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Integrating artificial intelligence into supply chain management: promise, challenges, and guidelines
Abstract
This paper argues that AI can improve supply chain efficiency, and transform supply chain management by providing tools for managing the functions across all the stages of a supply chain. Despite the promise, organisational and managerial challenges may limit AI and SCM integration. We explore the challenges of AI use in SCM and offer some guidelines for its successful integration. We propose that organisations need to make an economic case for AI adoption, develop a plan for AI implementation, including developing core capabilities and system trust for coordinating behaviour across the stages of the supply chain. As well, organisations need to manage the nexus of people and technology to reduce human-machine conflict, as the goal is for AI to augment human capabilities, not to replace them. We provide the implications of the paper for theory and practice.
Keywords: artificial intelligence; supply chain management; stages of supply chains; emerging technology; promise; framing; challenges; guidelines.
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Purpose
Procurement is a crucial part of supply chain management, consistently becoming a strategic vantage point in global competition. The industry 4.0 paradigm is transforming supply chains to smarter systems, giving rise to the concept of procurement 4.0. A systematic framework to transform in current scenario is crucial.
Design/methodology/approach
This study brings together these current researches to propose a redesigned procurement process by combining several technologies. A BPR approach is taken to present the new process and its merits are discussed.
Findings
A re-designed procurement framework is proposed. Radical improvements of cost, cycle time, human effort, degree of automation, traceability, information availability and uncertainty are achievable with the proposed framework.
Practical implications
The proposed re-engineered process addresses the visualization barrier for managers. The proposed framework is grounded on BPR which provides a generic ground for developing redesign exercise along with the visualization of new process.
Originality/value
There is literature discussing implementation, impact and advantages of individual and combination of technologies on procurement process but lacks visualization of the transformed process combining these technologies.
This paper seeks to identify the contributions of artificial intelligence (AI) to supply chain management (SCM) through a systematic review of the existing literature. To address the current scientific gap of AI in SCM, this study aimed to determine the current and potential AI techniques that can enhance both the study and practice of SCM. Gaps in the literature that need to be addressed through scientific research were also identified. More specifically, the following four aspects were covered: (1) the most prevalent AI techniques in SCM; (2) the potential AI techniques for employment in SCM; (3) the current AI-improved SCM subfields; and (4) the subfields that have high potential to be enhanced by AI. A specific set of inclusion and exclusion criteria are used to identify and examine papers from four SCM fields: logistics, marketing, supply chain and production. This paper provides insights through systematic analysis and synthesis.
Real engines of the artificial intelligence (AI) revolution, machine learning (ML) models, and algorithms are embedded nowadays in many services and products around us. As a society, we argue it is now necessary to transition into a phronetic paradigm focused on the ethical dilemmas stemming from the conception and application of AIs to define actionable recommendations as well as normative solutions. However, both academic research and society-driven initiatives are still quite far from clearly defining a solid program of study and intervention. In this contribution, we will focus on selected ethical investigations around AI by proposing an incremental model of trust that can be applied to both human-human and human-AI interactions. Starting with a quick overview of the existing accounts of trust, with special attention to Taddeo’s concept of “e-trust,” we will discuss all the components of the proposed model and the reasons to trust in human-AI interactions in an example of relevance for business organizations. We end this contribution with an analysis of the epistemic and pragmatic reasons of trust in human-AI interactions and with a discussion of kinds of normativity in trustworthiness of AIs.
In today's business environment, reusing the products could result in a great deal of supply chains cost reductions. Additionally, environmental regulations and customers' increasing expectations leave companies no choice but applying the return policy. In this regard, designing a supply chain network incorporating the reverse flow can provide environmental requirements in addition to economic objectives. In this paper, a new mixed integer linear programming (MILP) model is developed for a closed-loop supply chain network (CLSCN) design. The proposed model is multi-period and multi-product that covers facility location and routing of heterogeneous vehicle fleets, simultaneously. The efficiency of the model is validated based on a real case in production and distribution chain of automotive spare parts manufacturing company in Iran. Managerial insights are gained through the numerical results.
Purpose
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?
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
Purpose
This paper aims to investigate the way in which blockchain technology is likely to influence future supply chain practices and policies.
Design/methodology/approach
A systematic review of both academic and practitioner literature was conducted. Multiple accounts of blockchain adoption within industry were also consulted to gain further insight.
Findings
While blockchain technologies remain in their infancy, they are gaining momentum within supply chains, trust being the predominant factor driving their adoption. The value of such technologies for supply chain management lies in four areas: extended visibility and traceability, supply chain digitalisation and disintermediation, improved data security and smart contracts. Several challenges and gaps in understanding and opportunities for further research are identified by this research. How a blockchain-enabled supply chain should be configured has also been explored from a design perspective.
Research limitations/implications
This systematic review focuses on the diffusion of blockchain technology within supply chains, and great care was taken in selecting search terms. However, the authors acknowledge that their choice of terms may have excluded certain blockchain articles from this review.
Practical implications
This paper offers valuable insight for supply chain practitioners into how blockchain technology has the potential to disrupt existing supply chain provisions as well as a number of challenges to its successful diffusion.
Social implications
The paper debates the poential social and economic impact brought by blockchain.
Originality/value
This paper is one of the first studies to examine the current state of blockchain diffusion within supply chains. It lays a firm foundation for future research.
Purpose – The purpose of this study is to provide insights into the way in which understanding and implementation of disruptive technology,
specifically big data analytics and the Internet of Things (IoT), have changed over time. The study also examines the ways in which research in
supply chain and related fields differ when responding to and managing disruptive change.
Design/methodology/approach – This study follows a four-step systematic review process, consisting of literature collection, descriptive analysis, category selection and material evaluation. For the last stage of evaluating relevant issues and trends in the literature, the latent semantic analysis method was adopted using Leximancer, which allows more rapid, reliable and consistent content analysis.
Findings – The empirical analysis identified key research trends in big data analytics and IoT divided over two time-periods, in which research demonstrated steady growth by 2015 and the rapid growth was shown afterwards. The key finding of this review is that the main interest in recent big data is toward overlapping customer service, support and supply chain network, systems and performance. Major research themes in IoT moved
from general supply chain and business information management to more specific context including supply chain design, model and performance.
Originality/value – In addition to providing more awareness of this research approach, the authors seek to identify important trends in disruptive technologies research over time.
Purpose. This paper proposes an artificial intelligence-based framework to support decision making in wholesale distribution, with the aim to limit wholesaler out-of-stocks by jointly formulating price policies and forecasting retailer’s demand.
Design/methodology/approach. The framework is based on the cascade implementation of two artificial neural networks (ANNs) connected in series. The first ANN is used to derive the selling price of the products offered by the wholesaler. This represents one of the inputs of the second ANN that is used to anticipate the retailer’s demand. Both the ANNs make use of several other input parameters and are trained and tested on a real wholesale supply chain.
Findings. The application of the ANN framework to a real wholesale supply chain shows that the proposed methodology has the potential to decrease economic loss due to out-of-stock occurrence by more than 56%.
Originality. The combined use of ANNs is a novelty in supply chain operation management. Moreover, this approach provides wholesalers with an effective tool to issue purchase orders according to more dependable demand forecasts.
Artificial intelligence (AI)—defined as a system's ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation—is a topic in nearly every boardroom and at many dinner tables. Yet, despite this prominence, AI is still a surprisingly fuzzy concept and a lot of questions surrounding it are still open. In this article, we analyze how AI is different from related concepts, such as the Internet of Things and big data, and suggest that AI is not one monolithic term but instead needs to be seen in a more nuanced way. This can either be achieved by looking at AI through the lens of evolutionary stages (artificial narrow intelligence, artificial general intelligence, and artificial super intelligence) or by focusing on different types of AI systems (analytical AI, human-inspired AI, and humanized AI). Based on this classification, we show the potential and risk of AI using a series of case studies regarding universities, corporations, and governments. Finally, we present a framework that helps organizations think about the internal and external implications of AI, which we label the Three C Model of Confidence, Change, and Control.
Supply Chain Risk Management (SCRM) encompasses a wide variety of strategies aiming to identify, assess, mitigate and monitor unexpected events or conditions which might have an impact, mostly adverse, on any part of a supply chain. SCRM strategies often depend on rapid and adaptive decision making based on potentially large, multidimensional data sources. These characteristics make SCRM a suitable application area for Artificial Intelligence (AI) techniques. The aim of this paper is to provide a comprehensive review of supply chain literature that addresses problems relevant to SCRM using approaches that fall within the AI spectrum. To that end, an investigation is conducted on the various definitions and classifications of supply chain risk and related notions such as uncertainty. Then, a mapping study is performed to categorise existing literature according to the AI methodology used, ranging from mathematical programming to Machine Learning and Big Data Analytics, and the specific SCRM task they address (identification, assessment or response). Finally, a comprehensive analysis of each category is provided to identify missing aspects and unexplored areas and propose directions for future research at the confluence of SCRM and AI.
Purpose
While digitisation is a key driver of the fourth industrial revolution (Industry 4.0); organisations have different approaches to deal with this topic to get a clearer picture of the opportunities and challenges concerning the digital transformation. The purpose of this paper is to identify the impact of digitisation on procurement and its role within the area of supply chain management. The research will also explore potential barriers to digitising procurement and supply chains and ways to overcome them. Finally, the significance of potential enabling technologies to the digitisation will also be examined.
Design/methodology/approach
A quantitative approached utilising an online survey was used to collect the primary data for this study. Data were collected from 414 participants directly involved with procurement or related business functions and work for different organisations in different industries. The survey included eight items about the impact of digitisation on organisational performance in the area of procurement and supply chains; ten items related to key barriers to digitisation of organisations and ways to overcome them; and seven items about enabling technologies to leverage procurement procedures and processes digitisation. All of these items utilised the Likert five-point level of agreement scale.
Findings
The findings indicate that digitisation of procurement process can yield several benefits including: supporting daily business and administrative tasks, supporting complex decision-making processes, procurement will become more focussed on strategic decisions and activities, procurement will become a strategic interface to support organisational efficiency, effectiveness, and profitability, and supporting the creation of new business models, products, and services. The authors were also able to confirm that there are barriers to digitising procurement process and supply chains and such barriers found in existing procedures, processes, capacities, and capabilities. Finally, the significance of a number of enabling technologies to the digitisation process was revealed.
Originality/value
To the best of the authors’ knowledge, this is the first study of its kind with participants located world-wide. Industry 4.0 as a topic had been explored within different business areas and functions but very limited research specifically explored potential impact, barriers, and enabling technologies of procurement 4.0. The results can be beneficial for organisations already implemented Industry 4.0 or planning to do so. The study can also benefit academic scholars interested in the researched topic, business professionals, organisations within different sectors, and any other party interested in understanding more the concept of procurement 4.0.
Purpose
The purpose of this study is to analyze the cybersecurity assurance approaches to determine the key issues and weaknesses within the internal audit and risk management perspective. Organizations increasingly rely on digital data to drive their growth and they are interconnected in a complex web to a multitude of stakeholders.
Design/methodology/approach
In this paper, cybersecurity is defined, and cybersecurity assurance model is explained based on the relevant literature. In addition, the role of internal auditing is introduced within this new business landscape. Finally, recommendations are made to provide best practices for stakeholders.
Findings
There are four major cyber-focused standards and frameworks in the current literature, namely, Control Objectives for Information and Related Technology, International Organization for Standardization, The American Institute of Certified Public Accountants and National Institute of Standards and Technology. In addition, there are many mechanisms in existence and operation currently which support cybersecurity assurance to prevent major threats. These include risk assessment, risk treatment, risk management, security assurance and auditing.
Research limitations/implications
Cyber risk is not something that can be avoided; instead, it must be managed. Hence, it is very important to maintain formal documentation on related cyber controls. Internal audit should be an integral part of cybersecurity assurance process, as internal audit have a unique position to look across organizations. The contribution of internal audit also provides comfort to the Board and Audit Committee.
Practical implications
A model is introduced how the internal audit and information security functions could work together to support organizations accomplish a cost-effective level of information security. The key issues and approaches are explained for how to become a trusted cybersecurity advisor and a sample cybersecurity awareness program checklist is provided at Appendix 1.
Social implications
Considering cybersecurity threats grow with speed, complexity, and impact, organizations are no longer satisfied with an answer to a question like “are we secure?” instead, they need the answer for such a question like “how to give a reasonable assurance that our business will be secure enough?”. In that respect, the role of internal audit is discussed based on the relevant literature and the current condition of the business environment.
Originality/value
A model is introduced how the internal audit and information security functions could work together to support organizations accomplish a cost-effective level of information security. The key issues and approaches are explained for how to become a trusted cybersecurity advisor and a sample cybersecurity awareness program checklist is provided at Appendix 1.
Keywords:
Cybersecurity, Assurance, Internal auditing, Cyber risk, G3, D8, M14
Machine learning needs more rigor, scientists argue.
Purpose
This paper aims to study the antecedents of Internet of Things (IoT) adoption among farmers and determine how trust in the technology influences its adoption when mediated by perceived value and risk. Through the conceptualization of trust and perceived risk, the authors factor in farmers’ perceptions of agricultural technology providers and discuss different forms of perceived value, spanning economic, green and epistemic value.
Design/methodology/approach
This paper develops a distinctive research design, drawing on elements of the value-based adoption and technology acceptance models. By linking different elements of perceived value with IoT technology, the authors also apply the service-dominant logic to this study. They study how trust affects perceived value and risk and then determine how perceived value and risk, in turn, affect IoT adoption. The authors test the hypotheses by developing a structural equation model to analyze the results of a survey, wherein 492 farmers from Iowa, the USA, participated.
Findings
The results show a positive relationship between trust and perceived value and a negative relationship between trust and perceived risk. Perceived value had a positive impact on IoT adoption, whereas perceived risk had a negative impact on IoT adoption.
Practical implications
The research findings on trust and perceived value and risk are timely and relevant for business-to-business (B2B) marketing practitioners and agricultural stakeholders, especially in an era where farmers are expressing growing concerns about data handling risk posed by IoT technology adoption.
Originality/value
The research findings signal a transition in focus from the goods-dominant logic to the service-dominant logic in agriculture, whereby farmers are drawn to IoT technology because of perceived economic, green and epistemic value and as a result, can differentiate themselves on how well they deploy operant resources. This paper not only provides a unique conceptualization of perceived value but also pave the way for a richer conceptualization of IoT core functions that enable farmers to fulfill green and epistemic goals. This is the first B2B marketing paper discussing the antecedents of IoT adoption in agriculture, such as farmers’ perceptions of both monetary and non-monetary forms of value and perceived data handling risk.
In this article, we look at trust in artificial intelligence, machine learning (ML), and robotics. We first review the concept of trust in AI and examine how trust in AI may be different from trust in other technologies. We then discuss the differences between interpersonal trust and trust in technology and suggest factors that are crucial in building initial trust and developing continuous trust in artificial intelligence.
This report surveys the landscape of potential security threats from malicious uses of AI, and proposes ways to better forecast, prevent, and mitigate these threats. After analyzing the ways in which AI may influence the threat landscape in the digital, physical, and political domains, we make four high-level recommendations for AI researchers and other stakeholders. We also suggest several promising areas for further research that could expand the portfolio of defenses, or make attacks less effective or harder to execute. Finally, we discuss, but do not conclusively resolve, the long-term equilibrium of attackers and defenders.
Purpose
The purpose of this paper is to explore the role of trust, perceived organizational support and emotional attachment in bridging the gap between resistance and readiness to change
Design/methodology/approach
A conceptual model including five constructs is developed. The questionnaire survey using the study variables readiness to change, trust, perceived organizational support, emotional attachment and resistance to change was used in the present study. Descriptive statistics and mediation regression analysis is used to test all hypotheses using the survey data of 276 middle level managers.
Findings
The findings reflects, how readiness to change reduces the impact of resistance to change during organizational change. Furthermore, it also finds that how trust, perceived organizational support and emotional attachment mediates the relationship between resistance and readiness and reduces the gap between the two.
Research limitations/implications
The findings in the study have made significant contribution t the literature especially on middle level managers in Indian context. There was a paucity of research done on the study variables. The mediating effects of the study variable have never been explored earlier and therefore makes an immense contribution in the field of knowledge for practitioners and academicians.
Practical implications
The research results have many practical implications. It could be established that trust, perceived organizational support and emotional attachment has a strong and positive association with the management of change. Linking of study variables during change is helpful for the top managers for better understanding during a major organizational change. Supporting the employees through human touch during change will lead to easier transition. Understanding of various dimensions that influence employee to readiness for organizational change is an important endeavor for organizational change.
Originality/value
This research attempts to investigate how at the times of turbulent change in an organization, trust between the employees and their supervisor, perceived social support and emotional attachment with the organization positively impact the change management process. The findings provides valuable insights for the top management to understand the psyche of its employees and provide them a human touch during time of organizational change.
Artificial intelligence (AI) has penetrated many organizational processes, resulting in a growing fear that smart machines will soon replace many humans in decision-making. To provide a more proactive and pragmatic perspective, this article highlights the complementarity of humans and AI, and examines how each can bring their own strength in organizational decision making processes typically characterized by uncertainty, complexity, and equivocality. With a greater computational information processing capacity and an analytical approach, AI can extend humans' cognition when addressing complexity, whereas humans can still offer a more holistic, intuitive approach in dealing with uncertainty and equivocality in organizational decision-making. This premise mirrors the idea of 'intelligence augmentation': AI systems should be designed with the intention of augmenting, not replacing, human contributions.
Purpose
The purpose of this research is to demonstrate that customer engagement behavior may not always be a positive experience to customers. Specifically, the paper examines the effect of sources of help (employee vs. customer) on customer satisfaction, and the underlying mechanism behind such an effect.
Design/methodology/approach
Three studies were conducted to test the hypotheses, and bootstrapping was used to analyze the proposed mediation and moderation models.
Findings
Results from the studies demonstrated the effect of sources of help (employee vs. customer) on customer satisfaction. Specifically, compared to those who have received help from employees, customers who have received help from other customers showed lower satisfaction toward the firm. The relationship between sources of help and satisfaction was mediated by an affective factor, embarrassment, and a cognitive factor, altruistic motivation. In addition, the relationship between embarrassment and satisfaction was moderated by concern for face (CFF).
Practical implications
Firms should devote more resources toward minimizing customers’ embarrassment during service encounters and demonstrate altruistic motivation to provide voluntary help to lead customers to reciprocate helping.
Originality/value
The current research provides a new perspective on customer engagement behavior during service encounters. This research highlights the negative outcomes of receiving help from other customers.
The environmental impact has been a critical issue in supply chain network research. Handling it efficiently will help the firm reduce pollution and save cost. In order to improve the environmental effect on this supply chain network, recovery activity becomes a significant part in the reverse logistics network, due to complexity and heterogeneity. In a reverse logistics network, there are many decision problems to be taken into consideration such as facility location, capacity allocation, production planning and vehicle routing. Each of these problems can be categorized into three types of decision planning: strategic, tactical or operational, based on the time planning horizon. In this paper, we review the literature of supply chain network in an effort to achieve green through implementing reverse logistic practices, and classify the defined problems by the group of decision planning. In general, most of the literature are focused on the strategic and tactical decision planning, only a few are operational-based. The objective of this paper is to analyze the decision planning being done in this area of research and provide future insights on how to design the reverse logistics network with different decision planning which will reflect the real life scenario.
In recent era, the creation of electronic contraptions is expanding step by step, which leads to tremendous electronic waste. This is the condition drives as a result of the current individuals expecting new innovation in a brief time. This circumstance makes the manufacturers in a circumstance to create the new item in a brief time, so the current items need to either be disposed or dismantled. In this circumstance the manufactures wanted to re-manufacture the end-of-life (E-O-L) items to meet the component necessity in the new items production. In this sense in our previous work we exhibited a methodology to choose the optimal number of take back product, so that the aggregate expense needed for the reverse logistic (RL) can be lessened. In this paper, we exhibited a novel methodology for the RL operation of E-O-L items, in view of an optimal scheduling algorithm. In the previous work we have located the optimal number of take back E-O-L products for the disassembly-to-order (D-TO), and this paper displayed the procedure to legitimately schedule the machines to disassemble the products, so that the aggregate time as well as the total cost needed for the RL operation can be diminished. In the proposed structure artificial bee colony (ABC) algorithm is utilized. The proposed framework drives the preferred performance over the existing scheduling algorithms. Keywords reverse logistic, end-of-life product, disassembly-to-order, meta-heuristic optimization, artificial bee colony algorithm, disassembly scheduling.
Accpted for publication in Research Policy
Abstract: There is considerable and growing interest in the emergence of novel technologies, especially from the policy-making perspective. Yet as an area of study, emerging technologies lacks key foundational elements, namely a consensus on what classifies a technology as ’emergent’ and strong research designs that operationalize central theoretical concepts. The present paper aims to fill this gap by developing a definition of ’emerging technologies’ and linking this conceptual effort with the development of a framework for the operationalisation of technological emergence. The definition is developed by combining a basic understanding of the term and in particular the concept of ’emergence’ with a review of key innovation studies dealing with definitional issues of technological emergence. The resulting definition identifies five attributes that feature in the emergence of novel technologies. These are: (i) radical novelty, (ii) relatively fast growth, (iii) coherence, (iv) prominent impact, and (v) uncertainty and ambiguity. The framework for operationalising emerging technologies is then elaborated on the basis of the proposed attributes. To do so, we identify and review major empirical approaches (mainly in, although not limited to, the scientometric domain) for the detection and study of emerging technologies (these include indicators and trend analysis, citation analysis, co-word analysis, overlay mapping, and combinations thereof) and elaborate on how these can be used to operationalise the different attributes of emergence.
Artificial Intelligence (AI) characterizes a new generation of technologies capable of
interacting with the environment and aiming to simulate human intelligence. The success of integrating AI into organizations critically depends on workers’ trust in AI technology. This review explains how AI differs from other technologies and presents the existing empirical research on the determinants of human trust in AI, conducted in multiple disciplines over the last twenty years.
Based on the reviewed literature, we identify the form of AI representation (robot, virtual,
embedded) and the level of AI’s machine intelligence (i.e. its capabilities) as important antecedents to the development of trust and propose a framework that addresses the elements that shape users’ cognitive and emotional trust. Our review reveals the important role of AI’s tangibility, transparency, reliability and immediacy behaviors in developing cognitive trust, and the role of AI’s anthropomorphism specifically for emotional trust. We also note several limitations in the current evidence base, such as diversity of trust measures and over-reliance on short-term, small sample, and experimental studies, where the development of trust is likely to be different than in
longer term, higher-stakes field environments. Based on our review, we suggest the most
promising paths for future research.
This study focuses on four key new-age technologies – the Internet of Things, Artificial Intelligence, Machine Learning, and Blockchain – and their respective roles in marketing. Specifically, the study examines each of these four technologies in depth, to understand their key elements, the domains in which they operate, and current use cases pertaining to these technologies. The adoption of these new-age technologies is expected to have an impact on the outcomes for firms and customers. However, these are dynamically evolving technologies that have not yet been completely explored and whose full potential has yet to be uncovered. This study presents some research questions that are pertinent to key entities (firms, customers, intermediaries, developers, and regulators), and which merit deeper investigation in the future. This study also highlights major areas that need managerial focus in the adoption of these new-age technologies.
Purpose
The development of self-service technologies, while intended to better serve customers by offering them autonomy, has created situations in which individuals may require additional help. The purpose of this paper is to explore perceptions of chat as an assistance channel, to identify its perceived role in a customer service environment.
Design/methodology/approach
In all, 23 semi-structured interviews held with both chat and non-chat users assessed perceptions of chat in an assistance encounter. A thematic analysis was used.
Findings
The findings highlight a paradoxical perception of chat in a customer assistance context. On the one hand, customers perceive live chat as mainly beneficial in a customer service context, alleviating embarrassment, perceived threats and potential dissatisfaction linked to assistance requests. On the other hand, the elusive nature of a chat conversation interlocutor (human or artificial) adversely affects how customers interpret assistance from companies.
Research limitations/implications
This research underscores the perceived threats of assistance encounters and shows the ambivalent role of chat in such a context. It also highlights chat’s specific features that make it a relevant medium for assistance requests.
Practical implications
This study helps companies better understand customers’ perceptions of assistance requests and chat in that context. Companies can use the findings to develop better ways to address assistance needs and offer transparent and fully personalized human chat to provide an inclusive service.
Originality/value
This paper highlights the ambivalent role of chat as an assistance channel, easing assistance requests but also entailing a potential negative spillover effect, when negative chat perceptions of an artificial interlocutor have consequences.
As far back as the industrial revolution, significant development in technical innovation has succeeded in transforming numerous manual tasks and processes that had been in existence for decades where humans had reached the limits of physical capacity. Artificial Intelligence (AI) offers this same transformative potential for the augmentation and potential replacement of human tasks and activities within a wide range of industrial, intellectual and social applications. The pace of change for this new AI technological age is staggering, with new breakthroughs in algorithmic machine learning and autonomous decision-making, engendering new opportunities for continued innovation. The impact of AI could be significant, with industries ranging from: finance, healthcare, manufacturing, retail, supply chain, logistics and utilities, all potentially disrupted by the onset of AI technologies. The study brings together the collective insight from a number of leading expert contributors to highlight the significant opportunities, realistic assessment of impact, challenges and potential research agenda posed by the rapid emergence of AI within a number of domains: business and management, government, public sector, and science and technology. This research offers significant and timely insight to AI technology and its impact on the future of industry and society in general, whilst recognising the societal and industrial influence on pace and direction of AI development.
Purpose
The purpose of this study is to empirically investigate the extent to which two types of commercial partnerships (business partner and non-business partner) affect the collaborative innovation of firms in emerging economies. Specifically, the roles of two commercial partnerships are investigated. Additionally, the study explores the moderating effect of external technological uncertainty and internal dynamic capabilities on the relationship between two commercial partnerships and on collaborative innovation.
Design/methodology/approach
Using a sample of 370 high-tech firms in China, the authors applied the partial least squares structural equation modeling approach to model these relationships.
Findings
The findings reveal opportunities and challenges for companies according to two intensities of commercial partnership for collaborative innovation. The partnership contribution to innovation and competiveness is different within the two routes and ranges. The findings indicate that (1) intense commercial relationships with business partners have a stronger positive significant impact on collaborative innovation than those with non-business partners and (2) non-business partners have a weaker positive impact on collaborative innovation at high external technological uncertainty. It was also found that (3) the positive impact of business partners on collaborative innovation is weakened when a firm has high dynamic capabilities, whereas the positive impact of non-business partners is strengthened.
Research limitations/implications
Insight into the roles of two commercial partnerships in achieving collaborative innovation facilitates the advancement of the theoretical understanding of the circumstances under which cooperative innovation can be more effective under different partnerships.
Originality/value
A key strategic question is whether comprehensiveness enables firms to make better strategic decisions in various environments. In the process of innovation, companies must choose different types and quantities of partners, and they must regulate their partners’ innovative behavior by establishing a corresponding network structure and relationship rules. The current study focuses on analysis of how different intensities of commercial partnerships affect collaborative innovation. This research provides a theoretical framework that creates a new classification of commercial relations with regard to collaborative innovation, and it highlights the difference between the two types of partnerships. This study finds that there are many problems in the selection of innovative partners in China’s high-tech companies. Therefore, companies should strengthen their understanding of cooperative innovation, and they should build and manage highly efficient innovation networks. This study helps companies, high-tech industry associations, academia and government to take enhanced, informed actions.
Purpose
This paper aims to conceptualize two dimensions of active innovation resistance (AIR): cognitive active resistance and emotional active resistance. A scale to measure this construct is proposed and tested.
Design/methodology/approach
Three studies were conducted, with sample sizes of 195, 190 and 186, to test the discriminant, convergent, nomological and criterion validity of the proposed AIRc+e scale and to analyze its explanatory and predictive power. Data were gathered using the online platform of a US-based research company.
Findings
The authors provide evidence that AIR is a two-dimension construct comprising a cognitive and an emotional dimension. AIR was modeled as a third-order construct, comprising two second-order constructs, cognitive active resistance and emotional active resistance. The impact of adding an emotion dimension to active resistance was therefore assessed, and the results indicated that the explanatory and predictive power of the AIR measure improved as expected.
Practical implications
Consumers are most likely to resist innovations launched onto the marketplace, either prior to or after evaluating them. A better understanding of the reasons behind their resistance to innovation, as well as of its mechanisms, is of great importance in decreasing an innovation’s chances of failure.
Originality/value
This study proposes that incorporating emotion into the assessment of AIR will result in a deeper understanding of adoption and rejection behavior, expanding the current knowledge of consumer behavior in innovation-related, new product adoption and decisions.
Purpose
This paper aims to identify and appropriately respond to any socio-technical gaps within organisational information and cybersecurity practices. This culminates in the equal emphasis of both the social, technical and environmental factors affecting security practices.
Design/methodology/approach
The socio-technical systems theory was used to develop a conceptual process model for analysing organisational practices in terms of their social, technical and environmental influence. The conceptual process model was then applied to specifically analyse some selected information and cybersecurity frameworks. The outcome of this exercise culminated in the design of a socio-technical systems cybersecurity framework that can be applied to any new or existing information and cybersecurity solutions in the organisation. A framework parameter to help continuously monitor the mutual alignment of the social, technical and environmental dimensions of the socio-technical systems cybersecurity framework was also introduced.
Findings
The results indicate a positive application of the socio-technical systems theory to the information and cybersecurity domain. In particular, the application of the conceptual process model is able to successfully categorise the selected information and cybersecurity practices into either social, technical or environmental practices. However, the validation of the socio-technical systems cybersecurity framework requires time and continuous monitoring in a real-life environment.
Practical implications
This research is beneficial to chief security officers, risk managers, information technology managers, security professionals and academics. They will gain more knowledge and understanding about the need to highlight the equal importance of both the social, technical and environmental dimensions of information and cybersecurity. Further, the less emphasised dimension is posited to open an equal but mutual security vulnerability gap as the more emphasised dimension. Both dimensions must, therefore, equally and jointly be emphasised for optimal security performance in the organisation.
Originality/value
The application of socio-technical systems theory to the information and cybersecurity domain has not received much attention. In this regard, the research adds value to the information and cybersecurity studies where too much emphasis is placed on security software and hardware capabilities.
Purpose
The purpose of this paper is to propose a forecasting model to predict the demand under uncertain environment to control the bullwhip effect (BWE) considering review-period order-up-to level ((R, S)) inventory control policy and its different variants such as (R, βS) (R, γO) and (R, γO, βS) proposed by Jakšič and Rusjan, (2008) and Bandyopadhyay and Bhattacharya (2013).
Design/methodology/approach
A hybrid forecasting model has been developed by combining the feature of discrete wavelet transformation (DWT) and an intelligence technique, multi-gene genetic programming (MGGP), denoted as DWT-MGGP. Performance of DWT-MGGP model has been verified under (R, S) inventory control policy considering demand from three different manufacturing companies.
Findings
A comparison between DWT-MGGP model and autoregressive integrated moving average forecasting model has been done by estimating forecast error and BWE. Further, this study has been extended with analysing the behaviour of BWE considering different variants of (R, S) policy such as (R,βS) (R, γO) and (R,γO,βS) and found that BWE can be moderated by controlling the inventory smoothing (β) and order smoothing parameters (γ).
Research limitations/implications
This study is limited to different variants of (R, S) inventory control policy. However, this study can be further extended to continuous review policy.
Practical implications
The proposed DWT-MGGP model can be used as a suitable demand forecasting model to control the BWE when (R, S), (R,βS) (R,γO) and (R,γO,βS)inventory control policies are followed for replenishment.
Originality/value
This study analyses the behavior of BWE through controlling the inventory smoothing (β) and order smoothing parameters (γ) when demand is predicted using DWT-MGGP forecasting model and order is estimated using (R, S), (R,βS) (R,γO) and (R,γO,βS) inventory control policies.
Purpose
An emerging theme in the practitioner literature suggests that the supply chain of the future – enabled especially by developments in ICT – will be autonomous and have predictive capabilities, bringing significant efficiency gains in an increasingly complex and uncertain environment. This paper aims to both bridge the gap between the practitioner and academic literature on these topics and contribute to both practice and theory by seeking to understand how such developments will help to address key supply chain challenges and opportunities.
Design/methodology/approach
A multi-disciplinary, systematic literature review was conducted on relevant concepts and capabilities. A total of 126 articles were reviewed covering the time period 1950-2018.
Findings
The results show that both IoT and AI are the technologies most frequently associated with the anticipated autonomous and predictive capabilities of future supply chains. In addition, the review highlights a lacuna in how such technologies and capabilities help address key supply chain challenges and opportunities. A new supply chain model is, thus, proposed, one with autonomous and predictive capabilities: the self-thinking supply chain.
Originality/value
It is our hope that this novel concept, presented here for the first time in the academic literature, will help both practitioners to craft appropriate future-proofed supply chain strategies and provide the research community with a model (built upon multidisciplinary insights) for elucidating the application of new digital technologies in the supply chain of the future. The self-thinking supply chain has the potential in particular to help address some of today’s key supply chain challenges and opportunities.
Purpose
The purpose of this paper is to create an instrument for conducting future supply chain transparency research by developing and validating a measure of supplier transparency. Specifically, the research develops a two-dimensional measure of supplier transparency that builds on previous studies that independently examine visibility and traceability in supply chain management (SCM)/logistics.
Design/methodology/approach
The scale development process is carried out over three stages (item generation, scale purification, scale validation). Survey methods are used with two separate data collection phases involving a total of 358 managers from multiple and diverse industries.
Findings
The new supplier transparency measure is a concise, two-dimensional scale that has the potential for significant usage in the development and testing of SCM theory.
Research limitations/implications
This study implemented a purposefully general sampling procedure. However, different industries may have additional, specific constraints regarding what it means to be a transparent supplier. Additional opportunities for future research include applying the new supplier transparency measure to examine supply chain frameworks, regulatory compliance, supply chain relationships and the implementation of information technology.
Practical implications
Firms are under increasing pressure to be transparent about partner sourcing, resource utilization and other transactional issues related to the products and processes in their supply chains. The new measure may be utilized to address these issues as well as the interaction between supply chain operations and stakeholders by facilitating a quantitative assessment of supplier transparency.
Originality/value
Drawing on the established constructs of supply chain visibility and traceability, a measure of supplier transparency is developed, supported by a review of the literature, input from subject matter experts and interviews with supply chain managers. Suggestions are made for future validation of supplier transparency within established supply chain frameworks.
Changing organizations is difficult. In this paper, we analyze how sensemaking that follows the initiation of change initiatives relies on the interplay of prospective and retrospective aspects and we elucidate how organization members’ frames develop over time based on this interplay. Our data, 38 in-depth interviews with nursing and medical staff held at four different points in time, reveal how expectations impact the dynamics of meaning construction in change processes. Our findings demonstrate that the frames through which actors make sense of change initiatives continuously develop although the expectations embedded in them are sticky to some extent. The degree of stickiness depends on expectations that are formed through initial prospective sensemaking, as these expectations influence actors’ tolerance regarding dissonant cues. Change initiatives fail when this tolerance becomes exhausted. Our study contributes to theory on sensemaking and change by elaborating on the undertheorized role of prospective sensemaking during change processes.
Purpose
Organizations are social entities comprising multiple people that are goal-directed and have coordinated activities that are also linked to the external environment. As information technology improves, the organizational performance is also improved and it results in positive changes and development in the organizations. Near field communication (NFC) is one of such technologies that can be implemented and utilized in an organization. The purpose of this paper is to investigate the important variables impacting the adoption of NFC in organizations and propose an applicable model for it.
Design/methodology/approach
In this paper, to have a successful NFC implementation in organizations and analyze main factors impacting the NFC technology adoption in organizations, a technology acceptance model-based approach is used.
Findings
The findings show that the main variables impacting NFC adoption are ease of use, potential risk, usefulness and cost. The obtained results indicate that the model has adequate and sufficient reliability, convergent validity and discriminant validity.
Originality/value
In this paper, the factors impacting the NFC adoption in organizations are pointed out, and the proposed model is tested on samples gathered from Azerbaijan railway employees and for statistical analysis of questionnaires, the SMART-PLS 2.0 software package is used.
Landslides and large floods are serious natural disasters that every year cause multiple deaths and loss in property around the world. When these events occur in areas like the “favelas” or mountain regions in coastal cities like Rio de Janeiro, the situation becomes critical as buildings and infrastructures are not prepared to withstand them. Search and rescue teams in such disaster areas need to rely on real-time communication, which often cannot be adequately provided by cell or radio networks. In this paper, we argue that flying ad-hoc networks can provide the support needed in these scenarios and propose a new solution towards that goal, termed Flying Witness Units. We make our case by presenting real-time schedulability analysis of message delivery for a disaster scenario.
Partner selection is critical to developing successful collaboration for gaining competitive advantage in the logistics industry. In this paper, we present a hybrid approach based on BOCR and MULTIMOORA for the logistics service provider selection. The proposed approach comprises three steps. In the first step, we identify the partner selection criteria using four categories namely benefits, costs, opportunities and risks (BOCR). The second step involves generating linguistic ratings for potential partners on the identified criteria by a committee of decision-making experts. In the third and the last step, final partner selection is done using fuzzy MULTIMOORA. Linguistic information (fuzzy numbers) is used to address the lack of quantitative data. A numerical application is provided. Monte Carlo simulationbased sensitivity analysis is conducted to determine the robustness of MULTIMOORA to variation in criterion and decision maker weights. The strength of our work is the ability to perform logistics partner selection under limited or lack of quantitative data. Besides, BOCR technique allows evaluation of logistics partners from multiple perspectives namely benefits, costs, opportunities and risks. The use of MULTIMOORA technique permits the generation of robust alternative rankings due to incorporation of three inbuilt evaluation functions.
The impact of the industrial and digital (information) revolutions has, undoubtedly, been substantial on practically all aspects of our society, life, firms and employment. Will the forthcoming AI revolution produce similar, far-reaching effects? By examining analogous inventions of the industrial, digital and AI revolutions, this article claims that the latter is on target and that it would bring extensive changes that will also affect all aspects of our society and life. In addition, its impact on firms and employment will be considerable, resulting in richly interconnected organizations with decision making based on the analysis and exploitation of “big” data and intensified, global competition among firms. People will be capable of buying goods and obtaining services from anywhere in the world using the Internet, and exploiting the unlimited, additional benefits that will open through the widespread usage of AI inventions. The paper concludes that significant competitive advantages will continue to accrue to those utilizing the Internet widely and willing to take entrepreneurial risks in order to turn innovative products/services into worldwide commercial success stories. The greatest challenge facing societies and firms would be utilising the benefits of availing AI technologies, providing vast opportunities for both new products/services and immense productivity improvements while avoiding the dangers and disadvantages in terms of increased unemployment and greater wealth inequalities.
Given the complexity of green public procurement, decisions are likely to be driven by bounded rationality. However, we know little about what determines supplier selection criteria in any given situation. This study
explores buyer behavior when considering environmental criteria. We first conducted interviews and identified 12 operational procedures used by buyers. We then developed a survey to explore the use of these procedures.
Our quantitative analysis suggests that public buyers are motivated by their belief that they can make a difference. This is independent of buyers’ experience or gender. However, their occupational position and the nature of a procurement seem to influence how buyers seek information about
environmental criteria and which information source(s) they use. The data suggest that four specific decision-making heuristics are associated with the selected operational procedures.
In Brazil, a developing country, companies must continuously improve and specialise. Thus, in order to mitigate threats from competitors, logistics warehouse management has become a contributing factor for organisation success and part of strategy. In this sense, the more space and optimising inventory operation, the better material flow from production line up to service level, deploying profitable and productive results. Therefore, technical improvements in internal operations are critical for the studied organisation survival. In this study, we intend to demonstrate how best practices and tools based on lean manufacturing methodology are able to increase efficiency by reducing costs and waste in a company of oil and gas industry. This qualitative research integrated in a field research comprises company's warehouses visitation, non-invasion observation and employees' interviews. The result of this paper proposes solutions with low costs, mitigating waste performance to outline a production enhancement.
Numerous researchers have proposed that trust is essential for understanding interpersonal and group behavior, managerial effectiveness, economic exchange and social or political stability, yet according to a majority of these scholars, this concept has never been precisely defined. This article reviews definitions from various approaches within organizational theory, examines the consistencies and differences, and proposes that trust is based upon an underlying assumption of an implicit moral duty. This moral duty—an anomaly in much of organizational theory—has made a precise definition problematic. Trust also is examined from philosophical ethics, and a synthesis of the organizational and philosophical definitions that emphasizes an explicit sense of moral duty and is based upon accepted ethical principles of analysis is proposed. This new definition has the potential to combine research from the two fields of study in important areas of inquiry.
Big data and predictive analytics (BDPA) has been at the forefront of interest for both academics and practitioners. Scholars have acknowledged the importance of BDPA in achieving business value and firm performance. However, the role of BDPA assimilation on supply chain and organizational performance has not been thoroughly investigated. To address this gap, this paper follows a resource-based view perspective to: (i) conceptualise assimilation as a three stage process that is, acceptance, routinization, and assimilation; (ii) identify the influence of resources such as big data connectivity and information sharing under the mediation effect of top management commitment on big data assimilation (capability building), and (iii) the impact of big data assimilation on supply chain performance and organizational performance. Based on our findings, we argue that big data connectivity and IS under the mediation effect of top management commitment is positively related to BDPA acceptance, which is positively related to BDPA assimilation under the mediation effect of BDPA routinization, and positively related to SCP and OP. Finally, we provide the managerial implications of our findings, the limitations of our study and future research directions.
Automation with inherent artificial intelligence (AI) is increasingly emerging in diverse applications, for instance, autonomous vehicles and medical assistance devices. However, despite their growing use, there is still noticeable skepticism in society regarding these applications. Drawing an analogy from human social interaction, the concept of trust provides a valid foundation for describing the relationship between humans and automation. Accordingly, this paper explores how firms systematically foster trust regarding applied AI. Based on empirical analysis using nine case studies in the transportation and medical technology industries, our study illustrates the dichotomous constitution of trust in applied AI. Concretely, we emphasize the symbiosis of trust in the technology as well as in the innovating firm and its communication about the technology. In doing so, we provide tangible approaches to increase trust in the technology and illustrate the necessity of a democratic development process for applied AI.