The local economic development processes in low-income countries: the case of the metropolis of Chegutu in Zimbabwe

Local authorities are widely regarded as catalysts accelerating localised processes of economic development in industrialised countries but in low-income countries they are perceived as dysfunctional, inefficient and ineffective in meeting and addressing societal demands. This abstract view is however, not grounded in empirical research. As such, utilising the case of the metropolis of Chegutu a survey was designed to empirically explicate the economic processes militating its economic development. The findings are useful to policy-makers, local government authorities and management scholars. The study's unique contribution lies in its examination of the processes of local economic development in a low-income country

Unlocking the power of big data in new product development

This study explores how big data can be used to enable customers to express unrecognised needs. By acquiring this information, managers can gain opportunities to develop customer-centred products. Big data can be defined as multimedia-rich and interactive low-cost information resulting from mass communication. It offers customers a better understanding of new products and provides new, simplified modes of large-scale interaction between customers and firms. Although previous studies have pointed out that firms can better understand customers’ preferences and needs by leveraging different types of available data, the situation is evolving, with increasing application of big data analytics for product development, operations and supply chain management. In order to utilise the customer information available from big data to a larger extent, managers need to identify how to establish a customer-involving environment that encourages customers to share their ideas with managers, contribute their know-how, fiddle around with new products, and express their actual preferences. We investigate a new product development project at an electronics company, STE, and describe how big data is used to connect to, interact with and involve customers in new product development in practice. Our findings reveal that big data can offer customer involvement so as to provide valuable input for developing new products. In this paper, we introduce a customer involvement approach as a new means of coming up with customer-centred new product development

Innovation Practices in Emerging Economies: Do University Partnerships Matter?

Enterprises’ resources and capabilities determine their ability to achieve competitive advantage. In this regard, the key innovation challenges that enterprises face are liabilities associated with their age and size, and the entry barriers imposed on them. In this line, a growing number of enterprises are starting to implement innovation practices in which they employ both internal/external flows of knowledge in order to explore/exploit innovation in collaboration with commercial or scientific agents. Within this context, universities play a significant role providing fertile knowledge-intensive environments to support the exploration and exploitation of innovative and entrepreneurial ideas, especially in emerging economies, where governments have created subsidies to promote enterprise innovation through compulsory university partnerships. Based on these ideas, the purpose of this exploratory research is to provide a better understanding about the role of universities on enterprises’ innovation practices in emerging economies. More concretely, in the context of Mexico, we explored the enterprises’ motivations to collaborate with universities in terms of innovation purposes (exploration and exploitation) or alternatives to access to public funds (compulsory requirement of being involved in a university partnership). Using a sample of 10,167 Mexican enterprises in the 2012 Research and Technological Development Survey collected by the Mexican National Institute of Statistics and Geography, we tested a multinomial regression model. Our results provide insights about the relevant role of universities inside enterprises’ exploratory innovation practices, as well as, in the access of R&D research subsidies

Sourcing Technological Knowledge Through Foreign Inward Licensing to Boost the Performance of Indian Firms: The Contingent Effects of Internal R&D and Business Group Affiliation

Sourcing technological knowledge from abroad is becoming a popular strategy among emerging market firms (EMFs). Combining the Knowledge-Based View and the Resource Dependence Theory, we argue that augmenting technological knowledge through foreign licensing enables EMFs to access state-of-the-art technological knowledge, reduce operational costs and risks associated to the innovation process, and develop a knowledge-based competitive advantage, ultimately boosting their financial performance. Using data about Indian firms observed from 2001 to 2013, we find that firms with a higher share of foreign inward technology licenses report better financial performance. However, the positive impact of technological knowledge accessed through inward licensing on firm performance is contingent upon: (1) the internal knowledge developed through R&D activity, and (2) the affiliation with business groups. While Indian firms with higher level of internal R&D are able to better leverage the value of foreign technological knowledge, thus reaching higher performance, firms affiliated to business groups gain fewer benefits from licensed foreign technological knowledge than non-business-group affiliated firms

Upstream Supply Chain Visibility and Complexity Effect on Focal Company’s Sustainable Performance: Indian Manufacturers’ Perspective

Understanding supply chain sustainability performance is increasingly important for supply chain researchers and managers. Literature has considered supply chain sustainability and the antecedents of performance from a triple bottom line (economic, social, and environmental) perspective. However, the role of supply chain visibility and product complexity contingency in achieving sustainable supply chain performance has not been explored in depth. To address this gap, this study utilizes a contingent resource-based view theory perspective to understand the role of product complexity in shaping the relationship between upstream supply chain visibility (resources and capabilities) and the social, environmental, and economic performance dimensions. We develop and test a theoretical model using survey data gathered from 312 Indian manufacturing organizations. Our findings indicate that supply chain visibility (SCV) has significant influence on social and environmental performance under the moderation effect of product complexity. Hence, the study makes significant contribution to the extant literature by examining the impact of SCV under moderating effect of product complexity on social performance and environmental performance

Interactive Machine Learning: Managing Information Richness in Highly Anonymized Conversation Data

This case study focuses on an experiment analysing textual conversation data using machine learning algorithms and shows that sharing data across organisational boundaries requires anonymisation that decreases that data’s information richness. Additionally, sharing data between organisations, conducting data analytics and collaborating to create new business insight requires inter-organisational collaboration. This study shows that analysing highly anonymised and professional conversation data challenges the capabilities of artificial intelligence. Machine learning algorithms alone cannot learn the internal connections and meanings of information cues. This experiment is therefore in line with prior research in interactive machine learning where data scientists, specialists and computational agents interact. This study reveals that, alongside humans, computational agents will be important actors in collaborative networks. Thus, humans are needed in several phases of the machine learning process for facilitating and training. This calls for collaborative working in multi-disciplinary teams of data scientists and substance experts interacting with computational agents