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This paper aims to build on the latest advances in cognitive computing techniques to systematically illustrate how unstructured data from users can offer significant value for co-innovation.

The paper adopts a general overview approach to understand how unstructured data from users can be analyzed with cognitive computing techniques for innovation. The paper links the computerized techniques with marketing innovation problems with an integrated framework using dynamic capabilities and complexity theory.

The paper identifies a suite of methodologies for facilitating company co-innovation via engaging with customers and external data with cognitive computing technologies. It helps to expand marketing researchers and practitioners’ understanding of using unstructured data.

This paper provides a conceptual framework that divides co-innovation process into three stages, ideas generation, ideas integration and ideas evaluation, and maps cognitive computing methodologies and technologies to each stage. This paper makes the theoretical contributions by developing propositions from both customer and firm perspectives.

This paper can be used for companies to engage consumers and external data for co-innovation activities by strategically select appropriate cognitive computing techniques to analyze unstructured data for better insights.

Given the lack of systematic discussion regarding what is possible from using cognitive computing to analyze unstructured data for co-innovation. This paper makes first attempt to summarize how unstructured data can be analyzed with cognitive computing techniques. This paper also integrates complexity theory to the framework from a novel perspective.

The authors show how cognitive computing offers companies an opportunity to dramatically improve the efficiency of business functions throughout the enterprise – from core back office systems to critical middle office capabilities to essential front office functions.

Examples are given of companies that are using cognitive computing to transform the workings of individual business functions.

Cognitive systems will also create breakthrough opportunities for interactions between various functions of the organization.

Self–learning cognitive systems are enabling better-informed customer engagement in which the technology recognizes, learns and improves with every interaction.

Applied to innovation activities, cognitive computing helps organizations better formulate hypotheses, identify and validate new ideas, accelerate and refine scenario envisioning and planning. As organizations become more mature in both digital intelligence and digital re-invention, the dynamic interplay between functions will increasingly become a source of competitive advantage.

Cognitive computing is part of AI and cognitive applications consists of cognitive services, which are building blocks of the cognitive systems. These applications mimic the human brain functions, for example, recognize the speaker, sense the tone of the text. On this paper, we present the similarities of these with human cognitive functions. We establish a framework which gathers cognitive functions into nine intentional processes from the substructures of the human brain. The framework, underpins human cognitive functions, and categorizes cognitive computing functions into the functional hierarchy, through which we present the functional similarities between cognitive service and human cognitive functions to illustrate what kind of functions are cognitive in the computing. The results from the comparison of the functional hierarchy of cognitive functions are consistent with cognitive computing literature. Thus, the functional hierarchy allows us to find the type of cognition and reach the comparability between the applications.

The authors address how a combination of artificial intelligence (AI) and cognitive computing --- adaptive data management systems that monitor, analyze, make decisions and learn -- will transform businesses, work and customer offerings.

A survey of 6,050 C-suite executives worldwide identified a small group of cognitive innovators and revealed what they are doing differently.

Cognitive innovators identify customer satisfaction, retention, acquisition and revenue growth as the primary rationale for embracing cognitive technologies.

Cognitive computing systems are already helping make sense of the deluge of data spawned by ordinary commerce because they are able to adapt and learn.

The authors offer a four-step approach to cognitive computing innovation based on their research findings.

This vision article alerts service managers to the potential of cognitive computing to reframe their value propositions. Humans are bounded in three ways: perception, rationality and physicality. Cognitive computing, hardware or software that transcends these three limits, offers many opportunities to improve the performance of service systems, in particular those focused on customer engagement. The intangibility spectrum is presented as a mental model for service managers to consider how to use cognitive computing to support augmenting their value proposition by moving across the spectrum.

Three frameworks are integrated: a five systems framework, a system's impact classification of types of cognitive computing and a tangibility spectrum.

Three examples illustrate the potential value of this integrative approach for service management.

This is the first integration of these frameworks, and two of them are the result of the first author's research.

The purpose of this paper was to develop a comprehensive best practices checklist that can be used by governing bodies to identify and evaluate an enterprise’s risk exposure around cognitive systems (CSs) and formulate mitigating internal controls that can address these risks.

COBIT 5 was scrutinised to identify the processes which are necessary for the effective governance of CSs. The applicable processes were used to identify significant risks relating to cognitive computing (CC), as well as to develop a best practices control checklist.

The research output developed was a best practices checklist and executive summary that would assist enterprises in evaluating their CC risk exposure and assess the adequacy of existing controls. The first checklist highlights the incremental risk exposure which needs to be addressed. To evaluate the effectiveness of the cognitive computing control structure, a best practices checklist was developed that can be used by internal auditors and risk and audit committees. An executive summary was developed to highlight the key focus areas that governing bodies need to consider.

The checklist provides a tool to assess the enterprises’ risk exposure, evaluate the existing CC control mechanisms and identify areas that require management attention.

The checklists and executive summary developed provides enterprises with a comprehensive checklist that can be used, while at the same time allowing them to discharge their responsibility in terms of King IV.

The best possible way for brick-and-mortar retailers to maximise engagement with personalised shoppers is capitalising on intelligent insights. The retailer operates differently with diversified items and services, but influencing retail atmospheric on personalised shoppers, the perception remains the same across industries. Retail atmospherics stimuli such as design, smell and others create behavioural modifications. The purpose of this study is to explore the atmospheric effects on brick-and-mortar store performance and personalised shopper's behaviour using cognitive computing based in-store analytics in the context of emerging market.

The data are collected from 35 shoppers of a brick-and-mortar retailer through questionnaire survey and analysed using quantitative method.

The result of the analysis reveals month-on-month growth in footfall count (46%), conversation rate (21%), units per transaction (27%), average order value (23%), dwell time (11%), purchase intention (29%), emotional experience (40%) and a month-on-month decline in remorse (20%). The retailers need to focus on three control gates of shopper behaviour: entry, browsing and exit. Attention should be paid to the cognitive computing solution to judge the influence of retail atmospherics on store performance and behaviour of personalised shoppers. Retail atmospherics create the right experience for individual shoppers and forceful use of it has an adverse impact.

The paper focuses on strategic decisions of retailers, the tactical value of personalised shoppers and empirically identifies the retail atmospherics effect on brick-and-mortar store performance and personalised shopper behaviour.

Novel and emerging technologies such as cognitive analytics attract a lot of hype among academic researchers and practitioners. However, returns on investments in these technologies are often poor. So, identifying mechanisms through which cognitive analytics can add value to firms is a critical research gap. The purpose of this paper is to theorize how cognitive analytics technologies can enable the dynamic capabilities of sensing, seizing and reconfiguring for an organization.

This conceptual paper draws on the extant academic literature on cognitive analytics and related technologies, the business value of analytics and artificial intelligence and the dynamic capabilities perspective, to establish the role of cognitive analytics technologies in enabling the sensing, seizing and reconfiguring capabilities of an organization.

Through arguments grounded in existing conceptual and empirical academic literature, this paper develops propositions and a theoretical framework linking cognitive analytics technologies with organizations’ dynamic capabilities (sensing, seizing and reconfiguring).

This paper has critical implications for both academic research and managerial practice. First, the authors develop a framework using the dynamic capabilities theoretical perspective to establish a novel pathway for the business value of cognitive analytics technology. Second, cognitive analytics is proposed as a novel antecedent of the dynamic organizational capabilities of sensing, seizing and reconfiguring.

To the best of the authors’ knowledge, this is the first paper to theorize how cognitive analytics technologies can enable dynamic organizational capabilities, and thus add business value to an organization.

Artificial Intelligence (AI) has surpassed expectations in opening up different possibilities for machines from different walks of life. Cloud service providers are pushing. Edge computing reduces latency, improving availability and saving bandwidth.

The exponential growth in tensor processing unit (TPU) and graphics processing unit (GPU) combined with different types of sensors has enabled the pairing of medical technology with deep learning in providing the best patient care. A significant role of pushing and pulling data from the cloud, big data comes into play as velocity, veracity and volume of data with IoT assisting doctors in predicting the abnormalities and providing customized treatment based on the patient electronic health record (EHR).

The primary focus of edge computing is decentralizing and bringing intelligent IoT devices to provide real-time computing at the point of presence (PoP). The impact of the PoP in healthcare gains importance as wearable devices and mobile apps are entrusted with real-time monitoring and diagnosis of patients. The impact edge computing of the PoP in healthcare gains significance as wearable devices and mobile apps are entrusted with real-time monitoring and diagnosis of patients.

The utility value of sensors data improves through the Laplacian mechanism of preserved PII response to each query from the ODL. The scalability is at 50% with respect to the sensitivity and preservation of the PII values in the local ODL.

The purpose of this paper is to elucidate the emerging landscape of procurement analytics. This paper focuses on the following questions: what are the current and future state of procurement analytics?; what changes in the procurement process will be required to enable integration of analytical solutions?; and what future areas of research arise when considering the future state of procurement analytics?

This paper employs a qualitative approach that relies on three sources of information: executive interviews, a review of current and emerging technology platforms and a small survey of subject matter experts in the field.

The procurement analytics landscape developed in this research suggests that the authors will continue to see major shifts in the sourcing and supply chain technology environment in the next five years. However, there currently exists a low usage of advanced procurement analytics, and data integrity and quality issues are preventing significant advances in analytics. This study identifies the need for organizations to establish a coherent approach to collection and storage of trusted organizational data that build on internal sources of spend analysis and contract databases. In addition, current ad hoc approaches to capturing unstructured data must be replaced by a systematic data governance strategy. An important element for organizations in this evolution is managing change and the need to nourish an analytic culture.

While the majority of forward-looking research and reports merely project broad technological impact of cognitive analytics and big data, much of it does not provide specific insights into functional impacts such as the impact on procurement. The analysis of this study provides us with a clear view of the potential for business analytics and cognitive analytics to be employed in procurement processes, and contributes to development of related research topics for future study. In addition, this study suggests detailed implementation strategies of emerging procurement technologies, contributing to the existing body of the literature and industry reports.