Improving marketing success: The role of tacit knowledge exchange between sales and marketing

Successful organizations adapt their marketing strategies to marketplace changes. Boundary spanners, such as salespeople, because they are able to embed themselves in social networks outside the organization, play a key role in developing marketplace knowledge. However, if this knowledge remains solely with the boundary spanners, it cannot be used effectively to improve firm performance. This study investigates tacit knowledge exchange between sales and marketing and its ability to enhance marketing success (i.e., marketing program innovativeness, relative efficiency, and relative effectiveness). In addition, by examining five antecedents hypothesized to influence tacit knowledge exchange, it provides guidance to sales and marketing managers, who desire to improve tacit knowledge exchange, and, in turn, marketing success.     

Affective Computing in Marketing: Practical Implications and Research Opportunities Afforded by Emotionally Intelligent Machines

After years of using AI to perform cognitive tasks, marketing practitioners can now use it to perform tasks that require emotional intelligence. This advancement is made possible by the rise of affective computing, which develops AI and machines capable of detecting and responding to human emotions. From market research, to customer service, to product innovation, the practice of marketing will likely be transformed by the rise of affective computing, as preliminary evidence from the field suggests. In this Idea Corner, we discuss this transformation and identify the research opportunities that it offers.

     

From data to action: How marketers can leverage AI

Artificial intelligence (AI) is at the forefront of a revolution in business and society. AI affords companies a host of ways to better understand, predict, and engage customers. Within marketing, AI’s adoption is increasing year-on-year and in varied contexts, from providing service assistance during customer interactions to assisting in the identification of optimal promotions. But just as questions about AI remain with regard to job automation, ethics, and corporate responsibility, the marketing domain faces its own concerns about AI. With this article, we seek to consolidate the growing body of knowledge about AI in marketing. We explain how AI can enhance the marketing function across nine stages of the marketing planning process. We also provide examples of current applications of AI in marketing.     

Supporting Technologies and Organizational Practices for the Transfer of Knowledge in Virtual Environments

A field study of a global science/technology company provides evidence of the value of both organizational practices and technology tools for supporting knowledge attainment (the combined tacit and explicit knowledge gained in a focal area – in this study we focus on product knowledge attainment) in virtual environments. We present a three-dimensional typology of knowledge management systems. Method of input, form of content, and how the users accrue the benefit of the knowledge help us to argue that organizational practices and technological tools will have independent positive effects on user knowledge attainment. We find attendance at face-to-face community of practice meetings, use of searchable archives, video-on-demand, and full-text search of video-on-demand all positively predict knowledge attainment. We suggest that organizations develop both organizational practices and technical supports for knowledge transfer. An interview with the video-on-demand vendor gives us the context to discuss issues for the support of tacit knowledge in more virtual environments as well as issues of expertise as it relates to support for formal and informal learning.     

Challenges and Opportunities for Marketing Scholars in Times of the Fourth Industrial Revolution

Artificial Intelligence (AI) and 5G connectivity have been identified as drivers of the so-called Fourth Industrial Revolution (FIR). AI and 5G, through emerging technologies such as blockchain, gene editing, Internet of Things sensors, nanotechnology, or 3D printing accelerate a blurring of boundaries between digital, biological, and physical spheres. In this editorial, we introduce the term boundary object, or boundary technology, that can help process more information (syntactic boundary) for enhanced learning (semantic boundary) and that can create a higher-level intelligence (pragmatic boundary). Boundary objects are also a means of representing, learning about, and transforming knowledge at a given boundary. We propose that crossing syntactic, semantic, and pragmatic boundaries is facilitated by three FIR phenomena (big data, machine learning, and AI). Each of these phenomena possesses a unique capability (processing, learning, and adaptation) to help communities learn about their differences and dependences. We also show how the six articles in this special issue are related to boundary and sphere challenges, and we provide an overview of directions for future research. All in all, marketing scholars should focus on enhancing their abilities in knowledge integration across boundaries to sustain their role as cutting-edge scientists.     

Machines augmenting entrepreneurs: Opportunities (and threats) at the Nexus of artificial intelligence and entrepreneurship

Artificial intelligence (AI) refers to machines that are trained to perform tasks associated with human intelligence, interpret external data, learn from that external data, and use that learning to flexibly adapt to tasks to achieve specific outcomes. This paper briefly explains AI and looks into the future to highlight some of AI's broader and longer-term societal implications. We propose that AI can be combined with entrepreneurship to represent a super tool. Scholars can research the nexus of AI and entrepreneurship to explore the possibilities of this potential AI-entrepreneurship super tool and hopefully direct its use to productive processes and outcomes. We focus on specific entrepreneurship topics that benefit from AI's augmentation potential and acknowledge implications for entrepreneurship's dark side. We hope this paper stimulates future research at the AI-entrepreneurship nexus.Executive summaryArtificial intelligence (AI) refers to machines that are trained to perform tasks associated with human intelligence, interpret external data, learn from that external data, and use that learning to flexibly adapt to tasks to achieve specific outcomes. Machine learning is the most common form of AI and largely relies on supervised learning—when the machine (i.e., AI) is trained with labels applied by humans. Deep learning and adversarial learning involve training on unlabeled data, or when the machine (via its algorithms) clusters data to reveal underlying patterns.AI is simply a tool. Entrepreneurship is also simply a tool. How they are combined and used will determine their impact on humanity. While researchers have independently developed a greater understanding of entrepreneurship and AI, these two streams of research have primarily run in parallel. To indicate the scope of current and future AI, we provide examples of AI (at different levels of development) for four sectors—customer service, financial, healthcare, and tertiary education. Indeed, experts from industry research and consulting firms suggest many AI-related business opportunities for entrepreneurs to pursue.Further, we elaborate on several of these opportunities, including opportunities to (1) capitalize on the “feeling economy,” (2) redistribute occupational skills in the economy, (3) develop and use new governance mechanisms, (4) keep humans in the loop (i.e., humans as part of the decision making process), (5) expand the role of humans in developing AI systems, and (6) expand the purposes of AI as a tool. After discussing the range of business opportunities that experts suggest will prevail in the economy with AI, we discuss how entrepreneurs can use AI as a tool to help them increase their chances of entrepreneurial success. We focus on four up-and-coming areas for entrepreneurship research: a more interaction-based perspective of (potential) entrepreneurial opportunities, a more activities-based micro-foundation approach to entrepreneurial action, a more cognitively hot perspective of entrepreneurial decision making and action, and a more compassionate and prosocial role of entrepreneurial action. As we discuss each topic, we also suggest opportunities to design an AI system (i.e., entrepreneurs as potential AI designers) to help entrepreneurs (i.e., entrepreneurs as AI users).AI is an exciting development in the technology world. How it transforms markets and societies depends in large part on entrepreneurs. Entrepreneurs can use AI to augment their decisions and actions in pursuing potential opportunities for productive gains. Thus, we discuss entrepreneurs' most critical tasks in developing and managing AI and explore some of the dark-side aspects of AI. Scholars also have a role to play in how entrepreneurs use AI, but this role requires the hard work of theory building, theory elaboration, theory testing, and empirical theorizing. We offer some AI topics that we hope future entrepreneurship research will explore. We hope this paper encourages scholars to consider research at the nexus of AI and entrepreneurship.     

Knowledge Management in Central and Eastern Europe Through Network Development and Boundary Spanners

ABSTRACT

Central and Eastern European (CEE) countries have become a popular research venue due to their transitioning into market based economies. For international business scholars this represents a unique opportunity, as one of the key driving forces is the privatization of state-owned enterprises. Due to the market's great potential, many multinational enterprises are entering CEE countries and realizing that a transfer of market knowledge (i.e., customer tastes, distribution methods/channels, etc.) from the host country to the multinational, as well as from the multinational to the host country (i.e. technology, management/marketing capabilities, etc.), is essential.

The lack of host country skills have made knowledge transfer difficult. Also, the tacit nature of some of the most important knowledge embedded within the host country's organization and the multinational makes knowledge diffusion a daunting task to manage. However, successful knowledge management will assist in positive performance measures so researchers are examining knowledge management and its constructs (acquisition, transfer, conversion, and application).

Our research explores the linkage between knowledge management and performance in host country firms in a CEE country, Croatia. We also explored antecedents of successful knowledge management that of networks and human resources (boundary spanners). Finally we explored whether cultural distance moderates these relationships in the knowledge management process.     

Market-Based Organisational Learning - Theoretical Reflections and Conceptual Insights

As an area of academic inquiry, organisational learning has matured to such an extent that it can now be described as an institutionalisation of a body of knowledge. Nonetheless, underlying the apparent maturity of this literature lies the proliferation of disciplines that claim aspects of this theoretical territory, each with their own ontological premises and schools of thought resulting in frequently conflicting theories which often reflect a diverse pattern of phenomenological domains, interpretative issues, methodological predilections and conflicting operationalisations. The marketing literature is characteristic in detecting elements in cognate disciplines and introducing them within the guise of mainstream marketing thought. Consequently, it was a matter of time before marketing academicians became interested in exploiting seams of organisational learning research that are relevant to areas of marketing. Consequently, a recent announcement by the Marketing Science Institute finds that market-based organisational learning is now a 'second tier' research priority for 2002-2004, reflecting its developing attraction in organisational, conceptual, theoretical and empirical respects. In this paper, an attempt will be made to address the following: to delineate the organisational learning concept by reviewing multi-disciplinary contributions; to evaluate the intellectual roots to market-based organisational learning by considering its theoretical heritage; to propose a model of the theoretical properties of market-based organisational learning; to consider the extent to which market-based organisational theory satisfies criteria for theory construction in marketing and organisation science; to identify critical gaps in our knowledge of market-based organisational learning; and, to indicate the contextual issues surrounding the development of market-based organisational learning programs in firms.     

Knowledge transfer between marketing functions in multinational companies: a conceptual model

A key competitive advantage of multinational companies lies in their ability to exploit locally created knowledge worldwide. This implies that such companies have to be able to transfer knowledge within organizational networks characterized by separation through time, space, culture and language. Given the pivotal importance of knowledge transfer for the competitiveness of multinationals, it is remarkable that the process of transferring knowledge effectively across dispersed units of multinational corporations has only attracted little and rather fragmented research interest. What appears to be missing is a unifying framework that serves as a basis for a research agenda. Our paper aims to develop such a framework. Specifically, we propose a conceptual model of knowledge transfer between marketing functions within multinationals and advance research propositions for future empirical testing.     

Decision augmentation and automation with artificial intelligence: Threat or opportunity for managers?

Artificial intelligence (AI) has emerged as a promising and increasingly available technology for managerial decision-making. With the adoption of AI-enabled software, organizations can leverage various benefits of the technology, but they also have to consider the intended and unintended consequences of using the technology for managerial roles. It is still unclear whether managers will benefit from enhancing their abilities with AI-enabled software or become powerless puppets that do more than announce AI-enabled software results. Our research has revealed distinct ways in which organizations can use AI-enabled decision-making solutions: as tools or novelties, for decision augmentation or automation, and as either a voluntary or a mandatory option. In this article, we discuss the implications of each of these combinations on the relevant managers. We consider outcomes related to managerial job design and derive practical advice for organizational designers and managers who work with AI. Our outcomes provide guidance on how to deal with the conflict-riddled relationship between managers and technology with regard to capabilities, responsibilities, and acceptance of AI-enabled software.     

The role of top management networks for market knowledge creation and sharing in China

Despite the paramount influence of top managers within a firm, extant literature has under-researched the specific role of top management in market knowledge development. Drawing on social networks theory, the authors conceptualize a linkage between top management networks (TMNs) and market knowledge management. Based on a sample of Chinese firms, the results show that networking embeddedness of TMNs promotes new knowledge creation, but too much networking embeddedness leads to diminished returns. In addition, networking disparity of TMNs has a negative influence on existing knowledge sharing, and technological turbulence seems to weaken this negative impact. The findings have some important implications for marketing research and for managers on how to obtain superior learning in Chinese Economy.     

Knowledge transfer between self-initiated expatriates and their organizations: Research propositions for managing SIEs

Knowledge management is increasingly recognized as a key aspect of international business and management. This paper offers research avenues for investigating knowledge transfer between self-initiated expatriates (SIEs) and their employer organizations. Building on the existing literature on expatriation and knowledge transfer, it offers a series of research propositions. It illustrates the knowledge transfer process in three phases: opportunity seeking, knowledge sharing, and knowledge application. We suggest that different characteristics of SIEs and their organizations can influence the knowledge transfer process. Furthermore, we advance that SIEs may be a source of sustained competitive advantage to their companies. Several implications for future empirical research are also discussed and the paper concludes with practical implications for SIE talent management.     

Examining artificial intelligence (AI) technologies in marketing via a global lens: Current trends and future research opportunities

Artificial intelligence (AI) has captured substantial interest from a wide array of marketing scholars in recent years. Our research contributes to this emerging domain by examining AI technologies in marketing via a global lens. Specifically, our lens focuses on three levels of analysis: country, company, and consumer. Our country-level analysis emphasizes the heterogeneity in economic inequality across countries due to the considerable economic resources necessary for AI adoption. Our company-level analysis focuses on glocalization because while the hardware that underlies these technologies may be global in nature, their application necessitates adaptation to local cultures. Our consumer-level analysis examines consumer ethics and privacy concerns, as AI technologies often collect, store and process a cornucopia of personal data across our globe. Through the prism of these three lenses, we focus on two important dimensions of AI technologies in marketing: (1) human–machine interaction and (2) automated analysis of text, audio, images, and video. We then explore the interaction between these two key dimensions of AI across our three-part global lens to develop a set of research questions for future marketing scholarship in this increasingly important domain.     

Understanding Programmatic Creative: The Role of AI

In this article, we expand the concept of programmatic advertising to include programmatic creative as a vital component. While artificial intelligence (AI) has already automated the media buying process, the advertising creative process still requires extensive human efforts. Such discrepancy calls for AI to transform the advertising creative process. We provide a framework for understanding and investigating programmatic creative by drawing evidence from the advertising industry in China. We specifically discuss how big data and machine learning algorithms underpin programmatic advertising. We argue that AI will integrate programmatic buying and programmatic creative in the future. We also discuss the technological, regulatory, and legal challenges faced by programmatic creative. We argue that new theories and methods are needed to conduct research in this area and provide guidance for the advertising industry.     

Environmental context and marketing planning processes

The traditional model of marketing planning assumes that the process by which organizations take planning decisions is constant with respect to context. Organizational environment, internal context, performance and distinctive competency affect marketing planning only as information inputs for the planning process, not as influences on the choice of process. However, rapid change in environments, in competitive patterns and in market responses all demand action from organizations. Intuitively, we expect that rapid environmental change will require a faster planning process, and that increasing environmental complexity requires more careful deliberation. The marketing planning model does not accommodate such intuition. This paper outlines the case for rethinking marketing planning models, and develops a conceptual framework through which the impact of environmental context on marketing planning processes can be effectively examined.     

Inter-firm learning and knowledge-sharing in multinational networks: An outsourced organization's perspective

This research provides insights into how learning and knowledge are exchanged multinationally between customer firms and their outsourced suppliers who provide non-core, yet essential, services. The paper seeks to understand: (1) how information is exchanged in these networks; (2) how cumulative knowledge adds value in these networks; and (3) how boundary-spanners assist in the dissemination of knowledge and learning within the network. Based on a pharmaceutical industry case, the results suggest that: (a) multinational firms operate more effectively and interact by sharing knowledge with outsourced firms which reflect the customers' structure and fit; (b) networked firms benefit from interactions through economies of scope, but knowledge is not necessarily shared equally among partners; (c) learning and knowledge-sharing interactions are tightly coupled at the product development stage; and (d) outsourced firms interact with external boundary-spanners as needed. The research provides insights for managers of multinational organizations and managers of firms from where essential services are outsourced.     

A Conceptual Model for Virtual Organizational Learning

As a trend toward virtual organizations (VOs) emerges in the era of electronic commerce, an understanding of knowledge processing is essential to sustain performance. Active learning is the key to achieving flexibility in a VO; however, few studies have focused on this dimension. In this article we attempt to assemble insights about learning in a VO by developing a conceptual model for virtual organizational learning (VOL). The VOL model is focused on transactive memory, which is composed of knowledge maps, social networks, and mnemonic functions. The initialization of modeling learning in VOs in this article has been erecting the foundation for transactive memory systems in a cyber community. A case study, Smart Creative Teachers Network, is used for demonstrating the transactive memory system for a teacher community to share and create knowledge and form VOs across school boundaries.     

Organisational context and behavioural problems as determinants of the effectiveness of the strategic marketing planning process

This paper continues the authors’ recent work on organisational behaviour by arguing that the differences between theory and parctice in marketing planning can be better understood by analysing the corporate environment for marketing management. Thus, variables such as organisational structure, the configuration of corporate decision making processes, patterns of managerial behaviour and the impact of corporate culture, can help us to understand how marketing planning processes operate in practice.

The authors contention is that the real problems associated with the strategic marketing planning process should be grounded in the realities perceived by executives. They go on to discuss a number of common pitfalls in the planning process before reporting on an exploratory empirical study of UK firms with over 100 employees. The implication of the results is seen as reselling the management agenda with regard to the process of marketing planning.     

Designing,developing, and deploying artificial intelligence systems: Lessons from and for the public sector

Artificial intelligence applications in cognitive computing systems can be found in organizations across every market, including chatbots that help customers navigate websites, predictive analytics systems used for fraud detection, and augmented decision-support systems for knowledge workers. In this article, we share reflections and insights from our experience with AI projects in the public sector that can add value to any organization. We organized our findings into four thematic domains—(1) data, (2) technology, (3) organizational, and (4) environmental—and examine them relative to the phases of AI. We conclude with best practices for capturing value with cognitive computing systems.