Professional Learning – Reflexive Managerial Learning in the Context of Dynamic Capabilities

The notion of dynamic capabilities (DC) is suggestive that organisations adapt to changing environmental demands proactively or reactively by exercising capability. Based on the assumptions of incremental change DC is constrained by the imprints of evolutionary economics evident in the writings of the early pioneers. The historical trajectory is central alongside routinized practices setting the scene for predictable learning patterns and behaviour. In as much as capability is understood in terms of an evolutionary learning framework knowledge is always tied to the past, but DC by name should be an outlook to the future. DC still elicit strong sentiments not least associated with what some scholars have described as paradoxical in its conceptualisation steeped in routines and experiential learning. How then can capability be dynamic given that the very essence of capability is based on inertia, embeddeness etc. the very source of its causal ambiguity and competitive usefulness? Indeed the notion of capability is highly suggestive as being particularly constraining on cognitive independence. Managers are the linchpins of DC, it is not surprising therefore that in contemporary writing terms such as managerial dynamic capabilities and managerial cognition are commonplace. Notwithstanding the conceptualisation of DC seems to impose severe limitations on what managers can think, learn and do, in other words a heavily curtailed agency. The reality cannot be any further from the truth, embedded in institutional structures managers actively display agentic behaviours, such as innovativeness and entrepreneurship. Perhaps the evolutionary economics functionalist view of the world left more than just an “incremental change” imprint on the field! Managers are therefore not social dopes in spite of what existing theories that variously restricts learning to socialisation suggest. How then can managerial learning be explained without conflating the powers of the individual with that of structure? A critical realist approach potentially offers a way forward. From a critical realist approach capability can be conceptualised as a social structure. Social structures predate social actors but are only reproduced in action. Social structures are also relational in nature. Managers are viewed as roles played by actors occupying certain organisational positions. It is through the role play that capabilities are reproduced, thus managerial action are central in the exercise of capability. Pre-existing managerial roles are imbued with objective cultural artefacts (rules, norms, etc.) shaping the cognition of potential incumbents. However these are subjectively accessed meaning that action outcomes may not necessarily reflect intention. Viewed this way capabilities are susceptible to reproduction as well as elaboration or change; intentionally or otherwise. Drawing on Archer’s morphogenesis approach we argue that subjectivity functions as dominant reflexive modes. In this paper we employ the concept of internal conversation to distinguish between managers that are predominantly communicative reflexives and autonomous reflexives. ‘Autonomous’ managers are independent learners, future oriented, innovative, entrepreneurial, and act strategically to improve organisational practice. On the other hand ‘communicative’ managers favour the status quo, their learning is constrained by the structure they are embedded in and act to preserve existing practices. As such we seek, through critical realism, to theorise learning in terms of internal conversation emphasising impact of learning outcomes to organisations

Sentiment Analysis using KNIME: a Systematic Literature Review of Big Data Logistics

Text analytics and sentiment analysis can help researchers to derive potentially valuable thematic and narrative insights from text-based content, such as industry reviews, leading operations management (OM) and operations research (OR) journal articles and government reports. The classification system described here analyses the aggregated opinions of the performance of various public and private, medical, manufacturing, service and retail organizations in integrating big data into their logistics. Although our results show a promising high level of model accuracy, we also suggest caution that the performance of the solution should be compared in terms of the performance of other solutions. This work explains methods of data collection and the sentiment analysis process for classifying big data logistics literature using KNIME (Konstanz Information Miner). Finally, it explores the potential of text mining to build more rigorous and unbiased models of operations management

How is Big Data Transforming Operations Models in the Automotive Industry: A Preliminary Investigation

Over the years, traditional car makers have evolved into efficient systems integrators dominating the industry through their size and power. However, with the rise of big data technology the operational landscape is rapidly changing with the emergence of the “connected” car. The automotive incumbents will have to harness the opportunities of big data, if they are to remain competitive and deal with the threats posed by the rise of new connected entrants (i.e. Tesla). These new entrants unlike the incumbents have configured their operational capabilities to fully exploit big data and service delivery rather than production efficiency. They are creating experience, infotainment and customized dimensions of strategic advantage. Therefore the purpose of this paper is to explore how “Big Data” will inform the shape and configuration of future operations models and connected car services in the automotive sector. It uses a secondary case study research design. The cases are used to explore the characteristics of the resources and processes used in three big data operations models based on a connected car framework

Big data and supply chain management: A marriage of convenience?

Big data is the new “guy about town.” Indeed, the buzz about Big Data and business intelligence (BI) as drivers of business information data collection and analysis continues to build steam. But it seems not everyone is taking notice. Whilst scholars in main are excited about the “fields of possibilities” big data and related analytics offer, in terms of optimising firm capabilities, supply chain scholars have been surprisingly quiet. In this work we hope to break this silence and we achieve this through a comprehensive survey of the literature with the aim of exposing the dynamics of big data analytics in the supply chain context. Our findings suggest that the benefits of a big data driven supply chain are many on the proviso that organisations can overcome their own myopic understanding of this socio-technical phenomenon. However, this is not to suggest a one-size fits all approach, our findings also reveal that adopting a big data strategy in the supply chain is a strategic decision and as such, given the idiosyncrasies of industries, firms should leverage these technologies in congruence with their core capabilities. Strategic fit between a firm core competences and its big data strategy creates causal ambiguity which can in turn lead to sustainable competitive advantage

Do makerspaces represent scalable production models of community-based redistributed manufacturing?

This research explores the development of local community-based “makerspaces” as potential scalable forms of redistributed manufacturing (RDM). Makerspaces are rapidly emerging in post-industrial economies and have been identified as a catalyst of local regeneration in urban areas. However, their role in local production systems is limited. There is a gap in the literature, with respect to the evolution of makerspaces and their productive contribution. The purpose of this paper therefore is to identify, classify and examine the different types of makerspaces. Our focus is on the implementation characteristics that enable industrial production activity to take place. First, we used Leximancer (to identify from the literature) three types of makerspace. Second, we then identify five RDM implementation characteristics. The characteristics were integrated together to form the RDM-makerspace implementation model. Third, case studies were purposively selected to test and advance this model. They were subsequently classified as a Type 1 (educational), Type 2 (design) or Type 3 (production) makerspace. Only one of the case studies was classified as a fully evolved Type 3 production space. The findings concur with the literature that makerspaces tend to be primarily Type 1 or Type 2. Finally, the contribution to local production theory is emphasised

The application of digital twin technology in operations and supply chain management: a bibliometric review

Purpose The application of digital twins to optimise operations and supply chain management functions is a bourgeoning practice. Scholars have attempted to keep pace with this development initiating a fast-evolving research agenda. The purpose of this paper is to take stock of the emerging research stream identifying trends and capture the value potential of digital twins to the field of operations and supply chain management. Design/methodology/approach In this work we employ a bibliometric literature review supported by bibliographic coupling and keyword co-occurrence network analysis to examine current trends in the research field regarding the value-added potential of digital twin in operations and supply chain management. Findings The main findings of this work are the identification of four value clusters and one enabler cluster. Value clusters are comprised of articles that describe how the application of digital twin can enhance supply chain activities at the level of business processes as well as the level of supply chain capabilities. Value clusters of production flow management and product development operate at the business processes level and are maturing communities. The supply chain resilience and risk management value cluster operates at the capability level, it is just emerging, and is positioned at the periphery of the main network. Originality/value This is the first study that attempts to conceptualise digital twin as a dynamic capability and employs bibliometric and network analysis on the research stream of digital twin in operations and supply chain management to capture evolutionary trends, literature communities and value-creation dynamics in a digital-twin-enabled supply chain

Exploring the influence of big data on city transport operations: a Markovian approach

© 2017, © Emerald Publishing Limited.Purpose: The purpose of this paper is to advance knowledge of the transformative potential of big data on city-based transport models. The central question guiding this paper is: how could big data transform smart city transport operations? In answering this question the authors present initial results from a Markov study. However the authors also suggest caution in the transformation potential of big data and highlight the risks of city and organizational adoption. A theoretical framework is presented together with an associated scenario which guides the development of a Markov model. Design/methodology/approach: A model with several scenarios is developed to explore a theoretical framework focussed on matching the transport demands (of people and freight mobility) with city transport service provision using big data. This model was designed to illustrate how sharing transport load (and capacity) in a smart city can improve efficiencies in meeting demand for city services. Findings: This modelling study is an initial preliminary stage of the investigation in how big data could be used to redefine and enable new operational models. The study provides new understanding about load sharing and optimization in a smart city context. Basically the authors demonstrate how big data could be used to improve transport efficiency and lower externalities in a smart city. Further how improvement could take place by having a car free city environment, autonomous vehicles and shared resource capacity among providers. Research limitations/implications: The research relied on a Markov model and the numerical solution of its steady state probabilities vector to illustrate the transformation of transport operations management (OM) in the future city context. More in depth analysis and more discrete modelling are clearly needed to assist in the implementation of big data initiatives and facilitate new innovations in OM. The work complements and extends that of Setia and Patel (2013), who theoretically link together information system design to operation absorptive capacity capabilities. Practical implications: The study implies that transport operations would actually need to be re-organized so as to deal with lowering CO2 footprint. The logistic aspects could be seen as a move from individual firms optimizing their own transportation supply to a shared collaborative load and resourced system. Such ideas are radical changes driven by, or leading to more decentralized rather than having centralized transport solutions (Caplice, 2013). Social implications: The growth of cities and urban areas in the twenty-first century has put more pressure on resources and conditions of urban life. This paper is an initial first step in building theory, knowledge and critical understanding of the social implications being posed by the growth in cities and the role that big data and smart cities could play in developing a resilient and sustainable transport city system. Originality/value: Despite the importance of OM to big data implementation, for both practitioners and researchers, we have yet to see a systematic analysis of its implementation and its absorptive capacity contribution to building capabilities, at either city system or organizational levels. As such the Markov model makes a preliminary contribution to the literature integrating big data capabilities with OM capabilities and the resulting improvements in system absorptive capacity

Getting connected: An empirical investigation of the relationship between social capital and philanthropy among online volunteers

The concept of social capital has attracted much attention from researchers and policy makers, largely due to links with positive social outcomes and philanthropic acts such as volunteering and donations. However, a rapid growth in internet technologies and social media networks has fundamentally affected the formation of social capital, as well as the way in which it potentially associates with prosocial behaviors. This study uses unique data from a survey of online volunteers to explore the interrelationships between social capital and a mix of self-reported and observed philanthropic activities in both online and offline settings. Our results show that while social capital levels associate strongly with offline donations, there are key differences in the relationships between social capital and volunteering in online and offline settings. Using a 2SLS regression analysis in order to control for endogeneity, we also infer a number of causal relationships between social capital and philanthropy

International Supply Chain Resilience: a Big Data Perspective

It was not a natural disaster but terrorism (the September 9/11 attacks) that brought into question the transactional orthodoxy guiding the post-Cold War design configuration of international supply chains. The US government reaction was to put social pressure and introduce trade measures on multi-national enterprises (MNEs) that were importing manufactured products based on scale economies and low factor production costs. They were forced to self-police their supply chains and implement security measures. If they were be able to continue to have access to the US market. In order to reduce security risks they had to become involved in public/private partnerships, have CTPAT accreditation, build up buffer “stock” and offer financial support to domestic manufacturers and logistic firms. This was perceived as a cost of production rather than a source of future capability. However security poses only one source of disruption and it became evident that there were many natural as well as man-made disasters confronting international supply chains. Therefore, by 2005, the work of MIT’s Yossi Sheffi with his seminal book “The Resilient Enterprise” brought scholarly attention to the need for firms to have resilient supply chains. A chain robust enough to absorb disruption, keep functioning and return back to normal supply activity in as short a time as possible. In 2015, Sheffi re-emphasized the power of resilience in the supply chain through his latest book “The Power of Resilience: How the Best Companies Manage the Unexpected.” This perceived resilience as a capability for building supply chain competitive advantage. Whilst supply chain resilience has grown as an important scholarly field, one area overlooked by scholars is the role to be played by big data technology. In this technical viewpoint we explore the role that big data could play in the supply chain, to improve its resilience and transform its operational capability. It acknowledges the reasons for the dearth of scholarship and also looks at the “dark side” of big data as well as highlighting the contribution that such technology might play in a radical revision of the resilience discourse. Finally, we propose an initial theoretical framework with examples of the type of operational capabilities that big data could bring with respect to international supply chain resilience

An examination of the generative mechanisms of value in big data-enabled supply chain management research

Big data technologies (BDT) are the latest instalments in a long line of technological disruptions credited with advancing the field of supply chain management (SCM) from a purely clerical function to a strategic necessity. Yet, despite the wave of optimism about the utility of BDT in SCM, the origins of value in a BDT-enabled supply chain are not well understood. This study examines the generative mechanisms of value creation in such a supply chain by a two-pronged approach. First, we interrogate the theoretical raisons d’être of BDT in SCM. Second, we examine the evidence that support the value-added potential of BDT in SCM informed by extant empirical and quantitative studies (EQS). Taken together, our analyses reveal three key findings. First, in extending the dynamic capabilities perspective, we deduced that micro-founded rather than macro-founded studies tend to be more instructive to practice. Second, we discovered that the generative mechanisms of value in a BDT-enabled supply chain operate at the level of supply chain processes. And thirdly, we found that resilience and agility are the most important dynamic capabilities that have emerged from current BDT-enabled SCM research. Insights for policy, practice, theory, and future research are discussed