Dr Surajit Bag
email: surajitb@uj.ac.za

Dr Shivam Gupta
email: shivam.gupta@neoma-bs.fr

Dr Ajay Kumar
email: akumar@em-lyon.com

Dr Uthayasankar Sivarajah
email: u.sivarajah@bradford.ac.uk

An Integrated Artificial Intelligence Framework for Knowledge Creation and B2B Marketing Rational Decision Making for Improving Firm Performance

Keywords: Artificial Intelligence, Big Data, Knowledge Management, B2B Marketing, Firm Performance

This study examines the effect of big data powered artificial intelligence on customer knowledge creation, user knowledge creation and external market knowledge creation to better understand its impact on B2B marketing rational decision making to influence firm performance. The theoretical model is grounded in Knowledge Management Theory (KMT) and the primary data was collected from B2B companies functioning in the South African mining industry. Findings point out that big data powered artificial intelligence and the path customer knowledge creation is significant. Secondly, big data powered artificial intelligence and the path user knowledge creation is significant. Thirdly, big data powered artificial intelligence and the path external knowledge creation is significant. It was observed that customer knowledge creation, user knowledge creation and external knowledge creation have significant effect on the B2B marketing-rational decision making. Finally, the path B2B marketing rational decision making has a significant effect on firm performance.

Introduction

Business-to-business (B2B) marketing has offered new prospects and challenges in this digital age [START_REF] Bhandari | Towards an efficient method of modelling "Next Best Action" for Digital Buyer's journey in B2B[END_REF][START_REF] Rust | The future of marketing[END_REF]. Digital B2B marketing models have been found to surpass traditional models [START_REF] Bhandari | Towards an efficient method of modelling "Next Best Action" for Digital Buyer's journey in B2B[END_REF]. Furthermore, [START_REF] Davenport | How artificial intelligence will change the future of marketing[END_REF] indicated that artificial intelligence is on course to disrupt marketing management. In a complex business environment, B2B marketers need smart solutions to automate the process of structuring, standardising, aligning and customising data [START_REF] Fensel | Product data integration in B2B e-commerce[END_REF][START_REF] Jabbar | Real-time big data processing for instantaneous marketing decisions: A problematization approach[END_REF]. Leveraging big data to activate artificial intelligence (AI) technologies provides marketers with a competitive edge and is reflected in marketing strategies and customer behaviours that generate further positive outcomes [START_REF] Jabbar | Real-time big data processing for instantaneous marketing decisions: A problematization approach[END_REF]. Big data coming from social media and search engines can offer important insights for B2B marketers and help build programmes for online advertisements and customer assistance in a pioneering manner [START_REF] Jabbar | Real-time big data processing for instantaneous marketing decisions: A problematization approach[END_REF]. [START_REF] Lee | Making sense of text: artificial intelligence-enabled content analysis[END_REF] provided a roadmap for performing AI-enabled content analysis in the field of marketing. The role of the sales force is changing, and reliance on technology and analytics in order to achieve success is increasing [START_REF] Sleep | The business-to-business inside sales force: roles, configurations and research agenda[END_REF]. To avoid problems when decoding big data sets, Chen et al. (2020) recently provided an overview approach using cognitive computing techniques to evaluate unstructured data sets generated from users. Advances in technology have created a deep impact on marketing. It may be difficult to fully comprehend, but big data has enabled AI to such an extent that it has disrupted marketing management and made the traditional "4P" model increasingly outdated [START_REF] Rust | The future of marketing[END_REF]. The more available big data sets are, the stronger AI applications will be, since machines will be able to reason and act more effectively. AI technologies can help in the selection of international marketing strategies and effective marketing programmes [START_REF] Katsikeas | Revisiting international marketing strategy in a digital era[END_REF]. The literature indicates that although B2B companies use digital marketing tools, most of them fail to exploit them fully. There is a dearth of academic literature available in this area [START_REF] Pandey | Digital marketing for B2B organisations: structured literature review and future research directions[END_REF]. [START_REF] Paschen | Artificial intelligence (AI) and its implications for market knowledge in B2B marketing[END_REF] explained the role of AI in contributing to the knowledge creation in B2B marketing. This study highlighted some key research areas for the future such as: (a) the role of AI in capturing customer knowledge; (b) understanding the movement of user knowledge in B2B marketing; (c) how AI can be leveraged to enhance the market understanding abilities using external market knowledge.

Technology acts as an enabler in the domain of knowledge management apart from organisational structure and culture [START_REF] Syed-Ikhsan | Knowledge management in a public organisation: A study on the relationship between organisational elements and the performance of knowledge transfer[END_REF]. Big data powered AI technology (BDAI) acts as a key technological enabler in this digital age [START_REF] Dubey | Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations[END_REF], particularly for knowledge creation and decision making [START_REF] Duan | Artificial intelligence for decision making in the era of Big Data-evolution, challenges and research agenda[END_REF].

Importantly, there is limited research available for such interplay. Hence, RQ1: Can big data powered AI enhance knowledge creation in B2B marketing?

Customer knowledge, user knowledge and external market knowledge are important parts of the knowledge management process and are responsible for knowledge creation in B2B marketing [START_REF] Abubakar | Knowledge management, decision-making style and organisational performance[END_REF].

Big data can be useful for extracting important information such as web browsing behaviour, demographic features and purchasing patterns from structured and unstructured data inputs and provide useful customer knowledge for rational decision making. User knowledge is also important for developing new products, innovating and improving processes. Psychographic characteristics can be immensely helpful for B2B marketers in developing innovative products [START_REF] Paschen | Artificial intelligence (AI) and its implications for market knowledge in B2B marketing[END_REF]. External market knowledge is important to stay ahead of competitors. Competitive intelligence can be created using AI by analysing unstructured data such as news and social media content that can help B2B marketers make better decisions, improve analytic and decision making capacity and enhance creativity [START_REF] Paschen | Artificial intelligence (AI) and its implications for market knowledge in B2B marketing[END_REF].

Knowledge management and decision making styles are critical for the success of organisations in this digital age [START_REF] Abubakar | Knowledge management, decision-making style and organisational performance[END_REF]. The rational decision making style depends on quality information to make the right choices. This methodological approach involves scrutinising available alternatives when making decisions. It also enhances organisation performance [START_REF] Smolka | Getit together! Synergistic effects of causal and effectual decision-making logics on venture performance[END_REF]. The literature indicates a positive association between knowledge management processes and organisational performance [START_REF] Wu | The impact of knowledge circulation on industrial innovation[END_REF][START_REF] Abubakar | Knowledge management, decision-making style and organisational performance[END_REF]. Knowledge management systems work as superior decision support systems for organisations and eliminate operational bottlenecks [START_REF] Powell | Mapping the values in B2B relationships: A systemic, knowledge-based perspective[END_REF].

Again, very few studies are available that have examined the synergy and convergence of these interesting relationships in the field of B2B marketing. It would therefore be worth investigating and answering the following research question: RQ2: Can knowledge creation improve B2B rational marketing decision making and further enhance firm performance? [START_REF] Lilien | The B2B knowledge gap[END_REF] highlighted the existence of a B2B knowledge gap and suggested stronger academic research output to fill the gap and enable knowledge creation. This study offers relevant and rigorous research output that is practice oriented and extends the knowledge base. Our theoretical model is grounded in Knowledge Management Theory (KMT) and the model has been statistically validated using B2B data from South Africa. The data was collected from mining industry. Technological change and changing consumer behaviour have emerged as a major concern for the mining industry. The South Africa mining industry is in transition from a conventional mining environment to a mechanised shallower, technologically advanced industry. To stay competitive mining companies are adopting digitalisation as a basic part of their new business model encircling all functions: supply chain, marketing and sales and in particular interactions with current and potential customers. This is due to changing consumer behaviour as they link the product with their societal and environmental effect and have the platforms to gain publicity through social media.

Therefore, the responsibility is at the present on mining companies to leverage digital technologies to reach out and exhibit that they have fulfilled their commitments towards environment and society (SA Mine 2019 report).

In managing the B2B marketing of a firm, decisions often have to be made in an environment having some underlying uncertainty. Some examples include segmentation and targeting customers, understanding customer requirements, obtaining data/specifications, offering samples for trial, sales channel management, budgeting, investment in marketing activities, and customer relationship management. In a volatile business environment, decision making further becomes difficult. This study discusses decision making in B2B marketing management context using digital technologies, with a focus on extension of theory and making theoretical contribution based on this empirical study. Alvesson & Sandberg (2011) proposed a scientific method of deriving research questions by adopting the problematization approach. This method is good for finding and contesting assumptions related to the existing theories and further derive the research questions.

However, the commonly used approach taken by past researchers is the gap-spotting method. This gap-spotting method can reinforce or abstemiously revise already established theories (Sandberg & Alvesson, 2011). In the current study the research team favoured gapspotting method to derive the research questions. In specific, the research team used "application spotting" approach that can extend and complement prevailing theory.

Hence, this study extends the knowledge base in the industrial marketing management area from three perspectives: First, this study considers a very important industry from an emerging economy. Secondly, this study considers the knowledge management enabler from technology (big data powered artificial intelligence) point of view, which is a very important dimension of Industry 4.0. Thirdly, this study tested Knowledge Management Theory under industrial marketing management context and demonstrated key links between knowledge management enabler (big data powered artificial intelligence); knowledge management process (customer knowledge creation, user knowledge creation, external market knowledge creation); decision making style (B2B marketing rational decision making) and organisation output (firm performance).

Future research needs to examine the organisational and environmental factors that could influence the extent of knowledge management process and further impact on the decision making style.

The next section presents the scientific debate surrounding model development followed by the creation of research hypotheses. The third section explains the research method that dictated the research flow. Analysis are presented in section four, and the final section provides a framework for discussion by comparing findings with previous studies. The conclusion sets forth the theoretical and practical implications. All practical research work faces some resource constraints, and our study was also confronted with a few of them. The limitations of this work and some food for thought intended for future researchers are showcased at the end.

Theoretical Foundation and Hypothesis Development

Big data powered artificial intelligence

AI is the science of creating smart machines using algorithms to help computers solve problems that can be solved by human beings. As far as the history of AI is concerned, AI was incubated between 1943-1955 and finally took birth in the Dartmouth workshop in 1956. Unfortunately, there was an AI winter from the late 1960s to the late 1970s. This occurred for various reasons, one of them being a derogatory report submitted by Prof. Sir James Lighthill on the state of AI in the United Kingdom. AI research subsequently attracted criticism from the U.S. Congress and funding gradually decreased in the field of neural-net studies. However, from the 1980 onwards, the commercialisation of expert systems in private organisations was noticed [START_REF] Haenlein | A brief history of artificial intelligence: On the past, present, and future of artificial intelligence[END_REF]. The application of intelligent agents such as robots began in 1995 and 2001 witnessed the birth of big data. Big data has empowered AI significantly, enabling AI to power search engines, e-buying and digital assistance [START_REF] Batistič | History, evolution and future of big data and analytics: a bibliometric analysis of its relationship to performance in organisations[END_REF]. In 2005, Prof. Geoffrey Hinton discovered a unique process to efficiently train neural networks.

AI is a sub-field of computer science. The capabilities of AI include sensing, reasoning and acting, and some recent work has focused on developing human reasoning in machines to activate them thinking and working like humans. AI has made significant contribution in the production and distribution of goods. Robots controlled by computer systems is playing an important role in boosting output.AI powered automation has resulted into higher efficiency and output levels at a much lesser costs.AI has brought collaborative robots, self-adapting assembly lines, system for maintenance. For instance, General electric is using system named Predix to monitor goods in the field. AI is gaining popularity in banking, finance and insurance industries. Algorithms offer innovative and tailor-made services (eg., algo-trading, robo-advisors) [START_REF] Krüger | Cooperation of human and machines in assembly lines[END_REF].

Machine learning (ML) primarily use algorithms and data to make decisions. ML has huge importance in B2B marketing. For instance, it can generate rich insights from data related to customer buying behaviour and help in making good decisions [START_REF] Cortez | The future of B2B marketing theory: A historical and prospective analysis[END_REF]Wright et al., 2019;Liu, 2020). Supervised learning, unsupervised learning and reinforcement learning are the main ML techniques [START_REF] Lison | An introduction to machine learning[END_REF]. Supervised learning uses labelled data. Labelled data refers to data in which a label has been correctly assigned to a known feature (or features) of the data.

Unsupervised learning means just that! Scientists do not supervise the model; they do not provide examples of objects with attributes and labels. In unsupervised learning, the model learns on its own. Reinforcement learning draws from behaviourism and is a psychological perspective of human behaviour [START_REF] Lison | An introduction to machine learning[END_REF].

Deep learning is gaining importance but we need to remember that it is different from ML.

Nueral networks are utilised in deep learning to generate solutions [START_REF] Batistič | History, evolution and future of big data and analytics: a bibliometric analysis of its relationship to performance in organisations[END_REF].

Mining is an important economic sector that influences the economic development of a country. The adoption of AI, ML and autonomous technologies (AT) was started in the mining industry about a decade ago [START_REF] Hyder | Artificial Intelligence, Machine Learning, and Autonomous Technologies in Mining Industry[END_REF]. AI and AT have benefitted prospecting and exploration. AI and ML can be useful in developing autonomous drills. Not only that-AI, ML and AT can prevent mining workers from coming in contact with hazardous environment by sending signals and warnings. Autonomous mining haulage trucks, loaders and excavators are gaining popularity as they can operate for 24X7 continuously [START_REF] Hyder | Artificial Intelligence, Machine Learning, and Autonomous Technologies in Mining Industry[END_REF].

Thus, AI research has also been extended to the field of marketing science. Evaluating big data sets that come from various sources and demographics helps marketers to understand which parameters can improve marketing performance. Big data analysis reveals themes and patterns that AI can use to further enhance the efficacy of marketing strategies [START_REF] Paschen | Artificial intelligence (AI) and its implications for market knowledge in B2B marketing[END_REF].

Research aiming to develop theory is a popular subject among social science researchers.

Theory is a proclamation of relationships among concepts within a set of boundary suppositions and restraints [START_REF] Bacharach | Organisational theories: Some criteria for evaluation[END_REF]. Organisation theories can elucidate behaviours, designs and structures [START_REF] Bacharach | Organisational theories: Some criteria for evaluation[END_REF][START_REF] De Camargo Fiorini | Management theory and big data literature: From a review to a research agenda[END_REF] addressed various organisational theories related to large data research. They also provided a research schema to connect organisational theories to large data based empirical research. De Camargo [START_REF] De Camargo Fiorini | Management theory and big data literature: From a review to a research agenda[END_REF] specifically indicate that Knowledge Management Theory (KMT) can be helpful to investigate various aspects of large data adoption. The next section discusses the KMT to explain the technological application, processes and help B2B marketers to enhance firm performance.

Knowledge Management Theory (KMT)

Knowledge management is a popular strategy to enhance the competitive position of an organisation [START_REF] Detienne | Knowledge management: understanding theory and developing strategy[END_REF][START_REF] Dwivedi | Research trends in knowledge management: Analyzing the past and predicting the future[END_REF]. Organisational learning can aid in building new knowledge or providing insights that can change behaviour [START_REF] Mcelroy | Integrating complexity theory, knowledge management and organisational learning[END_REF]. Knowledge can be either explicit or tacit. Explicit knowledge is knowledge that has already been documented in some form (files, databases, manuals, etc.), whereas tacit knowledge is present only among employees and considered an important knowledge resource in any organisation. Through accessing, sharing and adopting both explicit and tacit knowledge, an organisation can dramatically enhance performance [START_REF] Detienne | Knowledge management: understanding theory and developing strategy[END_REF]. In the 21 st century, knowledge has proven to be an important part of production. It is essential for any organisation to learn and adapt in a volatile environment to enhance profit margins. Therefore, the knowledge-based approach is an important pillar for any organisation [START_REF] Tzortzaki | A review of knowledge management theory and future directions[END_REF].

KMT primarily focuses on structural, cultural and technological enablers to manage knowledge and knowledge processes [START_REF] Abubakar | Knowledge management, decision-making style and organisational performance[END_REF]. The theoretical foundation of knowledge management was laid by [START_REF] Hazlett | Theory building in knowledge management: in search of paradigms[END_REF] and [START_REF] Baskerville | The theoretical foundations of knowledge management[END_REF]. Although this theory is more popular in information systems research, KMT can be used for new concepts that provide logical reasoning for managing knowledge and can help define knowledge management processes to project the outcome of a process [START_REF] Baskerville | The theoretical foundations of knowledge management[END_REF]. KMT has been extended to the domain of relationship marketing [START_REF] Rowley | Partnering paradigms? Knowledge management and relationship marketing[END_REF]. Knowledge management and relationships may be different concepts, but they both involve communication, as has been shown in the literature on both. Customer data is useful in relationship management, and knowledge management thus creates an interesting interface for further research [START_REF] Rowley | Partnering paradigms? Knowledge management and relationship marketing[END_REF]. B2B marketing has recently gained significant attention due to the economic power of its transactions [START_REF] Cortez | The future of B2B marketing theory: A historical and prospective analysis[END_REF]. Theoretical advancements have occurred in the last decade and future research needs to focus on innovation, customer relationships, data analytics exploiting technology to increase income and the industrial environment [START_REF] Cortez | The future of B2B marketing theory: A historical and prospective analysis[END_REF].

This study aims to address the calls of previous researchers and to develop the theoretical model based on the preceding discussions (Figure 1). The model for this study considers big data, AI technology and organisation performance aspects in the context of the mining industry.

Knowledge management can be used to deal with the diffusion and development of knowledge and to analyse the knowledge features of a relationship in order to handle it more efficiently [START_REF] Powell | Mapping the values in B2B relationships: A systemic, knowledge-based perspective[END_REF]. Knowledge is an amalgamation of contextual information, the experience of experts and value that can result in innovation [START_REF] Abubakar | Knowledge management, decision-making style and organisational performance[END_REF]. Knowledge management can improve creativity and enhance organisational performance. Knowledge management enablers are driving factors that can enhance knowledge management-related tasks. These driving factors are organisational structure, organisational culture and technology. The role of information technology in eradicating communication-related barriers is noteworthy. Information technology aims to improve mutual learning, knowledge and proper communication [START_REF] Abubakar | Knowledge management, decision-making style and organisational performance[END_REF].

The knowledge management process consists of steps such as acquiring, designing, managing and disseminating knowledge to improve organisational performance.

Knowledge assets including inputs, outputs and brokers are important parts of the knowledge creation process. The knowledge management process can influence the decision making style. A decision making style can be intuitive or rational. However, the rational decision making style is a methodological process and requires intensive information. The final output can be individual performance/organisation performance. Knowledge management systems can improve organisational efficiency and lower operational barriers.

They help make better decisions and improve revenue [START_REF] Abubakar | Knowledge management, decision-making style and organisational performance[END_REF]. B2B marketers also need to focus on data security aspects to reduce any kind of breach [START_REF] Trim | The role of B2B marketers in increasing cyber security awareness and influencing behavioural change[END_REF].

The main pillars of our theoretical model are knowledge management enablers, knowledge management processes, decision making style and organisational output [START_REF] Paschen | Artificial intelligence (AI) and its implications for market knowledge in B2B marketing[END_REF]. The knowledge management enabler considered in our study is a technology (BDAI) that helps streamline the knowledge management process by creating customer knowledge, user knowledge and external market knowledge. This knowledge creation also leads to a rational B2B decision-making style which enhances firm performance [START_REF] Paschen | Artificial intelligence (AI) and its implications for market knowledge in B2B marketing[END_REF].

BDAI construct considered in this study is mainly related to the management of unstructured and structured data sets and further use them in automating certain steps of B2B marketing and sales. CUK construct is related to the use of AI to utilise structured and unstructured data of different nature and perform analysis related to attitude and behaviour of customers. Since user knowledge is important for developing new products and enhancing creativity. Therefore, USK in B2B context is considered in this theoretical model. AI-enabled knowledge about users may indicate how users modify some products themselves and such information can be helpful for new product developments [START_REF] Paschen | Artificial intelligence (AI) and its implications for market knowledge in B2B marketing[END_REF].

EMK is considered in the theoretical model as external market knowledge gives competitive edge to B2B marketers and this study focuses on BDAI that can be used to gather intelligence about external market forces and stakeholders [START_REF] Paschen | Artificial intelligence (AI) and its implications for market knowledge in B2B marketing[END_REF].

Another very important construct used here is BMD; as B2B marketing rational decision making is essential in this volatile business environment. BDAI technology enables in undertaking complex work and in making better informed decisions (Daun et al. 2019).

The endogenous construct i.e. firm performance measures the financial and marketing performance of the firm; for instance, ability to retain customers much better than competitors, improvement in ROI and market share [START_REF] Wamba | Big data analytics and firm performance: Effects of dynamic capabilities[END_REF]). The research team considered the effect of control variables such as size of firm and firm age. Literature indicates that larger the size of the firm; the greater is the capability to acquire and access resources. Similarly, the more is the age of the firm, the greater is the maturity level and high chances of adopting advanced technologies. Therefore, it is important that both these variables be controlled during the study to get accurate results.

Figure 1. Theoretical model

Hypothesis development

The seven research hypotheses were discussed as under.

Big data powered artificial intelligence and customer knowledge creation

In this digital age, organisations have access to big data sets that are structured/unstructured for analysis [START_REF] Gupta | Achieving superior organisational performance via big data predictive analytics: A dynamic capability view[END_REF]. However, marketers face two major challenges when using data that are available from social media. First, the unstructured nature of the data; and second, the fact that the high volume of data sets makes the standard analysis process impracticable [START_REF] Liu | A structured analysis of unstructured big data by leveraging cloud computing[END_REF]. [START_REF] Balducci | Unstructured data in marketing[END_REF] contributed to the literature by performing research on the usefulness of unstructured data in marketing. The analysis of such data sets requires advanced analytical methods and computational techniques (Dubey et al., 2019 a,b). Data visualisation methods help B2B marketers decode complex data and gain insights for decision making [START_REF] Duan | Artificial intelligence for decision making in the era of Big Data-evolution, challenges and research agenda[END_REF]. AI bots have gained popularity recently, and they can automate selling activities, supplementing the capabilities of existing sales force [START_REF] Leung | A B2B flexible pricing decision support system for managing the request for quotation process under e-commerce business environment[END_REF][START_REF] Paschen | Artificial intelligence (AI) and its implications for market knowledge in B2B marketing[END_REF].

Big data analytics (BDA) can exploit structured and unstructured data and extract key information such as web browsing frequency, types of industrial product searches, purchase history and buyer attributes that can help develop knowledge about customers [START_REF] Chintagunta | Marketing Science and Big Data[END_REF]. B2B marketers can also use ML and predictive analytics tools to develop the profile of current and future customers and devise a relationship strategy accordingly [START_REF] Paschen | Artificial intelligence (AI) and its implications for market knowledge in B2B marketing[END_REF][START_REF] Hofacker | Digital marketing and business-to-business relationships: a close look at the interface and a roadmap for the future[END_REF]. Every stage of the B2B sales process can be augmented with BDAI. B2B marketers can use the predictive modelling approach to identify high quality business leads without utilising many human resources. At the final stage of the sales process, B2B marketers can use AI presentation bots to prepare exceptional presentations. Emotion AI can be useful in negotiations when making business deals and prevail over any doubts that may arise in the customer's mind. AI can also help reduce competition and enhance an organisation's own value offerings. AI-based chat bots can be beneficial in doing order follow-ups with industrial customers and automating order processing activities [START_REF] Paschen | Artificial intelligence (AI) and its implications for market knowledge in B2B marketing[END_REF]. B2B marketing efficiency can therefore increase three-fold with the application of BDAI technology [START_REF] Jabbar | Real-time big data processing for instantaneous marketing decisions: A problematization approach[END_REF][START_REF] Liu | Analyzing the impact of user-generated content on B2B Firms' stock performance: Big data analysis with machine learning methods[END_REF]. [START_REF] Davenport | How artificial intelligence will change the future of marketing[END_REF] has indicated that advanced technologies like AI can change the future of marketing. This is possible by exploiting BDAI technology through the generation of knowledge from customers and further developing appropriate B2B marketing strategies [START_REF] Bonnin | The narrative strategies of B2B technology brands[END_REF]. Therefore, we hypothesize: H1: Big data powered artificial intelligence has a positive relationship with customer knowledge creation

Big data powered artificial intelligence and user knowledge creation

The digital revolution has increased computational abilities and big data seems to have made AI more powerful in recent times [START_REF] Duan | Artificial intelligence for decision making in the era of Big Data-evolution, challenges and research agenda[END_REF]. The literature indicates that BDAI can help create user knowledge for better decision making [START_REF] Paschen | Artificial intelligence (AI) and its implications for market knowledge in B2B marketing[END_REF]. Social media marketing and advertising is gaining popularity [START_REF] Dwivedi | Social media marketing and advertising[END_REF]. Content from social media platforms can be used for big data analysis and to gain further insights related to users' product buying needs, likes and attitudes towards industrial products. AI technology can identify buyer emotions and attitudes from text-based data. Psychographic features can be extremely useful for developing new products. Furthermore, BDAI enabled knowledge about users may reveal important information concerning user modifications of products and services (Huang and Rust, 2020) that can be valuable for product development [START_REF] Paschen | Artificial intelligence (AI) and its implications for market knowledge in B2B marketing[END_REF]. Therefore, we hypothesize: H2: Big data powered artificial intelligence has a positive relationship with user knowledge creation

Big data powered artificial intelligence and external market knowledge creation

BDAI has proven to be highly beneficial in this digital age [START_REF] Duan | Artificial intelligence for decision making in the era of Big Data-evolution, challenges and research agenda[END_REF]. AI models can be helpful to collect market intelligence data such as competitor activities, competitor product launches, changes in legislation and entry of new companies in the market [START_REF] Paschen | Artificial intelligence (AI) and its implications for market knowledge in B2B marketing[END_REF]. AI helps with market knowledge creation by decoding online web-based content with the aid of big data analytics. Natural language processing (NLP) and ML tools can be helpful for assessing the quality of market news and distinguishing between fake news and content. This adds great value in B2B marketing programs. BDAI can create a competitive advantage by decoding important information from competitors' documents such as news or social media content [START_REF] Paschen | Artificial intelligence (AI) and its implications for market knowledge in B2B marketing[END_REF]. Therefore, we hypothesize: H3: Big data powered artificial intelligence has a positive relationship with external market knowledge

Customer knowledge creation and B2B marketing rational decision making

Customer knowledge creation is important to gain a competitive advantage and to stay ahead of competitors. Customer knowledge can help B2B marketers make rational decisions [START_REF] Paschen | Artificial intelligence (AI) and its implications for market knowledge in B2B marketing[END_REF]. Rational decision making is a methodological approach that involves scrutinising alternatives and arriving at the optimal solution. AI can help B2B marketers make better decisions and increase creativity [START_REF] Duan | Artificial intelligence for decision making in the era of Big Data-evolution, challenges and research agenda[END_REF]. When based on KMT, knowledge creation has been shown to be a vibrant, multifaceted and intricate process [START_REF] Abubakar | Knowledge management, decision-making style and organisational performance[END_REF]. Customer knowledge is an asset that can help in knowledge creation by converting tacit knowledge into explicit knowledge. Customer knowledge is relevant and can be relied on to perform big data analyses and used to make important choices. Since rational decision making is a cognizant and thoughtful process free from intuition, the quality of decision making is much higher. Rational decisions made based on customer knowledge can help make B2B marketing more effective [START_REF] Paschen | Artificial intelligence (AI) and its implications for market knowledge in B2B marketing[END_REF]. Therefore, we hypothesize:

H4: Customer knowledge creation has a positive relationship with B2B marketing rational decision making

User knowledge creation and B2B marketing rational decision making

User knowledge creation can be gained using BDAI technology enablement as discussed in the previous sections. User knowledge involves psychographic characteristics and user experiences, likes, dislikes, attitudes and behaviours towards a company's product and service offerings [START_REF] Paschen | Artificial intelligence (AI) and its implications for market knowledge in B2B marketing[END_REF]. These data can be useful in making rational B2B decisions related to new product development and service offerings. B2B marketers can acquire, manage and process this rich information and create knowledge to further improve B2B marketing and sales processes. User knowledge creation brings an added advantage to B2B marketers when making rational choices by checking alternate options and probable consequences [START_REF] Abubakar | Knowledge management, decision-making style and organisational performance[END_REF]. Therefore, we hypothesize: H5: User knowledge creation has a positive relationship with B2B marketing rational decision making

External market knowledge creation and B2B marketing rational decision making

External market knowledge can keep B2B marketers informed about the latest happenings in the market. It allows B2B marketers to become cautious about competitors' activities and remain alert with regard to influences on their own brands. Fake news is often released by competitors to defame other brands, and these threats can be effectively managed by keeping abreast of the latest updates by leveraging BDAI technology [START_REF] Paschen | Artificial intelligence (AI) and its implications for market knowledge in B2B marketing[END_REF]. Rational decision making involves the decisive assessment of facts in a planned process that requires time and hard work. External market knowledge creation can be captured, stored, processed and organised using BDAI technology for problem solving and decision making [START_REF] Duan | Artificial intelligence for decision making in the era of Big Data-evolution, challenges and research agenda[END_REF]. Therefore, we hypothesize: H6: External market knowledge creation has a positive relationship with B2B marketing rational decision making 2.3.7 B2B marketing rational decision making and firm performance [START_REF] Prior | Revisiting contemporary issues in B2B marketing: It's not just about artificial intelligence[END_REF] suggested future research directions when they stated that attention must be given to decoding information for better solutions and that B2B marketing must be understood from an information requirement perspective. Rational decision making compels B2B decision makers to consider multiple situations and the probability of every selection before arriving at a given decision. Rational decision making criteria are established to explore a full set of choices. Therefore, it is important for relevant and reliable information to be available in order to make rational decisions. Rational decision making is free from assumptions and biases and thus leads to quality decisions and improved organisational performance [START_REF] Smolka | Getit together! Synergistic effects of causal and effectual decision-making logics on venture performance[END_REF]. Organisational performance involves the overall health of the organisation. Performance can be measured by comparing targets with actual performance. Knowledge management can help enhance organisation processes and eliminate other barriers by fostering rational decisions [START_REF] Johnston | 21 The organisational buying center: innovation, knowledge management and brand[END_REF][START_REF] Abubakar | Knowledge management, decision-making style and organisational performance[END_REF]. B2B rational decisions can help retain customers and improve profit margins. B2B marketers can also quickly launch new products and penetrate new markets (Singh et al., 2000). Market share can improve significantly with the aid of rational decision making [START_REF] Yu | Decision-making factors for effective industrial e-procurement[END_REF][START_REF] Wamba | Big data analytics and firm performance: Effects of dynamic capabilities[END_REF][START_REF] Wilson | Can artificial neural network models be used to improve the analysis of B2B marketing research data?[END_REF]. Therefore, we hypothesize: H7: B2B marketing rational decision making has a positive relationship with firm performance

Research Methods

This section presents the research strategy of this study.

Sample selection

We used an online survey with samples from the mining and mineral processing industry in South Africa. The sample for the pilot survey and final survey was established based on random sampling from Directory No. D1/2016 "Operating mines and quarries and mineral processing plants in the Republic of South Africa, 2016" Directorate of Mineral Economics. This database was compiled by Ms M C Lourens, and the 25 th revised edition was published in January 2016 and considered in our study.

In WarpPLS two methods are used to estimate minimum required sample sizes, the inverse square root and gamma-exponential methods [START_REF] Kock | Minimum sample size estimation in PLS-SEM: The inverse square root and gamma-exponential methods[END_REF].

The "statistical power and minimum sample size requirements" analysis indicates 655 samples as per the inverse square root method; and 647 samples as per the gammaexponential method. However, the research team selected the figure obtained from inverse square root method and further sent the online survey request to executive from every selected company and a total of 655 requests were sent out during the final survey.

Survey instrument

A structured questionnaire was used to collect the survey data. The questionnaire was based on a Likert-type scale (five points), where 1 means "strongly disagree"; 2 means "disagree"; 3 means "neutral"; 4 means "agree" and 5 means "strongly agree". The instrument was designed in consultation with other research team members from academia. Following the guidelines of [START_REF] Dillman | Mail and Internet surveys: The tailored design method--2007 Update with new Internet, visual, and mixed-mode guide[END_REF], background clearness of all the constructs was authenticated by two experts from academia. As per the suggestions of both experts, various changes were made to the questionnaire to suit this research study and the final version of the questionnaire was later approved by the same academic experts [START_REF] Chen | Towards a theory of supply chain management: the constructs and measurements[END_REF]. The survey was then sent out to 75 executives working in different mining and mineral processing companies located in the Gauteng, North West, Mpumalanga and Limpopo provinces of South Africa. After 36 data points were received from the pilot survey, research team checked the reliability and validity parameters and found that the results were acceptable. The research team did not make any changes to the questionnaire based on the pilot survey.

Data collection

The invitation letter to fill in the questionnaire was e-mailed along with the online link to senior executives working in mines and mineral processing plants in South Africa. A total of 655 requests were made. After conducting follow-up, 306 completed questionnaires (response rate of 46.71%) was received. The research team did not receive any incomplete questionnaires, as the settings in the google form-based questionnaire did not allow for this, and a warning would pop up with a note to prevent any duplicate or incomplete submissions. The details of the respondents and their organisations are provided in Table 1.

Common method bias

Online surveys in marketing research are often criticised for attracting common method variance (CMV) problems [START_REF] Hulland | Marketing survey research best practices: evidence and recommendations from a review of JAMS articles[END_REF]. Few key problems were identified as vital elements of survey validity: perform pretesting to make sure that good quality survey instruments are used; considering use of multi-item scales with strong psychometric properties to control measure unreliability; the use of research design elements that a priori assist to lower CMV; and considering tests to account post hoc for common method bias [START_REF] Hulland | Marketing survey research best practices: evidence and recommendations from a review of JAMS articles[END_REF].

Post hoc methods for avoiding common method variance problems include Harman's single factor test, partial correlation procedures controlling for CMV at the scale level, use of a directly measured or single unmeasured (latent) method factor to account for CMV at the item level, and use of multiple method factors to correct for CMV [START_REF] Hulland | Marketing survey research best practices: evidence and recommendations from a review of JAMS articles[END_REF].

Research team therefore took the utmost care and necessary precautions from the beginning of the survey to avoid any such problems. First, the research team drafted an invitation letter for the survey stating the reason the survey was being conducted and also indicating that participants can stop the survey at any time if they no longer wish to complete the survey.

The system would not allow incomplete questionnaires to be submitted. Second, all the questions were drafted in simple English and relevant to the industry so that readers would be spared from any confusion. Third, all of the questions involved simply ticking the response and did not include any writing. The questions were divided into two parts for easy understanding and finally, the questionnaire was kept short to prevent the survey participants from losing patience. Research team also performed post-hoc test and calculated the Harman's one factor test. The results indicated that a single factor accounts for 24.587 percent of total variance, which is far less than 50 percent. Therefore, it can be concluded that there is no danger of CMV (Podaskoff et al., 2003).

Typically, researchers do not worry much about method bias in PLS because PLS often focuses on formative factors. Formative factors very rarely suffer from method bias. The article written by Ned Kock suggests about dealing with method bias in PLS (Kock, 2015;[START_REF] Kock | WarpPLS User Manual: Version 7[END_REF]. As per the Kock (2020) full collinearity VIFs can be considered for CMV tests. This is better than the traditional tests conducted by researchers using EFA. PLS-based SEM recommends threshold value of full collinearity VIF should be 3.3 or below to avoid multicollinearity and CMV problems. Findings indicate that the full collinearity VIF for latent variables is below the threshold value and it is proved that CMV is not an issue in this study.

Non-response bias

The primary data was received in two phases. One set of responses was received before follow-up and the second set was received after doing follow-up with the potential respondents. The early wave (120 responses) and the late wave (187 responses) were compared using Levene's test to check for non-response bias [START_REF] Armstrong | Estimating nonresponse bias in mail surveys[END_REF].

Levene's homogeneity of variance test was conducted to check whether the distribution of our variables changed depending on the wave. SPSS software was used to compare means and perform the analysis by selecting one-way ANOVA to check the Levene statistic. The research team found that none of the values were significant, which means there was no difference between the waves.

Data Analysis and Findings

Partial least square-based structural equation modelling

Multivariate data analysis (MDA) has been shown to be useful in evaluating relationships among numeric variables while simultaneously controlling for the impact of other variables [START_REF] Hair | Multivariate data analysis[END_REF]. Multiple regression, path analysis and structural equation modelling (SEM) are the most commonly used MDA techniques. Furthermore, SEM is divided into two categories: covariance-based SEM (CB-SEM) and variance-based SEM. Variancebased SEM is also known as PLS-SEM. The benefits of PLS-SEM are: a) mainly getting a solution, even for complex models; b) it is not essential for variables to conform to parametric analysis conditions, for example, normality and larger sample size; c) it makes it possible to estimate parameters that have multiple formative latent variables and to assess moderating effects [START_REF] Kock | Using WarpPLS in e-collaboration studies: An overview of five main analysis steps[END_REF]. The research team decided to use partial least squarebased structural equation modelling (PLS-SEM). WarpPLS software was chosen to run the analysis. WarpPLS was invented by Ned Kock [START_REF] Kock | WarpPLS 5.0 user manual[END_REF], and as the software is very user friendly, it has become quite popular in a very short period of time. WarpPLS-based analysis has been used previously by leading researchers in the field of management, and their work has been accepted in top ranked journals (e.g., Dubey et al. 2019 a,b;[START_REF] Wamba | Big data analytics and firm performance: Effects of dynamic capabilities[END_REF]. Scientific debate over the use of CB-SEM or PLS-SEM is still ongoing.

However, simple guidelines were set forth by Preacher and Hayes (2008) and [START_REF] Hair | PLS-SEM or CB-SEM: updated guidelines on which method to use[END_REF] to select the right technique depending on the nature of the research work. The research team met the criteria as suggested by [START_REF] Hair | PLS-SEM or CB-SEM: updated guidelines on which method to use[END_REF] to proceed with the use of PLS-SEM. In the next stage, we checked the reliability and validity of the measurement model based on the suggestions of [START_REF] Peng | Using partial least squares in operations management research: A practical guideline and summary of past research[END_REF].

Measurement model

Before checking the results of the hypothesis testing, we checked the VIF value, as it helps identify the presence of data multicollinearity. The findings indicated that the average full collinearity VIF was 2.746. This is acceptable, as it should be less than 5. Ideally it should be lower than 3.3, and our value is much lower than the ideal value. It can therefore be stated that there are no multicollinearity issues in our work. Research team also checked the model fit indices and found them to be significant. The goodness of fit value shows a large fit (see Table 4).

The causality assessment indices employed (see [START_REF] Kock | WarpPLS User Manual: Version 7[END_REF] were the Simpson's paradox ratio (SPR), R-squared contribution ratio (RSCR), statistical suppression ratio (SSR) and nonlinear bivariate causality direction ratio (NLBCDR). They are within the threshold values and rule out any endogeneity problems (Kock, 2015) (see Table 2). The results indicate that the reliability of all the constructs accounts for 50% variance in their related items. Combined loadings and cross-loadings were found to be above 0.50 (refer to Appendix B).

Discriminant validity was checked (see Table 3) and the research team established discriminant validity by following the Fornell-Larcker criterion, i.e. for any latent variable, the square root of the AVE must be higher than its correlation with any other latent variable (Kock, 2014;2020). The results suggest that our measurement model displays acceptable discriminant validity.

Results

The hypothesis assessment findings are presented in Figure 2. The findings show that there is a positive relationship between BDAI →CUK (β = 0.67), BDAI →USK (β = 0.51), BDAI →EMK (β = 0.60), CUK →BMD (β = 0.27), USK →BMD (β = 0.38), EMK →BMD (β = 0.86) and BMD →FIP (β = 0.72). Statistical significance was considered at 5% as the cutoff value for accepting/not accepting research hypotheses. All hypotheses were supported except the effect of two control variables which were found to be non-significant.

Figure 2. Model after PLS-SEM analysis

The results of hypotheses testing are presented in Table 4.

Discussion

Information technology-based applications are gaining importance in designing intelligent products for human usage [START_REF] Shareef | A new health care system enabled by machine intelligence: Elderly people's trust or losing self control[END_REF]. Marketers can benefit significantly by adopting AI and VR technologies [START_REF] Dwivedi | Setting the future of digital and social media marketing research: Perspectives and research propositions[END_REF]. AI is the new electricity in this digital age [START_REF] Grover | Understanding artificial intelligence adoption in operations management: insights from the review of academic literature and social media discussions[END_REF]. AI based intelligent systems have the power to support strategic decision making, where strategic intelligence is necessary (Martínez-López & Casillas, 2013). However, there are limited research studies in this domain [START_REF] Pandey | Digital marketing for B2B organisations: structured literature review and future research directions[END_REF][START_REF] Pillai | Shopping intention at AI-powered automated retail stores (AIPARS)[END_REF]. Hence, this study aims to establish that "big data powered artificial intelligence" acts as a knowledge management enabler that drives "knowledge management process" i.e. customer knowledge creation, user knowledge creation and external market knowledge creation. These three dimensions of knowledge management process play a critical role in B2B marketing decision making style which will enhance the firm performance. [START_REF] Bacharach | Organisational theories: Some criteria for evaluation[END_REF] defined theory as a statement of links among ideas within a boundary set of assumptions and constraints. The aim of a theoretical statement is to organize (parsimoniously) and to communicate (clearly).

Further, [START_REF] Wacker | A conceptual understanding of requirements for theory-building research: Guidelines for scientific theory building[END_REF] stressed that academic research focuses on theory building. Good theories can be used to understand specific situations. Researchers need to enhance theory to make a theoretical contribution [START_REF] Reay | What constitutes a theoretical contribution in family business?[END_REF]. The fundamental building blocks of theory necessitates that researchers search answers for the four questions i.e. what are the main elements that are important to the elucidation of the phenomenon of attention? How are these main elements linked with one another? Why does this depiction of the phenomenon justify to be considered reliable? What are the settings under which researcher ought to assume the predictions of the theory to hold correct?

Byron & Thatcher (2016) provided recipes for making theory building and making theoretical contributions. Using KMT theory as a theoretical lens the research team answered two research questions in this study.

RQ1: Can big data powered AI enhance knowledge creation in B2B marketing? and RQ2:

Can knowledge creation improve B2B rational marketing decision making and further enhance firm performance?

The results indicate that the path "big data powered artificial intelligence and customer knowledge creation" is significant. Hence, H1 is supported. Second, the path "big data powered artificial intelligence and user knowledge creation" is significant which supports H2. Third, "big data powered artificial intelligence and external market knowledge creation" is significant which supports H3. Fourthly, the path "customer knowledge creation and B2B marketing rational decision making" was found to be significant and H4 was supported.

Fifth, the path "user knowledge creation and B2B marketing rational decision making" was found to be significant and H5 was supported. Sixth, the path "external market knowledge creation and B2B marketing rational decision making" was found to be significant and H6 was supported. Finally, the path "B2B marketing rational decision making and firm performance" was shown to be significant. Hence, H7 was supported.

It is clear from the findings that AI is changing the marketing landscape as previously indicated by [START_REF] Davenport | How artificial intelligence will change the future of marketing[END_REF]. Mining industry operations are complex and face many challenges related to energy access, strict health and safety regulations, capital, commodity price volatility and the environmental footprint, which makes the buying and selling processes for mining companies very complex and strict [START_REF] Hamann | Mining companies' role in sustainable development: The'why'and'how'of corporate social responsibility from a business perspective[END_REF]. Marketers who deal with mining companies therefore face many barriers when conducting B2B transactions. Mining and mineral processing companies tend to use sustainability practices to enhance business performance [START_REF] Gomes | Management for sustainability in companies of the mining sector: an analysis of the main factors related with the business performance[END_REF]. Marketers need to consider sustainability parameters in their product and service offering while dealing with mining and mineral processing companies. The current research applied Knowledge Management theory to better understand the ways B2B marketers leverage big data to empower AI, and the empirical testing of the theoretical model has yielded insights that can potentially make rich theoretical contributions. Big data powered AI enhances customer knowledge creation, user knowledge creation and external market knowledge creation, which further improves B2B marketing rational decision making and finally improves firm performance. Although these findings are supported by the work of [START_REF] Paschen | Artificial intelligence (AI) and its implications for market knowledge in B2B marketing[END_REF] but current research findings offer some interesting insights such as the impact of BDAI on customer knowledge creation is found to be much stronger than the impact of BDAI on external knowledge creation and user knowledge creation. It is therefore clear that B2B marketers in South African mining and mineral processing industry prefer to leverage BDAI and use structured and unstructured data of different nature and perform analysis related to attitude and behaviour of customers. In addition, machine learning and predictive analytics tools are increasingly used to create profile of future customers and develop relationship strategy accordingly. It was found that external market knowledge creation has more impact on B2B marketing rational decision making than customer knowledge creation, user knowledge creation.

Finally, the results indicate that B2B marketing rational decision making is very important for the strong performance of firms. [START_REF] Syam | Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice[END_REF] and recently [START_REF] Dwivedi | Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy[END_REF] have rightly pointed out that the fourth industrial revolution will be disrupted by artificial intelligence powered by big data technology. These advanced technologies will impact B2B marketing and sales management [START_REF] Davenport | How artificial intelligence will change the future of marketing[END_REF]. The theoretical contributions and implications of current study is presented below.

Theoretical contributions and implications

This study tested the theoretical model by drawing on KMT. Knowledge management can be used to deal with the diffusion and development of knowledge and to analyse the knowledge features of a relationship in order to handle it more efficiently [START_REF] Powell | Mapping the values in B2B relationships: A systemic, knowledge-based perspective[END_REF]. Knowledge is an amalgamation of contextual information, the experience of experts and value that can result in innovation [START_REF] Abubakar | Knowledge management, decision-making style and organisational performance[END_REF].

The theoretical model in this study is unique which further provided some testable hypotheses. The research team tested KMT theory in context to B2B marketing and sales. In

Practical implications

The digital disruption has changed the work flow and processes in organisations.

Information and communication technologies (ICT) are playing a critical role in the success/failure of a business. In Africa the mining and minerals processing business operates in a volatile environment. B2B marketers face lot of challenges while working in such environments. Advanced ICT technologies can not only help in capturing the market information but also provides important details of the plant operations that can be help in designing of right products and solutions. Top management can leverage advanced ICT to track the day-to-day sales calls and meetings made by every sales person. The sales team can capture the meeting related details on their tablet/mobile phone using some special apps.

They can as well highlight the need for management intervention, if any. Problems are solved much faster resulting in increased efficiency.

Not only that the analysis of large data sets can also be useful for understanding customers buying patterns, sentiments and attitudes towards particular brands. Further AI can be used to support to the B2B marketers. Therefore, B2B marketers can leverage big data powered AI to enhance knowledge management process much better in this I4.0 era.

Knowledge management is important to progress forward and enhance business performance. From a realistic point of view, a few of our study's takeaway points can be of use to executives. First, senior leaders need to pay proper attention to big data that can help harness AI and generate value. Second, it is essential to focus on knowledge management processes involving customer knowledge creation, user knowledge creation and external market knowledge creation in order to make B2B marketing rational decisions. Finally, the quality of B2B marketing decisions must be high, as they influence firm performance.

Our findings indicate that important BDAI features include access to large, unstructured and fast-moving data for analysis, the integration of external and internal data for analysis, the Hadoop platform for analysis, BDAI training and collaboration with DTI and universities.

Firms need to focus more on these BDAI enablers in particular.

The findings also indicate that in the CUK-related group, AI presentation bots that can assist sales teams in making convincing presentations are gaining more popularity. In the user knowledge category, however, two items are making more impact than the others: first, the capabilities of AI to understand emotions, values and attitudes in a text, and second, the ability of AI to recognise patterns in customer posts and their attitudes towards a particular product, which can in fact provide a great deal of valuable information for knowledge management. When it comes to external knowledge management, the two items that created the greatest impact are the ability of AI to gather business intelligence related to competitors and other stakeholders, and the ability of AI to exploit market knowledge by analysing unstructured big data sets from various sources. This can enhance the knowledge management process in B2B marketing.

The findings reveal that most important B2B marketing rational decisions are related to BDAI based expert systems to support decision making at strategic, operational and tactical levels. Second, the knowledge gained from BDAI application can keep firms up-to-date in terms of their current position compared to their competitors and help advance new product innovations. Finally, knowledge generated through BDAI based applications can help B2B marketers to be cautious about their brands and eliminate any threats that arise from fake news.

In terms of the impact of B2B marketing rational decisions on firm performance, the greatest benefits actually include better customer retention, penetration in new markets and the quick launch of new products. This study therefore advances the use of KMT in the field of B2B marketing.

Limitations and future research direction

The research team have taken the necessary precautions to avoid any kind of bias during data collection and checked all parameters before proceeding with data analysis.

  The questionnaire was divided into two sections: Part A and Part B. The first part consisted of questions related to the role of the respondent, work experience, nature of business activities, number of employees in the company, age of the organisation and annual turnover. The second part consisted of questions related to BDAI, customer knowledge, user knowledge, external market knowledge, B2B marketing decisions and firm performance. The scale was developed based on past studies. For example, BDAI questions (11 items) were taken from Dubey et al. (2019a,b), customer knowledge (8 items), user knowledge (6 items) and external market knowledge questions (4 items) were adapted from Paschen et al. (2019), B2B marketing decision questions (5 items) were taken from Daun et al. (2019) and firm performance questions (9 items) were adapted from Wamba et al. (2017) (refer to Appendix A).

  such process it has contributed to the existing literature from two aspects. First, the power of digital technology (BDAI) has been established in this work which basically acts as knowledge management enabler. Second, this research has established that knowledge management process including customer knowledge creation, user knowledge creation and external market knowledge creation is very important for B2B rational decision making.Hence, this study extends the knowledge base by clearly indicating the importance of digital technology-based knowledge enabler in creating knowledge and improving B2B decision making for enhancing organisational performance.

Table 1 .

 1 Details of respondents and their organisation

	Details	Respondents	(In Number)	(In Percentage)
		General Manager	45	14.70
	Designation	Senior Manager	207	67.64
		Manager	25	8.16
		Junior Manager	29	9.47
	Experience (Years)	Above 20 10 to 20 Below 10	77 185 44	25.16 60.45 14.37
		Mines,
	Nature of Business Activities	Quarries and allied industries Mineral processing and allied	145 161	47.38 52.61
		industries
		Above 20	272	88.88
	Age of the Firm	10 to 20	28	9.15
	(Years)	5 to 10	6	1.96
		Below 5	0	0.00

Table 2 .

 2 Model fit and quality indices

	Model fit and quality indices
	Average path coefficient (APC)=0.456, P<0.001
	Average R-squared (ARS)=0.530, P<0.001
	Average adjusted R-squared (AARS)=0.528, P<0.001
	Average block VIF (AVIF)=5.131, acceptable if <= 5, ideally <= 3.3
	Average full collinearity VIF (AFVIF)=2.746, acceptable if <= 5, ideally <= 3.3
	Tenenhaus GoF (GoF)=0.575, small >= 0.1, medium >= 0.25, large >= 0.36
	Sympson's paradox ratio (SPR)=1.000, acceptable if >= 0.7, ideally = 1
	R-squared contribution ratio (RSCR)=1.000, acceptable if >= 0.9, ideally = 1
	Statistical suppression ratio (SSR)=1.000, acceptable if >= 0.7
	Nonlinear bivariate causality direction ratio (NLBCDR)=0.833, acceptable if >= 0.7

Source: WarpPLS output

Composite reliability shows BDAI (0.893), CUK (0.821), USK (0.794), EMK (0.844), BMD (0.842) and FIP (0.922) values are higher than 0.70, and their AVE values are above 0.50.

Table 3 .

 3 Discriminant validity test results

		BDAI	CUK	USK	EMK	BMD	FIP
	BDAI	0.665	0.531	0.419	0.546	0.544	0.414
	CUK	0.531	0.627	0.424	0.449	0.571	0.440
	USK	0.419	0.424	0.684	0.767	0.554	0.347
	EMK	0.546	0.449	0.677	0.768	0.641	0.502
	BMD	0.544	0.571	0.554	0.641	0.720	0.718
	FIP	0.414	0.440	0.347	0.502	0.718	0.760
			Source: WarpPLS output
	Cronbach's alpha yielded the following values: BDAI (0.866), CUK (0.750), USK (0.683),
	EMK (0.747), BMD (0.765) and FIP (0.902). The values indicate that they are reliable.

Table 4 .

 4 Summary of hypotheses testing

	No. Hypotheses

  The study was conducted among South African-based companies involved in B2B business in the mining and mineral processing industry, and readers/researchers must therefore interpret our findings in light of the context. AI applications are currently in the nascent stage, and future researchers can explore the outcome of collaborative relationship aspects (supplier collaboration, human and machine collaboration, collaboration with project teams and universities) on AI and firm performance. Institutional mechanisms to drive big data andThe model demonstrates the impact of the knowledge management enabler (BDAI) on the knowledge management process for B2B marketing decisions. The key findings are: big data powered artificial intelligence have a positive relationship with customer knowledge creation. Second, big data powered artificial intelligence have a positive relationship with user knowledge creation. Third, big data powered artificial intelligence have a positive

	USK3	The AI system can understand emotions 0.019 BDAI10 Our BDAI team coordinate effectively with other departments and stakeholders AI bots can assist sales team by USK3 and attitudes in a text AI technology enables in undertaking -0.041 0.011 0.860 -0.226 0.025 0.048 0.050 <0.001 BMD5 complex work and make sound judgements -
	USK5	BDAI11 -0.074 -0.033	automating certain steps of sales and Psychographic characteristics can be a 0.946 0.435 -0.096 0.045 0.049 <0.001
	relationship with external market knowledge creation. Fourth, customer knowledge creation has a positive relationship with B2B marketing rational decision making. Fifth, user knowledge creation has a positive relationship with B2B marketing rational decision improving capabilities of sales force BDAI can be used to utilise structured and unstructured data of different nature and USK4 valuable source of insight for B2B marketers in development of new products AI can be used to identify themes and patterns in social media post related to FIP1 -Our firm is able to retain customers much USK6 -0.101 -0.011 0.948 0.377 -0.077 0.046 0.049 <0.001 better than competitors FIP2 -Sales increase has happened in our firm EMK1 -0.123 -0.003 0.629 0.874 -0.077 0.060 0.050 <0.001 CUK1 perform analysis related to attitude and behaviour of customers USK5 product buying and their product use experiences Our firm is able to achieve high profit -FIP3 margins EMK2 0.083 -0.039 0.002 0.917 -0.114 0.159 0.050 <0.001
	making. Sixth, external market knowledge creation has a positive relationship with B2B FIP4 Return on investment is higher in our firm -
	marketing rational decision making. Finally, B2B marketing rational decision making have a positive relationship with firm performance. The paths are unique and highlight that knowledge management using BDAI is important for B2B marketers to survive in this competitive environment. In this process, this study advances the theoretical debate surrounding AI application in B2B decision making. CUK2 Machine learning and predictive analytics tools can be useful to create profile of future customers and develop relationship AI-enabled knowledge about users may indicate how users modify some products Firm EMK3 0.160 0.016 -0.710 0.748 0.122 0.250 0.051 <0.001 Overall financial performance has FIP5 improved in our firm BMD1 0.413 -0.250 0.157 0.068 0.606 0.199 0.052 <0.001 USK6 themselves and such information can be helpful for new product developments Performance (FIP) Wamba et al. (2017) We have entered new markets more BMD2 -0.006 0.114 0.337 -0.368 0.674 0.323 0.051 <0.001 FIP6 quickly than our competitors BMD3 -0.205 -0.158 -0.008 -0.164 0.736 0.494 0.051 <0.001 strategy accordingly CUK3 BDAI can improve every process involved in B2B sales activity EMK1 We have introduced new products or -BDAI can be used to gather intelligence FIP7 services to the market faster than our BMD4 -0.112 0.290 -0.166 0.321 0.770 0.471 0.051 <0.001 about external market forces and competitors -stakeholders Our success rate of new products or BMD5 -0.012 -0.040 -0.236 0.101 0.797 0.426 0.051 <0.001
	artificial intelligence in the digital age require further investigation. Privacy is a big concern when using big data and artificial intelligence, particularly in B2B marketing. Customer fears concerning the sharing of personal data can impede the application of BDAI and prevent it from unlocking the value of big data. Research is needed on the development of bottom line research need more attention from future researchers. BDAI3 We apply advanced analytical techniques CUK7 and beat customers by improving own BMD1 organisational employees to make better -for decision making AI can be useful in convincing customers AI is believed to be able to help BDAI3 0.750 -0.152 0.490 -0.453 -0.148 0.076 0.051 <0.001 BDAI policies to safeguard against data misuse by B2B marketers. Finally, B2B and triple Appendix A: Operationalization of constructs Constructs Code Items Adapted from BDAI1 Our organisation has access to unstructured and structured data sets BDAI2 Our organisation amalgamates internal and Customer Knowledge Creation (CUK) Paschen et al. (2019) CUK4 AI can be used to identify prospects CUK5 AI can automate sales process and schedule meetings and further answer common questions CUK6 External Market Knowledge Creation (EMK) EMK2 BDAI enables external market knowledge FIP8 services has been higher than our FIP1 -0.086 0.001 -0.113 -0.024 0.067 0.773 0.051 <0.001 competitors FIP9 FIP2 -0.167 0.004 -0.090 -0.031 0.161 0.862 0.050 <0.001 Our market share has exceeded that of our FIP3 -0.194 0.032 -0.052 -0.013 0.177 0.923 0.050 <0.001 competitors Paschen et al. (2019) EMK3 FIP4 -0.157 0.056 -0.269 0.109 0.166 0.893 0.050 <0.001 AI systems using NLP and ML algorithms FIP5 -0.205 0.044 -0.080 0.020 0.187 0.929 0.049 <0.001 are more and more used to examine and FIP6 0.306 -0.076 -0.541 0.590 -0.033 0.616 0.052 <0.001 identify fake news content Appendix B: Combined loadings and cross loadings FIP7 0.328 -0.101 -0.178 0.450 -0.200 0.579 0.052 <0.001 AI presentation bots can assist B2B sales force in making mind blowing presentations EMK4 BDAI CUK USK EMK BMD FIP SE P value FIP8 0.304 -0.026 0.839 -0.582 -0.504 0.548 0.052 <0.001 AI can enable B2B marketers develop competitive intelligence BDAI1 0.605 -0.323 0.062 -0.113 -0.074 0.121 0.052 <0.001 FIP9 0.304 -0.008 0.878 -0.633 -0.461 0.574 0.052 <0.001 external data for value analysis business environment BDAI2 0.792 -0.254 0.301 -0.348 -0.031 0.079 0.051 <0.001 Source: WarpPLS output
	6. Conclusion This study is focused on the potential of BDAI to unlock value based on customer knowledge, user knowledge and external market knowledge. The theoretical model was grounded in KMT and involves BDAI (knowledge management enabler), customer knowledge creation, user knowledge creation and external market knowledge creation (knowledge management process), B2B marketing rational decisions (decision making style) and firm performance (organisation output) in line with the study by Abubakar et al. Big data powered Artificial intelligence (BDAI) Dubey et al. (2019a,b) value offerings decisions and improve creativity BDAI4 0.547 -0.067 -0.266 0.296 0.493 0.405 0.053 <0.001 BDAI4 We use computing techniques (e.g. Hadoop) for processing of large data sets BDAI5 We use data visualization methods to decode complex data BDAI6 Our management have approved budget for big data and artificial intelligence project BDAI7 We give BDAI training to our employees BDAI8 We appoint persons having long experience in handling BDAI BDAI9 We have collaborated with DTI and CUK8 AI can be useful in automatic order follow-ups and order processing User Knowledge Creation (USK) USK1 User knowledge is important for developing new products and enhancing creativity Paschen et al. (2019) USK2 AI can perform content analysis and provide insights about users' likings and disliking B2B BMD2 -AI based expert systems in a support role BDAI5 0.756 -0.148 -0.185 0.232 0.221 0.224 0.051 <0.001 can help users make good decisions BDAI7 0.764 -0.166 -0.156 0.149 -0.226 0.078 0.051 <0.001 Marketing Rational Decision Making (BMD) Daun et al. (2019) BMD3 This insight gained through AI based BDAI8 0.790 -0.006 0.002 0.137 -0.330 0.245 0.051 <0.001 knowledge creation can be useful in BDAI9 0.728 0.064 -0.076 0.289 -0.192 0.082 0.051 <0.001 understanding firm's current position -against competitors BMD4 BDAI10 0.522 0.492 -0.697 0.586 0.749 0.485 0.053 <0.001 This insight gained through AI based CUK1 -0.115 0.733 0.224 -0.112 -0.088 0.318 0.051 <0.001 knowledge creation can caution marketers -to remain alert about their brands and CUK2 -0.032 0.834 -0.343 0.200 0.091 0.172 0.050 <0.001 identify fake news that can cause harm to CUK3 -0.124 0.736 0.222 -0.115 -0.090 0.305 0.051 <0.001 the brand -Universities for implementing BDAI CUK4 -0.023 0.826 -0.378 0.235 0.079 0.176 0.050 <0.001 projects CUK5 -0.084 0.578 0.393 -0.481 -0.181 -0.052 <0.001

(2019)

. This research work highlights the requirements of and creates a model for knowledge management and decision making related to B2B marketing in this digital age.