 2017). Advanced information and communication technologies tastes evolve rapidly, which creates huge difficulties for firms. Changing have taken business-to-business (B2B) firms to the next level with customer behavior leads to quickly outdated products and services, embedded big data analytics and artificial intelligence (AI) capability which requires changing the operating process and introducing inno­ (Gupta et al., 2020; Bag et al., 2021a; Bag et al., 2021b; Bag and Rah­ vative products and services to satisfy customers (Dubey et al., 2020). man, 2021; Chung et al., 2021). The exploration of Chatterjee et al. Therefore, firms need to build technological capability to develop AI- (2021d) demonstrates that artificial intelligence-supported customer CRM capability for adapting to this changing business environment. relationship management (AI-CRM) systems can be of paramount sig­ Meanwhile, customer relationship management (CRM) integrates nificance for B2B firms in this fourth industrial revolution era to remain and examines client data produced from the formal and casual re­ sustainable in their respective industry. Implementation of AI-CRM also lationships among partners in the network, including service providers requires a combination of various resources and firm capabilities, and and clients (Zablah et al., 2004; Bag et al., 2021c). Firms’ AI-driven this can be achieved through collaboration and cooperation (Teece customer relationship management (CRM) capability is a vital aspect * Corresponding author. E-mail addresses: [email protected] (M.S. Rahman), [email protected] (S. Bag), [email protected] (S. Gupta), u.sivarajah@bradford. ac.uk (U. Sivarajah). https://doi.org/10.1016/j.jbusres.2022.113525 Received 21 December 2021; Received in revised form 27 November 2022; Accepted 29 November 2022 Available online 8 December 2022 0148-2963/© 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). M.S. Rahman et al. Journal of Business Research 156 (2023) 113525 of competitiveness that enables firms to understand their customers’ businesses include industrial buying and selling. It is very important that changing preferences and optimize their relationship performance every B2B firm understands its industrial clients’ needs and identifies (Herman et al., 2021; Saura et al., 2021) due to the successful effects of any uncertainty in order to develop technology readiness to build AI- adopting the traditional approach of CRM in the B2B context (Fotiadis & CRM capability that in turn could enhance relationship performance Vassiliadis, 2017). In recent times, firms have shown considerable in­ and finally social sustainability performance. terest in applying AI-CRM tools with built-in predictive analytics and Based on the empirical analysis of primary data collected from 217 machine learning, such as account-based marketing (ABM) platforms managers of different B2B firms in South Africa, the present study like Demandbase, Terminus, HubSpot, Salesforce Einstein, Hootsuite, contributes to the existing customer relationship management and social etc. to enhance their relationship performance (Dooley, 2020; Dixit, sustainability literature in the B2B context. The study identifies the 2022). Indeed, the outcome of relationship performance results in sus­ important mediating mechanisms (firms’ information communication tainable social performance (Edwin Cheng et al., 2021; Vesal et al., technology capability) that optimize firms’ dynamic capabilities, such as 2021). Stakeholders will notice a company’s sustainable social perfor­ AI-driven customer relationship management, through which relation­ mance once they understand how it releases its production processes, ship performance drives socially sustainable performance. This study manufactures safe and environmentally friendly products, uses re­ also provides the moderating effect of industry dynamism in order to sources, and takes sustainable steps for the betterment of society (Zadek, understand the effect of ICT capability on AI-driven customer relation­ 2004; Tate et al., 2010; Mani et al., 2018). ship management. The hypotheses are drawn based on dynamic capa­ Additionally, implementing intelligent digital systems is an essential bility view and presented in Section 2. This study uses data from B2B driver of the B2B manufacturers and services enterprise. This environ­ industrial firms in South Africa. The methodology is presented in Section ment of digitalization has given a new shape to the Internet of Things 3 and then the proposed hypotheses are tested using covariance-based (IoT) that allows firms and their respective communities to understand structural equation modeling in Section 4. The discussion is provided and perform effectively real-world activities (Mora Cortez & Johnston, in Section 5. Limitations and future research is presented in Section 6. 2017). For example: by using data streaming on the Internet, B2B firms Conclusions are drawn from the empirical study in the final section. can improve the control of information flows and develop accurate interpretation, manage waste, monitoring cost, loss, and profit (John­ 2. Theoretical underpinnings ston, 2014). In the current literature gaps in the scholastic works still exist about the digitalization of B2B business firms. There is a lack of 2.1. Dynamic capability view (DCV) empirical evidence related to the effect of technology readiness on ICT capability and AI-CRM to understand the relationship performance for The resource-based view (RBV) (Barney, 2001) posits that a firm’s achieving social sustainability performance (Mora Cortez & Johnston, ability to control resources and competencies that are valued, uncom­ 2017; Foltean et al., 2019; Möller et al., 2020; Ledro et al., 2022). Thus, mon, imperfectly imitable, and non-substitutable gives it a persistent there is a lack of research in the B2B context to understand and validate competitive edge. Dynamic capability view (DCV) is an offshoot of RBV. the impact of B2B firms’ capability in terms of technology readiness, DCV is a popular theory and widely used in management research to ICT, and AI-CRM on relationship performance to achieve social sus­ explain capability building in this dynamic business environment tainable performance and this study is unique in integrating these di­ (Wilson & Daniel, 2007; Wang et al., 2007; Ahmad Zaidi & Othman, mensions into the framework of the existing social sustainable 2014; Bag & Rahman, 2021). Dynamic capabilities (DC) emerged from performance literature. Therefore, it is very important that firms the school of strategic management literature and it analyzes the develop AI-CRM capabilities to effectively engage customers in this competitive advantage of firms working in environments where tech­ fourth industrial revolution era. The most interesting part of the AI-CRM nology evolves rapidly (Teece et al., 1997; Teece, 2007; Chowdhury & application is that it can be effectively used to develop CRM strategies Quaddus, 2017). Firms avail themselves of dynamic capabilities by for different segments (Chatterjee et al., 2021d). Although the studies by continuously building, adapting, and reconfiguring internal and Chatterjee et al. (2021d), Singh and Santos (2022), Itani et al. (2022), external competencies by applying advanced technology to connect with and Peruchi et al. (2022) contribute significantly to the AI-CRM litera­ their customers and fostering relationship performance to achieve sus­ ture, they do not cover social sustainability performance. In this digital tainable performance (Teece et al., 1997; Zhu & Kraemer, 2002). Dy­ age, every firm is trying to adapt to technological changes and namic capabilities help firms to develop sensing, seizing, and strengthen its CRM capability by investing in advanced ICT technologies reconfiguring capacities to adapt them without difficulty to the chang­ for greater visibility in the market (Vesal et al., 2021). AI-CRM systems ing business environments (Teece, 2007). DCV suggests that the business generate information that gives customers a perception of buyer firms’ environment is dynamic. Firms, therefore, acquire and deploy resources social sustainability. This information can be immensely helpful in to respond to market variance over time (Eisenhardt & Martin 2000; creating social sustainability programs that can enhance the social sus­ Makadok 2001). Capabilities are also dynamic, as they can assist firms to tainability of partner organizations in the supply chain network (Sroufe implement competitive strategies by considering the fact of changing & Gopalakrishna-Remani, 2019). However, this area is under- market conditions by combining and transforming available resources in researched and requires further examination. a novel and alternative way (Morgan et al., 2009; Eriksson, 2014). The importance of data-driven CRM systems is on the rise. Although the literature has indicated the antecedents for AI-CRM, studies exam­ 2.2. Model building ining the capabilities required for building AI-CRM capability in theory- driven, large-scale, quantitative, and empirical studies are relatively Prior studies have suggested that firms need to spend substantial scarce, and to fill the gap we aim to answer the questions below: resources on information communication technology to support AI-CRM systems to achieve marketing capabilities by deploying CRM tools to RQ1. What are the effects of B2B firms’ technology readiness, ICT improve relationship performance (Wang & Kim, 2017). Thus, building capability, and AI-CRM capability on social sustainability on this logic, we argue that the technology readiness of B2B firms will performance? help build AI-CRM capability while ICT capability plays a mediating RQ2. What is the effect of industry dynamism on the path of joining role. For this, technology readiness and ICT are lower-order capabilities ICT capability and AI-CRM capability? that lead to the development of a higher-order capability i.e., AI-CRM capability. Since firms operate in a highly volatile environment, we The current study is important as it examines a very important area have used industry dynamism as a contextual factor and operationalized (i.e., CRM) in the domain of B2B marketing management. B2B it as a moderation variable between ICT and AI-CRM capability. 2 M.S. Rahman et al. Journal of Business Research 156 (2023) 113525 Secondly, AI-CRM helps B2B firms sense opportunities and threats seize H1: Technology readiness of B2B firms has a positive relationship new prospects related to the market, and further address customers with ICT capability. through newer products/services, finally helping them to reconfigure their business to adapt to technological changes (Morgan & Slotegraaf, 2.3.2. Technology readiness of B2B firms and AI-CRM capability 2012; O’cass & Ngo, 2012; Wali et al., 2016). AI-CRM helps improve The technology readiness of firms is important to embrace emerging relationship performance and enhances social sustainability perfor­ Industry 4.0 (I4.0) technologies (Telukdarie et al., 2018). Chatterjee mance (Prior & Keränen, 2020; Ronaghi & Mosakhani, 2022). The et al. (2019) mentioned that data and AI are two key requirements for theoretical model is presented in Fig. 1. The theoretical model is enabling digital CRM actions in the firm. Examples of such smart sys­ grounded in DCV since AI-CRM is conceptualized as a DC since it is tems include “dynamics 365 for customer insights”. This system is rooted in changing routines, for instance, showing creativity in tech­ capable of performing predictive analytics on customer data sets. Some nology readiness and working toward developing ICT capability. In more examples of companies offering AI-CRM tools as cited by Chat­ these dynamic business situations, success will depend on how quickly terjee et al. (2019) are Zoho, SugarCRM, and Salesforce. Chatterjee et al. and effectively a firm can realign its unique resources and competencies (2019) further mentioned that firms face infrastructure-related chal­ to take advantage of opportunities and meet market demands. lenges while adopting AI-CRM tools and these challenges can be over­ come with a readiness strategy and effective training. Chatterjee et al. 2.3. Hypotheses development (2021b) also claimed that “design and development of the AI-CRM-KM tool”, “support of the immediate manager”, “adequate training and 2.3.1. Technology readiness and information and communication readiness”, “business value addition”, “adequate security mechanism”, technology capability (ICT) “developing a privacy policy for the AI-CRM-KM”, “simplicity of the new There is an extensive literature gap between developed and devel­ AI-CRM-KM system”, “supporting legal requirements” and “ease of use” oping countries in terms of B2B firms’ technology readiness (Saif et al., are nine key factors that are essential for the adoption of an AI- 2022). Lee (2001) suggested that more focus is required on building ICT integrated CRM-KM system. Therefore, from the evidence of the capability since ICT capability gears firms to adapt to changing business above-mentioned literature, we can see that adequate training and environments and gain a competitive advantage (Napitupulu et al., readiness influence an important role in AI-CRM. In line with that, we 2018). ICT capability involves developing abilities to create and use new argue that the technology readiness of organizations is important for applications. This requires complete knowledge of the business, product, building AI-CRM capability. Therefore, we hypothesize: and services related to the firm and its partner firms’ processes to develop ICT infrastructure to run their new applications (Vize et al., H2: Technology readiness of B2B firms has a positive relationship 2013). Regular training and continuing education on digital technolo­ with AI-CRM capability. gies provide the desired results in terms of productivity improvement. B2B firms need to invest money in advanced ICT training and in building 2.3.3. Information and communication technology capability and AI-CRM the technological infrastructure for the effective application of new capability technologies like AI-CRM (Chatterjee et al., 2020; Chatterjee et al., ICT capability requires integration with several (tangible and 2021d). Hence, resources must be well facilitated and decision-makers intangible) resources to build a competitive advantage (Akter et al., must carefully monitor this, learn the natural elements and reconfig­ 2020). In addition, firms’ learning is a very important dimension that ure and change their assets in the right manner to develop competencies includes information gathering, securing information, information (Sunday & Vera, 2018). Therefore, we hypothesize: diffusion, and data-based decision-making to build new competencies. Furthermore, firms should reconfigure and further transform resources to reap the full benefits in turbulent times (Sunday & Vera, 2018). This is ICT Industry Capability H7 Dynamism H1 H3 H6 Social Technology AI-CRM Sustainability Relationship Readiness Capability Performance H2 H4 Performance H5 Control Variables (firm size, firm experience, industry type) Fig. 1. Theoretical framework (Source: Authors’ conceptualization using DCV). 3 M.S. Rahman et al. Journal of Business Research 156 (2023) 113525 important for building ICT, which in turn will lead to the development of relationship performance and social sustainability performance lacks AI-CRM capability (Chatterjee et al., 2021d). Therefore, IT facilities are empirical investigation. Hence, we hypothesize: important for smooth operations and services. Moreover, the establish­ ment of an operative and flexible IT planning process along with in­ H5: B2B firms’ relationship performance has a positive relationship dustrial partners is necessary for building AI-CRM capability. In with social sustainability performance. addition, it is vital that B2B firms create a climate that is supportive of their industrial partners to try out new and better ways of using ICT 2.3.6. Mediation effect of information and communication technology (Bharadwaj, 2000; Weill & Vitale, 2002; Stoel & Muhanna, 2009; Lu & According to Parasuraman (2000), technology readiness is the will­ Ramamurthy, 2011). Hence, we hypothesize: ingness of humans to adapt and further apply new technologies. The underlying mechanism involves some enablers and inhibitors that shape H3: ICT capability has a positive relationship with AI-CRM the human mind to make the decision to use the latest technology. capability. Therefore, a positive perception of the technology brings comfort whereas a negative perception will lead to discomfort and unwillingness 2.3.4. AI-CRM capability and B2B firms’ relationship performance to use the technology (Parasuraman, 2000). Positive thinking and nov­ The availability of data in this I4.0 era is powering AI-based appli­ elty are important drivers of technology readiness (Lin et al., 2007). cations like CRM and providing low-cost tailor-made services for each Zeithaml et al. (2002) argued that technology readiness is positively customer segment (Chatterjee et al. (2021d)). Bag et al. (2021a) claimed related to the tendency to engage with new technology. Chatterjee et al. that big data analytic-powered artificial intelligence systems aid firms in (2021d) claimed that AI-CRM systems are very effective in managing the the creation of customer knowledge, user knowledge, and external relationships with customers in the network. First, AI-CRM creates a market knowledge. Therefore, AI-CRM systems can be useful in identi­ positive perception among employees and users, second, it triggers fying important customers, and predicting and making investments in innovativeness, third, it gives comfort when using this type of system for long-term relationships. This will help to enhance the relationship per­ better information management, and lastly, it offers better security, formance as the B2B partners (supplier firm and buyer firm) will want to which enables technology readiness as a whole. Technology readiness is remain associated and interact regularly to explore potential business very important for adapting to technological changes in the fourth in­ opportunities. dustrial revolution and we must not forget that technology readiness Chatterjee et al. (2021c) suggested that CRM quality and satisfaction leads to ICT capability building, which will in turn lead to the devel­ have a considerable impact on employees’ views and inclinations to opment of AI-CRM capability. However, the mediating role of infor­ embrace AI-enabled CRM solutions. According to Wernerfelt (1984), the mation and communication technology capability, which increases the resource-based view (RBV) states that when firms have resources that technology readiness of B2B firms to achieve AI-CRM capability has not are not easily available, valuable, and not easy to duplicate, this leads to been thoroughly studied. Hence, we hypothesize: a greater competitive advantage and better sustainable performance. Jap (1999) further stretches the RBV framework to explain inter-firm H6: Technology readiness of B2B firms under the mediation effect of relationships. Higher performance is achieved when the partner firms ICT capability is positively related to AI-CRM capability. invest time, resources, and knowledge to build capabilities in the supply chain (Dyer & Singh, 1998). Firms’ relationship-specific resources, 2.3.7. Moderating effect of industry dynamism knowledge-sharing practices, complementary assets and capabilities, When an industry is highly dynamic, there is a fear of losing profits and effective governance practices are the foundations of inter-firm and market share, unless firms adapt to the changes and match existing competitive advantage (Dyer & Singh, 1998). Zhang et al. (2020) resources with capabilities to explore various growth prospects (Larra­ claimed that big data analytical intelligence has a positive relationship ñeta et al., 2014; Chung et al., 2021). Farjoun & Levin (2011) stated that with mass customization capability and enhances CRM performance. In researchers use industry dynamism to capture the degree, recurrence, line with the paradigm of DCV, we argue that AI-CRM capability is a and capriciousness of changes in the market. Strategic management source of B2B inter-firm competitive advantage, as it will provide literature indicates that complexity, munificence, and dynamism are the valuable information for making key B2B business decisions. However, main factors that influence firms (Dess & Beard, 1984). Firms face the association between AI-CRM capability and B2B firms is under- challenges because of fluctuations in the availability of resources that researched, and hence, we hypothesize: are important to sustain operations, which is a question of industry dynamism. Therefore, industry dynamism is associated with the rate of H4: AI-CRM capability has a positive relationship with B2B firms’ variation and level of volatility and turmoil in the business surroundings relationship performance. (Farjoun & Levin, 2011). In order to survive in such a changing envi­ ronment, modern firms develop dynamic capabilities (Teece, 2007). 2.3.5. B2B firms’ relationship performance and social sustainability Previous studies have already operationalized industry dynamism as a performance moderating variable (e.g., Ruigrok et al., 2013; Larrañeta et al., 2014; Social sustainability is becoming extremely important in this digital Bag et al., 2021b). However, the moderating effect of industry dyna­ era (Bai et al., 2020). Social sustainability problems occur when firms do mism on the relationship between ICT capability and AI-CRM capability not treat their employees and partners fairly, allow improper labor has not been the focus of previous researchers. Hence, we hypothesize: conditions in factories, tolerate a lack of health and safety matters in the workplace, and lack corporate social responsibility, diversity, and H7: Industry dynamism moderates the relationship between ICT product responsibility practices (Mani et al., 2018). The literature in­ capability and AI-CRM capability. dicates that investment in relationships leads to better social sustain­ ability performance (Awan et al., 2018) or improvement in a sustainable 3. Research methods society (Bai et al., 2020). Strong business relationships among B2B firms will result in the alignment of interests and thus the alignment of sus­ B2B firms in South Africa are growing fast (Bughin et al., 2016; Ocloo tainable development goals. This will be demonstrated by an increased et al., 2020). A UNCTAD (2018) report revealed that the African region focus on societal responsibility (Mani et al., 2018; Kapitan et al., 2019) is showing quality progress in multiple vital indicators related to B2B- such as decent working conditions and economic growth, lower in­ relevant business. The adoption of new technology by firms has pre­ equalities, and the promotion of justice, peace, and inclusive societies dominantly improved B2B firms in countries like South Africa (Evans, (Bai et al., 2020). However, the association between B2B firms’ 2019). Bughin et al (2016) report that in South Africa, consumers are 4 M.S. Rahman et al. Journal of Business Research 156 (2023) 113525 considered above the middle class according to Organization for Eco­ Table 1 nomic Co-operation and Development (OECD) standards and collec­ Demographic particulars (n = 217). tively spend an additional $174 billion per year on housing, consumer Demographic details Description Numbers Percentage goods, education, and transportation services by 2025. While South Age Group 20–30 12 5.53 % African consumer data has resulted in significant business strategy 31–40 54 24.88 % moves for many firms, the relatively fast-growing business-to-business 41–50 118 54.38 % (B2B) sector is one of the most significant spenders in this region. For 51–60 25 11.52 % instance, firms in this African region spent almost $2.6 trillion in 2015, Above 60 8 3.69 % Educational Postgraduate 117 53.92 % with 40 % in Nigeria and South Africa, while B2B spending is expected Qualifications Graduate 92 42.40 % to rise to $3.5 trillion by 2025, 50 % of which will be spent on materials, Diploma 8 3.69 % 16 % on capital goods and the rest on the services sector (finance, Designation CEO/President/Owner/ 9 4.15 % transportation, and telecommunications) (Molla & Licker, 2004; Gono Managing Director CIO/Technology Director 8 3.69 % et al., 2016; Rodrik, 2018; Boso et al., 2019; Oguji & Owusu, 2021). Fifty Senior VP/VP 4 1.84 % percent of large firms in Africa are based in South Africa. This study was Head of Business Unit or 22 10.14 % therefore conducted in various provinces in South Africa, where re­ Department searchers collected data from employees working in the manufacturing Senior Manager 112 51.61 % sector involving B2B business practices in that region. Manager 34 15.67 % Junior Manager 2 0.92 % Data Analyst 10 4.61 % 3.1. Sampling and data collection process Data Scientist 14 6.45 % Consultant 2 0.92 % This study used a survey questionnaire to gather data from the em­ No. of Employees in 50–300 25 11.52 % your Organization 301–500 46 21.20 % ployees of B2B industrial goods manufacturers. The sampling frame­ 501–1000 105 48.39 % work included various industry participants to enrich the More than 1000 41 18.89 % generalizability of the findings. The data collection of this research Age of the Organization Above 20 159 73.27 % consisted of various phases. The first phase applied the scientific process (Years) 10 to 20 58 26.73 % to select the questionnaire items, and in the next phase, the survey Less than 10 0 0.00 % Nature of Business Agriculture and allied 3 1.38 % questionnaire was operationalized for its final use in the field. Activities industrial products The first phase was covered between December 2020 and January Automobiles and allied 63 29.03 % 2021. This phase was divided into two parts. In the first part of this manufacturers phase, the researchers asked five senior academicians and also five se­ Information technology 22 10.14 % Biotechnology product 9 4.15 % nior managers of B2B firms to express their views and suggest required manufacturers adjustments regarding the measurement items related to each construct Industrial chemical 23 10.59 % [B2B firms’ technology readiness (B2BTR), information and communication manufacturers technology capability (ICT), industry dynamism (IND), relationship perfor­ Cement manufacturers 2 0.92 % mance in B2B firms (RP), artificial intelligence-based customer relationship Industrial electronics 29 13.36 % product manufacturers management (AICRM), social sustainability performance (SSP)] selected Industrial fabric 32 14.75 % from the previous literature. Based on the feedback given by the experts, manufacturers the researchers adjusted the wordings in the instrument. Metals and mining 5 2.30 % Furthermore, in the second stage of instrument validation, we con­ Steel mill 29 13.36 % Firm Size Small 9 4.15 % ducted a pilot study among 45 employees from different firms operating Medium 62 28.57 % in business markets to examine the internal consistency and validity of Large 146 67.28 % the measurement items. The results reflect satisfactory Cronbach’s alpha (α) values for all the study constructs ranging between 0.798 and 0.863. This confirms that all the items are valid for measuring the latent con­ communication technology capability (ICT) (5 items) adapted from the structs in the proposed questionnaire. Therefore, the researchers iden­ research of Bharadwaj (2000), Ross et al. (1996), and Lu & Ramamurthy tified no additional concerns from the results of the pilot study. (2011). To measure industry dynamism (IND), the study used four items Hence, the questionnaire was operationalized for the primary survey adapted from Dubey et al. (2020). Relationship performance in B2B to collect data from B2B firms (Appendix A1). The researchers chose 650 firms (RP) consisted of five items adapted from the research of Sir­ B2B firms from the Ezee-Dex database of South African businesses using deshmukh et al. (2002), Kumar et al. (1995), Hewett & Bearden (2001), a simple random sampling technique. Once the researchers specified Cannon & Perreault (1999) and Lages et al. (2008). The construct of potential respondents, they were contacted online and the study’s ob­ artificial intelligence-based customer relationship management jectives were clearly explained to them. The researchers issued re­ (AICRM) was measured using nine items adapted from the research of minders via follow-up emails and requests from peers as suggested in the Chatterjee et al. (2021d). Finally, social sustainability performance literature to increase the response rate (So et al., 2019). The data (SSP) consisted of seven items adapted from the research of Mani et al. collection started in February 2021 and was completed by August 2021. (2018) and Kapitan et al. (2019). The study applied a 7-point Likert scale At the end of August 2021, the researchers received only 217 completed for all questions where “7′′ reflected “strongly agree” and “1” reflected responses, denoting a 33.38 % response rate from the targeted firms. The “strongly disagree”. This scale was used because it is essential to demographic profile of the employees of B2B firms who contributed to consider the number of points to be operationalized, making sure that this survey is shown in Table 1. the same number of points is applied to all measurement items to suc­ cessfully analyze the data via structural equation modeling (SEM) 3.2. Procedure & measurement (Nanna & Sawilowsky, 1998; Hair et al., 2010). The previous study mentioned that using a seven-point rather than a five-point (or less) A Google form-based questionnaire was prepared with a total of 36 scale leads to more accurate responses that are easier for the researchers items distributed under each relevant construct. As discussed in the to use with a good reproduction of a respondent’s actual assessment of conceptual framework, B2B firms’ technology readiness (B2BTR) (6 the study variables, improving respondent stimuli and resulting in lesser items) adapted from Vize et al. (2013) and information and measurement errors (Wakita et al., 2012; Joshi et al., 2015; Awang et al., 5 M.S. Rahman et al. Journal of Business Research 156 (2023) 113525 2016). This is also in line with several existing B2B studies, for instance In addition, numerous authors such as Antonakis et al. (2014) and by Mauldin et al. (2006), Schultz et al., (2012), Gligor et al. (2021), and Guide & Ketokivi (2015) also agree that CMB could result in endoge­ Zhou et al. (2021), that have successfully utilized seven-point Likert neity issues. Therefore, this study applied the various tests discussed in scale. the previous section to assess CMB in our data set. We reached the conclusion that there was no CMB issue in our data set. As the study used 3.3. Control variables cross-sectional data, it reduced the chance of causality. Each item under the study variables was adapted from the literature (Damali et al., 2016). In the present research, we control three variables, i.e., firm size, Finally, the study also applied the strategy of considering control vari­ firm experience, and industry type, which have been used in the previ­ ables as recommended by Antonakis et al. (2014) to avoid potential ous studies and which may influence B2B firms’ social sustainability endogeneity problems. performance (Guo et al., 2018; Vesal et al., 2021). For instance, firm size is measured as the number of permanent staff, and the firm experience 3.5. Data analysis method was assessed by the total time period the business has operated since it first started its sales operation. In addition, an industry type was also The study performed descriptive statistics using SPSS 22.0 to identify considered as one of the control variables since we selected B2B firms missing data and relevant outliers and to check normality. Descriptive involved in different business activities. The outcomes show that no statistics were also performed to understand the frequency, percentage, control variables significantly influenced B2B firms’ social sustainability and relevant average values of the respondents’ demographic data. performance. Therefore, to assess the measurement model and examine the respective psychometric properties of the scale, the study applied confirmatory 3.4. Test of method bias and endogeneity factor analysis (CFA) by using AMOS 20. In this data analysis phase, the researchers examined the measurement model goodness-of-fit measures The study minimizes the common method bias effect in the data by via absolute fit measures, incremental fit measures, parsimonious fit collecting data from multiple respondents (e.g., Podsakoff et al., 2003; measures, and comparative fit index (Reuterberg & Gustafsson, 1992; Foss et al., 2013; Bag et al., 2022). This study adopted preventive Hurley et al., 1997; Hoyle, 2000; Gatignon, 2010). Then, the study measures by randomly distributing the items in the survey questions, applied structural equation modeling (SEM) to test whether the B2B which makes it difficult to easily understand the guided causal rela­ firms’ technology readiness (B2BTR), information and communication tionship, and by reassuring respondents of their anonymity so that they technology capability (ICT), artificial intelligence-based customer rela­ could answer the questions confidently without the fear of being iden­ tionship management (AICRM), and relationship performance in B2B tified. Contributors were told that no response to the survey questions is firms (RP) to predict social sustainability performance (SSP), through essentially correct or incorrect (Podsakoff et al., 2003). However, to which the nomological validity and proposed relationship were also further control for the common method bias effect, respondents were examined (Luo, 2002; Gallagher et al., 2008; Hair et al., 2014). informed about the confidentiality of their responses and that the results The researchers applied Hayes’ PROCESS macro-Model 4, recom­ would be used for research and academic publication purposes only mended by Hayes (2013) to test the mediating effect of information and (Slotegraaf & Atuahene-Gima, 2011). communication technology capability (ICT) in the relationship between In addition, to further validate the common method bias effect on the B2B firms’ technology readiness (B2BTR) and artificial intelligence- collected data set, this study also applied a statistical test through the based customer relationship management (AICRM). Finally, whether operationalization of the marker variable (MV) as recommended by this indirect path (i.e., B2BTR → ICT → AICRM) is conditional upon B2B Lindell & Whitney (2001) and Podsakoff et al. (2003). In this analysis, firms’ industry dynamism (IND) was tested through PROCESS Model 14 we adopted an MV of the industry type, which is considered to be as suggested by Hayes (2017) and Hayes & Rockwood (2017). conceptually not connected to at least one of the variables in the model (e.g., B2B firm’s technology readiness). The results of the data analysis 4. Data analysis suggest that there is no significant difference between the base model [χ2 (429) = 616.86 and the alternative model χ2 (412) = 590.16, chi-square 4.1. Measurement model validation difference: χ2 (26) = 21.26, n.s.). Therefore, we can confirm that the addition of this MV does not change the significance levels of the study We conducted confirmatory factor analysis (CFA) to assess the variables and CMB is unlikely to distort the hypothesized relationships measurement model along with psychometric competence among the in the proposed conceptual model. study constructs (i.e., B2B firms’ technology readiness-B2BTR, infor­ The study tested the potential threat of non-response bias by mation and communication technology capability-ICT, relationship comparing respondents and non-respondents in terms of firm size, performance in B2B firms-RP, B2B firms’ industry dynamism-IND, experience, and industry type. The results from the t-test revealed that artificial intelligence-based customer relationship management-AICRM no statistical difference existed between the demographic details of re­ and social sustainability performance-SSP). The results from the CFA spondents and non-respondents. In addition, the statistical results also analysis also show that all the fit measures (absolute fit, incremental fit, revealed that there was no significant variance between early (147) and parsimonious fit, and the comparative fit index) were found satisfactory late responses (70) across the B2B firms, proving that non-response bias (see Table 2). For instance, the study examined absolute fit measures by was not a concern in this work (Mentzer & Flint, 1997). examining the values of GFI (goodness-of-fit index), AGFI-adjusted GFI, The study also examined the potential endogeneity issue (Damali RMSEA (root mean square error of approximation), and RMSR (root et al., 2016). For instance, endogeneity may be a problem because of the mean square residual). The results of indicators such as GFI and AGFI reverse causality between the independent variables (IV) and the values are greater than 0.90. Hence, the values of RMSEA and RMSR are dependent variable (DV), which means that DV causes IV also satisfactory ( MSV, AVE > ASV, and all the AICRM is mediated by ICT capability. As presented in Table 6, the 95 % values of AVE of latent constructs are higher than the squared correla­ confidence interval of the indirect effect of B2BTR on AICRM through tions between the latent variable and all other variables. Above all, the ICT capability ranged from 0.053 to 0.319, with a coefficient of the results from the CFA analysis confirm that all the measures and the indirect effect of 0.186, SE = 0.029. H6 was therefore supported. measurement model are valid and reliable for further analysis. 7 M.S. Rahman et al. Journal of Business Research 156 (2023) 113525 Table 3 Assessment of the measurement model. CN Mean SD L α CR AVE MSV ASV B2BTR ICT RP IND AICRM SSP B2BTR 4.49 1.005 0.874 0.887 0.571 0.228 0.196 0.755 B2BTR1 0.701 B2BTR2 0.629 B2BTR3 0.852 B2BTR4 0.756 B2BTR5 0.734 B2BTR6 0.839 ICT 4.58 1.189 0.862 0.878 0.592 0.376 0.275 0.339 0.769 ICT1 0.696 ICT2 0.752 ICT3 0.827 ICT4 0.780 ICT5 0.786 IND 4.53 1.086 0.897 0.905 0.704 0.229 0.183 0.426 0.387 0.839 IND1 0.832 IND2 0.853 IND3 0.869 IND4 0.802 RP 4.59 1.043 0.862 0.879 0.594 0.287 0.193 0.403 0.406 0.429 0.770 RP1 0.708 RP2 0.847 RP3 0.725 RP4 0.804 RP5 0.762 AICRM 4.63 1.039 0.898 0.914 0.514 0.337 0.271 0.473 0.379 0.406 0.392 0.716 AICRM1 0.682 AICRM2 0.652 AICRM3 0.625 AICRM4 0.803 AICRM5 0.816 AICRM6 0.702 AICRM7 0.742 AICRM8 0.691 AICRM9 0.717 SSP 4.79 0.873 0.875 0.886 0.528 0.376 0.259 0.425 0.362 0.432 0.406 0.401 0.726 SSP1 0.651 SSP2 0.754 SSP3 0.717 SSP4 0.747 SSP5 0.662 SSP6 0.726 SSP7 0.818 Notes: CN: Construct’s Name, SD: Standard Deviation, L: Loadings, α: Cronbach’s Alpha, CR: Composite Reliability, AVE: Average Variance Extracted, MSV: Maximum Shared Variance, ASV: Average Squared Shared Variance, B2B firm’s technology readiness (B2BTR), information and communication technology capability (ICT), relationship performance in B2B firms (RP), B2B firm’s industry dynamism (IND), artificial intelligence-based customer relationship management (AICRM), social sustainability performance (SSP). ICT Capability β=0.309 2 β=0.273 R =0.282 β=0.389 β=0.469 Social Technology AI-CRM Relationship Sustainability Readiness Capability Performance Performance β=0.427 2 2 R =0.263 R =0.332 2 R =0.532 Fig. 2. Structural model (SEM output). 4.4. Test of moderated mediation examine the moderated IND meditation in the relationship between ICT capability and AICRM. The condition of moderated mediation is The study applied Model 14 of Hayes’ (2013) Process macro to attained when the conditional indirect effect of firms’ B2BTR on AICRM 8 M.S. Rahman et al. Journal of Business Research 156 (2023) 113525 Table 4 Table 7 Standardized regression weights. Moderated mediation test. Hypotheses Path Estimate P Constructs and Steps Outcome variable (AICRM) H1 B2BTR → ICT 0.309 *** H2 B2BTR → AICRM 0.427 *** Step one: Controls H3 ICT → AICRM 0.273 *** Firm Size 0.091 H4 AICRM → RP 0.389 *** Firm Experience 0.071 H5 RP → SSP 0.469 *** Industry Type 0.082 Step two: main effects of predictor variables B2B firm’s 0.297** Notes: *** = 0.01 or less. information and communication technology capability (ICT) Notes: B2B firm’s technology readiness (B2BTR), information and communica­ Step three: Effect of the moderator (industry dynamism -IND) 0.149* tion technology capability (ICT), relationship performance in B2B firms (RP), B2B firm’s information and communication technology artificial intelligence-based customer relationship management (AICRM), social capability (ICT) – Artificial intelligence-based customer sustainability performance (SSP). relationship management (AICRM) Step four: Interaction effect- ICT*IND 0.253** Values of moderator Conditional Bootstrap Lower Upper industry dynamism (IND) indirect effect SE CI CI Table 5 Test of mediation. Mediator 1: B2B firm’s information and Construct’s Relationship Estimate CR (t) SE communication (β) technology capability B2BTR → AICRM 0.427* 8.211 0.052 (ICT) Mediation relationship model 1: B2BTR → ICT − 1 SD 0.179 0.076 0.046 0.219 → AICRM 0.203* 5.205 0.039 M 0.227 0.094 0.147 0.290 B2BTR → AICRM** 0.397* 4.563 0.087 − 1 SD 0.276 0.107 0.178 0.362 B2BTR → ICT 0.254* 4.703 0.054 Note(s): *p < 0.05, **p < 0.01; Outcome variable: AICRM; Criterion Variable: ICT → AICRM ICT; Mediator: ICT; CI: Confidence. Note(s): *Significant at 0.05 level; B2B firm’s technology readiness (B2BTR), Interval; and SE: Standard Error. information and communication technology capability (ICT), Artificial intelligence-based customer relationship management (AICRM), CR: Critical Ratios; and SE: Standard Error; **indicates a direct effect of the independent variable (B2BTR) on the dependent variable (AICRM) in presence of mediating variable (ICT). Table 6 Estimate of the indirect effect. Construct’s Estimate (β t(SE) LP UP Sig Relationship indirect) B2BTR → ICT → 0.186 6.413 0.053 0.319 0.000 AICRM (0.029) Fig. 3. The moderating role of industry dynamism (IND) in B2B firms’ infor­ Note(s): t: t-value; SE: Standard Error (in parentheses); LP: Lower Percentile; UP: mation and communication technology capability (ICT) [note: Low ICT is (-1 Upper Percentile; and Sig: Significance. SD) and High ICT is (+1 SD)], - artificial intelligence-based customer rela­ tionship management (AICRM) relationship. through B2B firms’ ICT capability differs in terms of IND. The results shown in Table 7 proved that a significant interaction between the ICT 5. Discussion capability and IND in predicting the AICRM and firms’ B2BTR has different conditional indirect effects on the AICRM via ICT capability at This study empirically investigates the concept of B2B firms’ tech­ high and low levels of the moderator (i.e., IND) (Guarana & Hernandez, nology readiness, AI-based CRM capability for relationship perfor­ 2016). In line with that, the results from hierarchical regression (Aiken mance, and social sustainability by developing a theoretical framework. and West, 1991) show that the moderating effect of IND on the rela­ Inspired by the philosophy of dynamic capability view, the present study tionship between B2B firms’ ICT capability and AICRM is significant. has already addressed RQ1 and RQ2 by developing a theoretical This means that the interaction between ICT capability and IND was also framework to understand the technology readiness of B2B firms and AI- significant in predicting AICRM (βICT*IND = 0.253, p < 0.01), which based CRM capability for enhancing social sustainability performance demonstrates that a positive and significant relationship between ICT by developing direct effects, mediating effects, and conditional moder­ capability and AICRM can be more robust for those with strong B2B ating effects to test the hypotheses. The prime theoretical contribution of firms’ IND and thus supporting hypothesis H7 (Fig. 3). the present study, therefore, lies in the theoretical and empirical In addition, the results from moderation analyses based on Hayes’s explanation of the causal and mediating relationship between B2B firms’ (2013) PROCESS macro for SPSS also revealed that the indirect effect of technology readiness, the firm’s ICT capability, and the AI-based CRM of B2BTR on AICRM via B2B firms’ ICT capability is weakest at the lowest the firm based on conditional moderating effects of B2B firms’ industry level of IND and strongest at the highest level of IND (see Table 7 and dynamism. Fig. 3). The findings also indicate that B2BTR affects AICRM and is The results from the data analysis revealed that all seven hypotheses connected to B2B firms’ ICT capability, which states that the indirect formulated for this research were supported. The results indicate a sig­ effect of B2BTR on AICRM via B2B firms’ ICT capability is conditional on nificant positive relationship between B2B firms’ technology readiness IND. This proves hypothesis H7 (see Table 7 and Fig. 3). and the firm’s ICT capability and AI-based CRM. Hence, the study sup­ ports the first and second hypotheses and confirms the findings of the previous investigation in the context of B2B firms that suggested a sig­ nificant influence between technology readiness and ICT capability; and 9 M.S. Rahman et al. Journal of Business Research 156 (2023) 113525 technology readiness and AI-based CRM (Kogut & Zander, 1992; Saura customer management (Wang & Wang, 2020) and apply AI-CRM tools to et al., 2021; Baabdullah et al., 2021). This research also examines the improve relationships and social sustainability performance. In turn, the B2B relationship performance concept for examining the B2B business strategy will definitely improve the B2B firm’s local and global repu­ relationships, which in turn influence B2B firms’ social sustainability tation. Firms that will not adapt to technological changes will not performance. remain sustainable and will perish in the long run. This research study The results from the empirical evidence show a significant rela­ clearly sends a message to B2B firms that they should focus on three tionship between B2B relationship performance and socially sustainable important dimensions in this turbulent business environment; firstly, performance indicating that inter-firm relationships are vital and new developing AI-based CRM capability, secondly, investing in relation­ trends in the present business environment for achieving social sus­ ships, and thirdly, focusing on sustainability performance. The world tainable performance (Cox et al., 2004; Lefaix-Durand et al., 2005; will enter the fifth industrial revolution by 2050, where AI will be an Huang et al., 2022). Empirical findings also support the relationship integral part of every system. B2B firms must gear up now to adapt to between B2B firms’ ICT capability, AI-based CRM, and relationship this fourth industrial revolution and gradually shift to the fifth industrial performance, and finally, B2B firms’ relationship performance and so­ revolution over time. cial sustainability performance are significant and positive, and support the third, fourth, and fifth hypotheses. It also confirms the findings of the 5.1. Theoretical contributions previous investigation as well (Martínez-López and Casillas, 2013; Paschen et al., 2020; Han et al., 2021; Bag et al., 2021b). The results of the present study provide significant contributions to The role of technology readiness of B2B firms under the mediation the B2B literature. Existing studies have only focused on B2B firms’ ICT effect of ICT capability is also positively related to AI-CRM capability. as a singular entity (Liu et al., 2008; O’cass and Ngo, 2012; Guo et al., Empirical studies confirm the findings and emphasize the presence of 2018; Ritter & Pedersen, 2020). Considering the firm’s technology B2B firms’ ICT capability, which is an important construct to consider in readiness, ICT, and industry dynamism as exogenous antecedents to the relationship between the technology readiness of B2B firms and AI- analyze the AI-based CRM of B2B firms to enhance relationship per­ CRM capability (Obal & Lancioni, 2013; Lipiäinen, 2015; Nguyen et al., formance has strengthened the social sustainability performance model. 2022). The study results also emphasized the need to include industry The present research advances the understanding of ICT by applying AI- dynamism as a moderating variable in the relationship between B2B based CRM systems. This study also provides empirical evidence on the firms’ ICT capability and AI-based CRM capability, which is supported impacts of industry dynamism’s role on AI-based CRM systems. In this by previous studies such as those of Ruigrok et al. (2013), Larrañeta way, the present research provides an empirical reference for academics et al. (2014) and Bag et al. (2021). and management to understand the importance of AI-based CRM to In the field of technology orientation of firms and AI-based CRM successfully achieve social sustainability in the B2B context. capability for enhancing relationship performance for social sustain­ The study tests the model using dynamic capability theory and the ability, several scholars have already discussed B2B firms’ need to unique contribution of this study is the empirical investigation of the emphasize the importance of technology, industry dynamism, and AI- paths (Fig. 2). Previous studies have demonstrated that AI-based CRM based CRM capability to achieve strong relationships and social sus­ improves organizational performance (Zhang et al., 2020; Chatterjee tainability and the present study contributes significantly from the et al., 2021d; Olan et al., 2022). However, our study further extends the empirical findings (Vize et al., 2013; Kapitan et al., 2019; Blut & Wang, AI-based CRM literature by showing that dynamic capabilities also 2020; Chatterjee et al. 2021a, d). improve social sustainability performance. The goal of manufacturing B2B firms is to operate in a competitive On the other hand, this study also empirically demonstrates the environment by adapting to the consequence of operational uncertainty impact of B2B firms’ ICT on firms’ AI-based CRM while explaining the due to the Covid-19 pandemic and sustaining their business and underlying conditional moderated-mediation processes of industry enhancing their relationships with their respective partners in order to dynamism in connection with B2B firms’ technology readiness and ICT achieve social sustainability performance. However, the extant litera­ as technology capability, providing a new theoretical contribution. The ture has also failed to extensively discuss the significance and enablers of present research has indicated the moderating effect of industry dyna­ technology-driven orientation for B2B firms that enhance technology mism on the underlying mediating mechanism of B2B firms’ ICT be­ readiness by assessing the role of B2B firms’ ICT capability in the rela­ tween B2B firms’ technology readiness and AI-based CRM in a single tionship between B2B firms’ technology readiness and AI-based CRM. holistic model by extending social sustainability performance. The present research closes the literature gap, particularly in relation to Above all, the application of dynamic capabilities enables B2B firms AI-based CRM and relationship performance in B2B firms, by empiri­ to use AI-based CRM to leverage their existing information technology cally investigating a model that mixes several critical lower-order ca­ resources to capture new strategic relationship opportunities and remain pabilities of B2B firms to develop AI-CRM capability and subsequently competitive in the business environment by achieving social sustain­ attain social sustainability performance. This study clearly explains the ability performance (Teece et al., 1997; Desai et al., 2007; Cherkasova & elements and approaches that help B2B firms to understand and realize Zainullina, 2020). the importance of firm’s resources by configuring them into the B2B firm’s dynamic capabilities in practice for superior strategies that in­ 5.2. Practical implications fluence B2B firms’ social sustainability performance. The empirical findings from all the hypotheses are in line with the dynamic capability The findings from this research suggest that the technology readiness view in the B2B context. B2B firms should modify functional capa­ of B2B firms has a positive relationship with information and commu­ bilities by combining, developing, and reconfiguring their internal and nication technology capability (ICT). Here, the key takeaway point for external resources to respond to the fluctuating situation to address managers of B2B firms is to be conscious of technological readiness. No social sustainability expectations and improve firms’ economic, envi­ progress in the development of ICT capabilities will be made unless ronmental and social competencies both on the local and global levels employees and channel members devote time and effort to technology (Zhang & Wu, 2017). readiness. Digital technologies must be incorporated into daily opera­ Above all, this study bridges the gap between theory and practice by tions and marketing tasks by managers. Regular employee training on highlighting the lower-order capabilities (e.g., ICT capability) that digital technologies that highlight the advantages needs to receive more managers should focus on to develop higher-order capabilities (AI-based attention. CRM capability). Future business processes will be dominated by AI, and The second finding shows that the technology readiness of B2B firms therefore it is essential that firms leverage data-driven decisions for has a positive relationship with AI-CRM capability and the third finding 10 M.S. Rahman et al. Journal of Business Research 156 (2023) 113525 shows that information and communication technology capability (ICT) the existing organizational procedures. Finally, managers need to ensure has a positive relationship with AI-CRM capability. Managers need to that the process continues if they want to firm to adapt to the changing understand that technology readiness and ICT capabilities are organi­ demands and win over competitors. zational capabilities or zero-order capabilities that will aid in building a first-order capability or DC i.e., AI-CRM capability. Managers must keep 6. Limitations and future research directions in mind that a company can develop a DC if it can mix, construct, and reconfigure its technology readiness and ICT capabilities in response to a DCV has some limitations that must be considered while interpreting changing business situation. Here, the key takeaway point for managers the findings. Many academics argue that managers’ conscious efforts to would be to stay updated with the latest technological innovations in the acquire and strengthen dynamic capabilities (DCs’) may not be benefi­ ICT field and further develop ICT infrastructure in line with the latest cial because DCs have frequently been represented in research as ab­ technological trends. Lastly, adopting flexible IT planning systems stract capabilities. Second, there is no standard measurement procedure jointly with suppliers and customers is essential. to measure DCs’. We have also used cross-sectional data like past studies The fourth finding shows that AI-CRM capability has a positive and further tested the theory. However, long-term and time-series data relationship with B2B firms’ relationship performance. AI-CRM is a DC are needed to quantify the evolution of DC and its impact on relationship that will assist the B2B company in adapting to the ever-changing performance and social sustainability performance. Hence, future re­ business environment. It can support marketers in every typical searchers can design their studies accordingly. In the context of B2B manual work, including managing calendars, scheduling meetings, firms’ social sustainability performance, the authors argue that the making phone calls, taking records, and following up. Therefore, man­ present research efforts complement a few areas of inquiry, such as agers need to align business strategies with AI-CRM goals. Also, access to harnessing technology, data analytics, and relationship value suggested data is necessary to execute AI-CRM; therefore, the focus is required on by Mora Cortez & Johnston (2017) and Lilien (2016). Future research developing a data-driven culture. B2B firms can more effectively gather, also may focus on other areas such as B2B customer journey and rela­ store, manage, and organize interactions with the help of AI-CRM sys­ tionship value, marketing or finance matter, and their impact on reve­ tems, which are strong data aggregation tools. Long-lasting customer nue growth by focusing on a specific industry context by emphasizing relationships are fostered by improved management and automated B2B firms’ social sustainability performance. client outreach. AI-based CRM capability improves relationship perfor­ Future research endeavors can also apply innovation capability, B2B mance, which means that managers need to learn to use AI-based CRM customer journey and B2B relationship value, data analytics capability in the right way to categorize important customers and partners for with advanced technology and revenue growth to determine the chal­ providing customized services to fulfill their needs and demands. lenges for implementing B2B firms’ social sustainability performance. According to Day (2000), the investment in CRM is evident and Above all, the present study applied the proposed theoretical framework concerns the top management. The findings from this research provide testing through the data collected from a specific emerging market essential insight to the managers of B2B firms that, in the short run, firms (South Africa) in a B2B context. To address this limitation, future may not reap the benefits of AI-based CRM systems, but optimizing them research also can use other different emerging country’s B2B contexts, will provide higher returns in the future. Hence, managers should trust such as Latin America (Fastoso & Whitelock, 2011); India (Nyadzayo AI-CRM systems for B2B relationship management. et al., 2018), and Malaysia (Boniface et al., 2012) and analyze data The fifth finding shows that B2B firms’ relationship performance has separately or integrated into a comparative regional study by applying a positive relationship with social sustainability performance. Managers the same proposed theoretical framework for further generalization. need to understand that investing in relationship rents is going to have positive repercussions on society. When firms jointly work with supply 7. Conclusion chain partners in developing sustainable marketing programs it will produce a good outcome. Therefore, managers need to consider the Every firm considers its marketing and sales department and its impact on local communities while making B2B marketing decisions. customers and business partners to be of central importance because Moreover, managers need to respect labor laws and enhance working they are the lifeline of any firm. The findings of this research indicate conditions for providing a safe and healthy environment to the em­ that customer relationship management is an important activity ployees. Managers must maintain a good relationship with stakeholders involved in the downstream part of the supply chain where the focal firm and never show any kind of abusive behavior towards the employees or develops relationships with industrial dealers and wholesalers and in­ local communities. dustrial customers in the supply chain network. In this fourth industrial The sixth finding shows that the technology readiness of B2B firms revolution, firms are benefitting from data-driven decision-making and under the mediation effect of information and communication tech­ AI-CRM has shown huge potential in improving customer management. nology capability is positively related to AI-CRM capability. Managers The literature reveals that AI-CRM can improve a firm’s performance, need to clearly understand that ICT capability is assisting technology but the literature on developing lower-order capabilities that can be readiness to develop AI-CRM capability. Hence, managers must never helpful in building AI-CRM capability in B2B firms lacks investigation. ignore the importance of ICT capability. Managers need to develop be In addition, its connection with social sustainability has been under- creative and develop some new routines in order to develop ICT researched, if researched at all. The current study provides interesting capabilities. insights based on an analysis of data collected from B2B firms. The Lastly, the research finding indicates that industry dynamism mod­ findings reveal that the technology readiness of B2B firms in this fourth erates the relationship between ICT capability and AI-CRM capability. industrial revolution era is positively related to AI-CRM capability while Managers need to understand that the higher the industry dynamism, ICT capability demonstrates a partial mediating role. Industry dyna­ the greater the influence of ICT capability on AI-based CRM capability. It mism is found to act as a contextual variable under the association of reflects that firms need to focus more on the DC i.e., AI-CRM capability information and communication technology capability and AI-CRM in dynamic situations. With the recent trends such as faster technolog­ capability. In addition, it was found that AI-CRM capability improves ical change, shifts in manufacturing and labor markets, the population relationship performance among B2B firms, which ultimately improves shift from villages to cities in developing nations, and climate change it social sustainability performance. In other words, firms willing to is extremely important that managers of B2B firms focus their attention improve social sustainability performance must aim to build AI-CRM on developing the DC i.e., AI-CRM capability to evolve in this changing capability. environment. Hence, the new capabilities must be operationalized by managers who must create and carry out a plan to incorporate them into 11 M.S. Rahman et al. Journal of Business Research 156 (2023) 113525 CRediT authorship contribution statement Methodology. Muhammad Sabbir Rahman: Writing – original draft, Methodol­ Declaration of Competing Interest ogy, Formal analysis. Surajit Bag: Writing – original draft, Formal analysis, Data curation, Conceptualization. Shivam Gupta: Visualiza­ The authors declare that they have no known competing financial tion, Validation, Software, Resources, Project administration. Uthaya­ interests or personal relationships that could have appeared to influence sankar Sivarajah: Supervision, Resources, Project administration, the work reported in this paper. Appendix A1. Operationalization of constructs Construct Items Items Adapted from B2B Firm’s Technology Readiness B2BTR1 Digital technologies generate a perception of productivity improvement Vize et al. (2013) (B2BTR) among industrial manufacturing firms B2BTR2 Using digital technologies at work is giving better results compared to the use of manual techniques a few years back B2BTR3 Digital technologies provide better flexibility at work B2BTR4 Our firm has adopted advanced digital technologies beforehand than our competitors B2BTR5 Our firm provides regular training on digital technologies to employees and demonstrates the benefits B2BTR6 Our firm has invested a huge amount in building the technological infrastructure for the effective application of data-driven AI-CRM Information and Communication ICT1 We have ICT facilities for smooth operations/services Ross et al. (1996); Bharadwaj (2000); Weill & Vitale Technology Capability ICT2 Our firm has established an operative and flexible IT planning process (2002); Stoel & Muhanna (2009); Lu & Ramamurthy (ICT) along with our industrial partners (2011) ICT3 Our firm constantly keep our industrial partners updated with new information technology innovations ICT4 Our firm creates a climate that is supportive to our industrial partners to try out new ways of using the best use of ICT ICT5 My firm’s business operations are shifting toward digital technologies Industry Dynamism IND1 Rapid obsoleting of our products and services are a big concern for our Dubey et al. (2020) (IND) firm IND2 Our firm launch new products and services on a regular basis IND3 Our firm revises the standard operating process from time to time IND4 The buying behaviour of our customers is changing rapidly Relationship Performance in B2B RP1 Our firm has policies that reflect respect for their industrial customers Kumar et al. (1995); Cannon & Perreault (1999); Firms RP2 We need to stay ad a faithful supply chain partner to our industrial clients Hewett & Bearden (2001); Sirdeshmukh et al. (2002); (RP) because we have pride in being related to an organisation that Lages et al. (2008) acclimatizes to technological changes RP3 In our relationship, we share confidential information with our industrial partners as they also sharer reliable information with us RP4 We interact regularly with our existing industrial partners RP5 If we had to do the business again, we would still choose to connect with our existing industrial partners Artificial intelligence-based AICRM1 Customary testing of AI-CRM is critical to look at its suitability Chatterjee et al. (2021d) customer relationship AICRM2 Quality AI-CRM execution for B2B relationship management enhances the management pleasure level (AICRM) AICRM3 B2B firms’ strategies are aligned with AI-CRM objectives AICRM4 We have access to data sets for the actual execution of AI-CRM in customer management AICRM5 Our firm has been able to integrate AI-CRM with our global IT system AICRM6 We feel that our AI-CRM system will be able to handle the increasing pressures related to customer enquiries AICRM7 I have faith in the operation of AI-CRM for improving the social sustainability and ultimately, the reputation of our organisation AICRM8 I am sure that AI-CRM has given us an edge over competitors who are not using such systems AICRM9 I trust that AI-CRM for B2B relationship management has led to improvement in our market shares Social Sustainability Performance SSP1 We are focusing on social sustainable practices and related actions Mani et al. (2018); Kapitan et al. (2019) (SSP) sincerely SSP2 We have developed plans for improving social sustainability SSP3 Our business decisions are made considering the impact on local communities SSP4 We warrant suitable labour working environments SSP5 We have a stringent policy for the prevention of child and forced labour SSP6 We sustain stringent watch on labour rights abuses SSP7 We don’t discriminate against people looking at their age, sex, race, religion, etc. 12 M.S. Rahman et al. Journal of Business Research 156 (2023) 113525 References Cox, A., Lonsdale, C., Sanderson, J., & Watson, G. (2004). Current Approaches to the Analysis of Business Relationships. In Business Relationships for Competitive Advantage (pp. 3–31). London: Palgrave Macmillan. Aiken, L., & West, S. (1991). Multiple Regression: Testing and Interpreting Interactions. Damali, U., Miller, J. L., Fredendall, L. D., Moore, D., & Dye, C. J. (2016). 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