Microfoundations of dynamic capabilities in retail in the age of artificial intelligence and robotics

Abstract. The purpose of this study is to find out what are the skills possessed by people working in physical stores and how these can be utilized and managed in the future. The research problem lies in an assumption that do people working in retail stores obtain required skills and capabilities or not, and are they recognized and utilized by management. Many technological solutions have been introduced within the retail industry, but there seems to be lack of understanding how to utilize peoples’ skills differently as some predictable and repeatable tasks are (or will be) performed by machines. There have been studies conducted in the previous years about dynamic capabilities from the perspective of strategic leaders of companies. Dynamic capabilities have been researched in customer centric industries also from the perspective of corporate directors. However, there lacks a study that fully concentrates on retail industry (no other customer centric industries) and solely limits the research to study physical stores mainly from the perspective of people who actually work in the stores, not the leaders in headquarters. Therefore, it could be stated that this study fulfils a research gap lying in the analyses of dynamic capabilities and their microfoundations. The main results of the study show, that there are variations related to information flows as well as there are inefficiencies about information of technological developments happening in the field in general. There is high motivation towards personal and professional development and willingness to learn. Changes made in processes and operations are mostly based on stores’ own customer analyses and communication with their customers. Even though needs for changes are recognized and there is willingness to development, it is highly resources-dependent. Realignments or redeployments are not fully conducted within store operations if their current resources and technologies in use do not allow this. There is a need to move away from certain routines towards being more creative and analytical. Even though there were need to move away from certain routines, there were no indications of considering the routines to be disappearing from store operations. There are structures in stores and in their supportive operations, which enable good customer service, doing the tasks and routines but there are little enabling factors for other things e.g. to innovate and be creative. Skills such as ability to analyse customer behaviour, product knowledge, good communication skills, ability to learn and share new knowledge, plan, organize and prioritize are possessed. However, the structures and processes prevent to utilize these skills to truly fluently communicate new opportunities forward and be part of creating something completely new. Utilization of skills in a new untraditional way are remained in the background because of routines and the traditional role descriptions and therefore it could be argued, that the microfoundations of retail companies are not dynamic

A Review on various E-business and M-business models & Research Opportunities

A business model is a set of process/activities that results in sustainable profit through desired revenue and customer value. The business model spells out how a company makes money by specifying its position in the value chain. A business model which uses electronic communication technology such as internet for exchanging information is called e-business model. The e-business model includes the roles and relationships among a firm's customers, allies, and suppliers; the major flows of product, services, information, and money; and the major benefits to the participants. This paper contains review on various business models used in e-business, m-business, and m banking. All the major E-commerce business models which fall under 3 main categories : B2B - Business to business, B2C - Business to consumer, C2C - Consumer to consumer are also discussed with their benefits and limitations. Based on business model framework, various research agenda and opportunities are identified and elaborated

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl

Major challenges and solutions for utilizing big data in the maritime industry

The dissertation is a study of big data for the use in the maritime industry. Today’s society is information-intensive. The term “big data” is becoming more common. In fact, some maritime companies and institutions have already been trying to utilize big data for enhancing maritime safety and environmental protection. In order to promote this trend, the dissertation tries to identify common and important challenges for the whole maritime industry in terms of the utilization of big data and propose corresponding solutions. First, by reviewing the definitions of big data, three major features are identified. Big data takes electronic form, is derived through various sensors, and has difficulties in treatment. In terms of difficulty, there are four aspects, which are volume, velocity, variety and veracity. Noting these features, the scope of the dissertation is set as electronic voyage-related data derived on board ships. It is clarified that the major features of big data is applicable to such data. Second, examples of cutting edge institution/framework related to maritime big data are studied. As such institutions, DNV-GL, Lloyd’s Register Foundation and e-navigation framework in the IMO are chosen. Consequently, four major categories of difficulty in further use of maritime big data are identified. They are sound competitive conditions, human resources, technology and security. Third, possible solutions for identified four categories of problem are discussed and proposed. Regarding sound competitive conditions, instructions on rights and responsibilities of treating big data should be provided. As regards human resources, governments are expected to show its strategy on nurturing experts in need and promote cooperation with the academia and associated industry. Development of technology can be underpinned by research and development aid scheme as well as unification of the style of relevant data sets. Security issue requires well-established legislations and secure and resilient system against cyber-attacks

How to design for persistence and retention in MOOCs?

Design of educational interventions is typically carried out following a design cycle involving phases of investigation, conceptualization, prototyping, implementation, execution and evaluation. This cycle can be applied at different levels of granularity e.g. learning activity, module, course or programme. In this paper we consider an aspect of learner behavior that can be critical to the success of many MOOCs i.e. their persistence to study, and the related theme of learner retention. We reflect on the impact that consideration of these can have on design decisions at different stages in the design cycle with the aim of en-hancing MOOC design in relation to learner persistence and retention, with particular attention to the European context

Corporate venturing in the media & entertainment industry: contextual factors that influence corporate venture decision making

This research study focuses on Corporate Venturing (CV) within the Media and Entertainment(M&E) industry and investigates factors that influence CV decision-making. CV provides a viable strategy to facilitate innovation and organizational change within companies; however, this area is understudied within the M&E industry. Companies that make up the entertainment industry may not have specific insight into how best to exploit this opportunity. Specifically, this study looks at the influence of firm characteristics, industry characteristics, and other outside factors using the PESTLE strategy model characteristics impacting the parent or venture unit\u27s CV decision-making within the M&E industry. In terms of this research project, the focus is on the CV activity of the firm. Activity is defined as the decision to engage in corporate venturing, as well as the mode of CV to engage in. CV modes include, but are not limited to, corporate venture capital (CVC), venture alliances, and transformation arrangements. Additionally, in terms of M&E, this research focuses on television, film, and streaming with a specific emphasis on innovation and growth strategies. This research is a descriptive study and provides propositions to contribute to the emerging convergent literature on the topic. This research makes a relevant contribution to the fields of CV and strategy and to the entertainment and technology industries

It's getting crowded! : improving the effectiveness of microtask crowdsourcing

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(Dis)Obedience in Digital Societies: Perspectives on the Power of Algorithms and Data

Algorithms are not to be regarded as a technical structure but as a social phenomenon - they embed themselves, currently still very subtle, into our political and social system. Algorithms shape human behavior on various levels: they influence not only the aesthetic reception of the world but also the well-being and social interaction of their users. They act and intervene in a political and social context. As algorithms influence individual behavior in these social and political situations, their power should be the subject of critical discourse - or even lead to active disobedience and to the need for appropriate tools and methods which can be used to break the algorithmic power

(Dis)Obedience in Digital Societies

Algorithms are not to be regarded as a technical structure but as a social phenomenon - they embed themselves, currently still very subtle, into our political and social system. Algorithms shape human behavior on various levels: they influence not only the aesthetic reception of the world but also the well-being and social interaction of their users. They act and intervene in a political and social context. As algorithms influence individual behavior in these social and political situations, their power should be the subject of critical discourse - or even lead to active disobedience and to the need for appropriate tools and methods which can be used to break the algorithmic power

User-Generated Data Network Effects and Market Competition Dynamics

This Article defines User-Generated Data (“UGD”) network effects, distinguishes them from the more familiar concept of traditional network effects, and explores their implications for market competition dynamics. It explains that UGD network effects produce various efficiencies for digital service providers (“data platforms”) by empowering their services’ optimization, personalization, and continuous diversification. In light of these efficiencies, competition dynamics in UGD-driven markets tend to be unstable and lead to the formation of dominant multi-industry conglomerates. These processes will enhance social welfare because they are natural and efficient. Conversely, countervailing UGD network effects also empower data platforms to detect and neutralize competitive threats, price discriminate among users, and manipulate users’ behaviors. The realization of these effects will result in inefficiencies, which will undermine social welfare. After a comprehensive analysis of conflicting economic forces, this Article sets the ground for informed policymaking. It suggests that emerging calls to aggravate antitrust enforcement and to “break up” Big Tech are ill-advised. Instead, this Article calls for policymakers to draw inspiration from traditional network industries’ public utility and open-access regulations