RSS 1.0Business and management
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The production of garments and textiles in Bangladesh: trade unions, international managers and the health and safety of workersThis paper offers a view of working practices within the garment and textile (G&T) industry in Bangladesh. The G&T industry accounts for over 84 per cent of Bangladesh exports and is therefore viewed as key to the country’s economic development. This importance is seen in the creation of Export Processing Zones (EPZs), which were created by that state to encourage foreign investment by offering a congenial climate free from cumbersome procedures. Trade unions are outlawed in these areas. Health and safety are poor within the G&T industry. However, the Rana Plaza disaster of 2013, which caused 1,132 deaths and over 2,500 injuries, placed the issue of workplace safety on the international agenda. Arguably, this prompted a change of attitude within Bangladesh and the G&T industry towards health and safety. The presence of international managers appears to have played a significant role in improving health and safety in the working environment, however these international managers do face a range of cultural barriers, which include both language and a different perception of the value of health and safety in the workplace. This paper has adopted a mixed method of both qualitative and quantitative data, collected through interviews and questionnaire surveys within the G&T industry in Bangladesh.
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A combined tactical and operational deterministic food grain transportation model: particle swarm based optimization approachThis paper proposes a combined tactical and operational two stage food grain transportation model with linear formulation in the first stage and a mixed-integer non-linear problem (MINLP) in the second stage taking the case of India. Transportation cost is minimized in both stages to fulfil a deterministic demand. First and the second stages correspond to the movement of food grains in between state and central level warehouses respectively. A novel k-parameter based method of constraint handling has been proposed. Further, the two stage MINLP formulation newly incorporates vehicle capacity constraints and proposes a generic metric for measuring vehicle utilization. First stage is solved by CPLEX and for the second stage, two population based random search techniques: Particle swarm optimization-composite particle (PSOCP) and PSO, have been employed. Experimentations on 10 different problem sets reveal that PSOCP performs marginally better than PSO with lesser standard deviation of global fitness and better solution quality with slightly higher CPU time. Later, sensitivity analysis is conducted on all ten problem sets and a decision support framework is proposed to assist potential stakeholders.
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Assessing the appropriate grassroots technological innovation for sustainable developmentGrassroots technological innovation (GRTI) is perceived as a source of sustainable development while addressing local problems and needs of people belonging to the bottom of the economic pyramid. The fostering of sustainable development develops a need for scientific evaluation and subsequent diffusion of GRTI to ameliorate the livelihood of grassroots communities. It is, hence, the purpose of this research to assess the relative performance of different GRTIs with respect to economic, social, and environmental benefits. The empirical data for this study comprised of 32 GRTIs from the three different rural non-farm sectors in the Indian context. Analytical hierarchy process is used for deducing the relative assessment of the selected GRTI against the aforementioned performance criteria. The findings of this study offer imperative insights into the field of technology diffusion and development at the grassroots level and suggest recommendations for sustainable policy formulation.
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Multi-response optimization using anova and desirability function analysis: a case study in end milling of inconel alloyNickel-based super alloys are classified as 'difficult to machine' materials due to its inherent characteristics such as high hardness, and toughness, high strength at elevated temperatures, low thermal conductivity, ability to react with cutting inserts, and ability to weld onto the surface of the cutting insert. The present study investigated the parameter optimization of end milling operation for Inconel 718 super alloy with multi-response criteria based on the Taguchi method and desirability function analysis. Experimental tests were carried out based on an L9 orthogonal array of Taguchi method. The influence of machining factors cutting speed, feed rate and depth of cut were analyzed on the performances of surface roughness and material removal rate. The optimum cutting conditions are obtained by Taguchi method and desirability function. The analysis of variance (ANOVA) is also applied to investigate the effect of influential parameters. A regression model was developed for surface roughness and material removal rate as a function of cutting velocity, feed rate and depth of cut. Finally, the confirmation experiment was conducted for the optimal machining parameters, and the betterment has been proved. © 2006-2014 Asian Research Publishing Network (ARPN).
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Development of an effective cost minimization model for food grain shipmentsThis paper makes an attempt to address the issue of food grain shipments taking a case of Indian food grain supply chain. The existing process of transportation and distribution has been represented as a four stage process. In this work only the first stage has been formulated as a bi-level nodal capacity network flow problem with linear model in the first level and a mixed integer non-linear model in the second level considering minimization of transportation costs. The model captures rail-road flexibility. Finally two variants of particle swarm optimization algorithms are used to solve the model and results are compared.
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Part segregation based on particle swarm optimisation for assembly design in additive manufacturingMinimising total production time in the additive or layered manufacturing is a critical concern, and in this respect, the idea of balancing assembly time and build time is rapidly gaining research attention. The proposed work intends to provide benefit in terms of reduced lead time to customers in a collaborative environment with simultaneous part printing. This paper formulates a mixed-integer non-linear programming (MINLP) model to evaluate the near optimal threshold area and support material allocation while segregating parts for a single material additive manufacturing set-up. The resulting time minimisation model is finitely bounded with respect to support material volume, total production time and total assembly cost constraints. A novel swarm intelligence-based part segregation procedure is proposed to determine the number of part assemblies and part division scheme that adheres to cross-sectional shape, cross-sectional area, and height restrictions. The proposed approach is illustrated and evaluated for objects with regular as well as free-form surfaces using two different hypothetically simulated real size 3D models. Results indicate that the proposed approach is able to reduce the total amount of manufacturing time in comparison with single part build time for all the tested cases.
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Optimization of machining parameters for end milling of Inconel 718 super alloy using Taguchi based grey relational analysisThis study investigated the parameter optimization of end milling operation for Inconel 718 super alloy with multi-response criteria based on the taguchi orthogonal array with the grey relational analysis. Nine experimental runs based on an L9 orthogonal array of Taguchi method were performed. Cutting speed, feed rate and depth of cut are optimized with considerations of multiple performance characteristics namely surface roughness and material removal rate. A grey relational grade obtained from the grey relational analysis is used to solve the end milling process with the multiple performance characteristics. Additionally, the analysis of variance (ANOVA) is also applied to identify the most significant factor. Finally, confirmation tests were performed to make a comparison between the experimental results and developed model. Experimental results have shown that machining performance in the end milling process can be improved effectively through this approach.
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The applicability of best value in the Nigerian public sectorWe examine the applicability of Best Value practices in the Nigerian public sector and present a Best Value Model for Nigeria. We find the literature does not extend to the Nigerian context. We make contributions towards understanding stakeholder perceptions of public service delivery best practice. We show Best Value as a significant initiative for improving public service delivery. The mixed methods survey reveals Nigerian Best Value initiatives do not exist significantly, but are applicable. Outcomes are exploration of a new area for Best Value application, incorporation of implementation issues into the model and the seven-stage process for its implementation. © Taylor & Francis Group, LLC.
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Impact of engaging teaching model (ETM) on students’ attendanceNon-attendance in Higher Education is not a new concept. In recent years with the exponential growth in digital learning, physical attendance has become a more complex issue. Educators are continually advocating an engaging teaching approach for students as a means of enhancing learning. This on-going study focuses on exploring the existing issues related to student non-attendance and the impact of a proposed engaging teaching model (ETM) on students’ attendance. This research questions whether an engaged learning session could make a positive impact on students’ attendance. The objectives highlighted in this study are to examine the reasons for non-attendance and generic measures for increasing attendance; and highlight the impact of an engaging teaching model on students’ attendance. The inference drawn from the qualitative method undertaken by 89 participants is the development of ETM to enhance students’ attendance. The study is beneficial to educators, researchers and policy-makers, in order for them to consider not only the content of their subjects, but also how students engage with these resources, which consequently facilitate students’ interest in attending lectures.
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Impact of COVID-19 pandemic on information management research and practice: transforming education, work and lifeThe COVID-19 pandemic has forced many organisations to undergo significant transformation, rethinking key elements of their business processes and use of technology to maintain operations whilst adhering to a changing landscape of guidelines and new procedures. This study offers a collective insight to many of the key issues and underlying complexities affecting organisations and society from COVID-19, through an information systems and technological perspective. The views of 12 invited subject experts are collated and analysed where each articulate their individual perspectives relating to: online learning, digital strategy, artificial intelligence, information management, social interaction, cyber security, big data, blockchain, privacy, mobile technology and strategy through the lens of the current crisis and impact on these specific areas. The expert perspectives offer timely insight to the range of topics, identifying key issues and recommendations for theory and practice.
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Predicting monthly natural gas production in China using a novel grey seasonal model with particle swarm optimizationAccurate prediction of short and medium-term monthly natural gas production in a country is the basis for understanding the supply capacity of natural gas in different months, and for the timely adjustment of natural gas production and import strategies. In China the monthly production of natural gas has obvious seasonal and cyclical variations, thus the use of a traditional grey prediction model is not very effective. As a result, a novel grey seasonal model is proposed in this paper. This is the Particle swarm optimized Fractional-order-accumulation non-homogenous discrete grey Seasonal Model (PFSM(1,1) model). This model enhances the adaptability to seasonal fluctuation data in two ways: the seasonal adjustment of the original data, and improvement of model self-adaptability. We use monthly natural gas production data of China for the period 2013-2018 as samples to predict those for the period 2019-2023. To demonstrate the PFSM(1,1) model does indeed exhibit better predictive capability, we also use the Holt–Winters model and a seasonal GM(1,1) model to predict monthly natural gas production, and compare the results with the model proposed here. The prediction results show that monthly natural gas production in China will continue to increase throughout the 2019-2023 period, that the peak-to-valley differences in monthly production values will also increase, and that the seasonal variations in production will become increasingly pronounced. Moreover, although Chinese production of natural gas is increasing, it will still be difficult to meet future demand, and hence the gap between supply and demand will increase year by year. We conclude that China needs to develop a more complete import plan for gas to meet expected natural gas consumption.
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IoT sensors in aquaculture-barriers and facilitators for sustainability in Brazilian contextThe UNEP has identified Artisanal Fisheries (AF) as the most important sector for meeting the twin needs of economic development and environmental sustainability. During 2004-14, fish exports from Brazil declined from 107,000 tons to 34,000 tons while fish imports grew from 158,000 tons to 403,000 tons due to low productivity of Brazilian AFs. Hence it is important to understand opportunities for productivity improvement of these fisheries. With productivity improvement and further expansion, sustainability issues should be considered carefully. In a project funded by the Newton Fund in the UK and FAPESC in Brazil, we explored drivers of sustainable development of AFs in Santa Catarina via a stakeholder workshop and field visits. We also interacted with officials at the EPAGRI, the research and agricultural extension organization of the State of Santa Catarina. Findings of the workshop and field visits are briefly summarized below but will be elaborated during the presentation at the conference. • The level of technology use in aquafarms in Brazil is basic. There were one or two instances of old mechanical technology but there is no integration with the latest electronic, IoT technologies. • There is general lack of awareness of sustainability issues among most AFs. Since water supply is abundant, they tend to think pollution is not an issue. Without adequate efforts in treating the waste water before they leave aquafarms, pollution issues could be the most significant limiting factor for future expansion of AFs. • There is a lack of awareness of the fish supply chains as most AFs do not directly deal with supermarkets or final consumers. • Lack of financing and access to banks is also a serious bottleneck for future expansion. • The AFs tend to work with traditional models of fishing. More training on new business models (e.g., (i) linking directly to market, (ii) using cooperative structures to ensure large supplying power and consequently winning bargaining power, and (iii) developing future contracts for supply of fish to the market to ensure all fish produced are sold on time) will help the entire AF industry. • New Internet of Things (IoT) technologies should be developed with local culture in mind. It can be automatic resulting in fewer labour requirements but should be supplemented with proper guidance documents (e.g., user manuals or online provision). Working with local knowledge centres such as EPAGRI is crucial for continued diffusion of these technologies to all eligible AFs and for regular advice on maintenance. •Lack of supporting infrastructure for aquaculture development, e.g. analytic systems, ERP, e-learning, regulations, government support, legal frameworks, etc. On the basis of the study, three areas can be recommended for future IoT and Big Data applications: (i) improving the monitoring and control of water quality, such as oxygen and temperature, by using sensors and remote control digital technologies; (ii) optimising and managing the fish feed to improve resource efficiency; (iii)-enabling fish farmers to have better access to information (e.g. suppliers information, weather, market demand and prices, costs, etc), knowledge and technical support and expert advice via mobile Apps.
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Entrepreneurial architecture: a framework to promote innovation in large firmsIn spite of the recognition that entrepreneurship and innovation are interlinked, very few studies have attempted to articulate this relationship. The aim of this article is to explain the nature of the relationship between entrepreneurship and innovation in large firms, arguing that entrepreneurship is an antecedent to innovation. The study employs a multidimensional entrepreneurial architecture (EA) framework for the first time and tests the effect of a battery of entrepreneurship measures on innovation output, which is reflected as degree and frequency of incremental and radical innovations. Adopting a quantitative approach, data were collected from 400 corporate firms in Oman representing various sectors of the economy. The EA dimensions reflected through entrepreneurial culture, entrepreneurial structure, entrepreneurial strategies and entrepreneurial leadership were tested through measurement and structural modelling. The results confirmed that entrepreneurship is a precursor to innovation. The EA framework, through its four dimensions, creates a collaborative and complimentary intensity that promotes innovation outputs, which may not be possible from the isolated effects of individual factors. The present study extends the extant literature, explaining how these entrepreneurship measures synergistically impact varying levels of innovation output. It has practical implications for managers in large firms involved in promoting innovation. They can transform the existing organisational architecture into an EA, by transplanting these entrepreneurship measures and creating a framework that promotes innovation.
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Human resource development, creativity and innovationHuman resource management (HRM) and development, learning, knowledge management and innovation represent complex and dynamic fields that draw upon multiple disciplines and emphasise the need for multilevel consideration. Such dynamic complexities present opportunities and challenges in an attempt to develop holistic theoretical approaches of how people management implications might contribute to sustainable innovation and performance. The various contributions to this book raise awareness and contribute to a shared understanding of innovation and HRM from multiple perspectives. They highlight the implications for people management through different lenses, including strategic and systems approaches at the level of the organisation, leadership, learning and the contribution of the broader national context to skill development.
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Employee voice in the SME contextIn this chapter, we show that employee voice in small and medium-sized enterprises (SMEs) is largely absent in academic studies, representing a missing link in the theorization of employee voice. We present a general overview on important contemporary debates in the employee voice literature and locate it in industrial relations and human resource management literatures. Finally, we explore how employee voice in SMEs can be studied. We offer a number of suggestions for the academic and practitioner use of employee voice in the SME sector.
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Imaginative communities: Admired cities, regions and countries, Govers, Robert Reputo Press, Antwerp, Belgium, 2018. 158 pp. $17.99 (paper) [book review]Review of "Imaginative communities: Admired cities, regions and countries", Govers, Robert Reputo Press, Antwerp, Belgium, 2018. 158 pp. $17.99 (paper)
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Isomorphic factors in the adoption of ERP by Indian medium-sized firmsPurpose: The purpose of this paper is to adopt an institutional theory perspective to investigate the adoption of enterprise resource planning (ERP) systems by medium-sized firms in India. The rationale for this study is to provide a more complete understanding of ERP adoption, moving beyond the traditional technical and economic perspectives to include social, cultural and structural influences. These later influences are more implicit, insidious and pervasive and hence require elucidatory studies such as this, but offer a greater understanding of the adoption of information systems (IS). Design/methodology/approach: The study is undertaken by means of nine case studies of medium-sized firms in India that have adopted ERP systems. Qualitative interviews were undertaken with a range of staff in each firm and are supplemented by data from other sources such as site visit notes. Findings: Institutionally based studies have tended to focus on three high-level isomorphic pressures: coercive, normative and mimetic. The study identifies number of more detailed factors that contribute to each of these three pressures. These more detailed factors are then used to consider how factors can interact and how they can explain aspects of the Indian context of the study. Originality/value: The conceptual contribution of this study is to move beyond the technical and economic rationales frequently identified for the adoption of IS by identifying influences that are social, cultural and structural in nature. The study shows that the three high-level isomorphic pressures, mimetic, coercive and normative are comprised of more detailed factors. The empirical contribution of the paper is to identify these detailed factors, and to explore their influence, in the case of ERP adoption by Indian medium-sized firms. The study is of value to practitioners, since it is at the detailed level of factors that managers can recognize the forces they are subject to and can take action. It is also valuable to researchers since the detailed factors help address two limitations of institutional theory; a lack of agency perspective and a degree of conceptual ambiguity.
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Gaining competitive advantage from analytics through the mediation of decision-making effectiveness: An empirical study of UK manufacturing companiesWhile it is widely believed that analytics capabilities enable a company to identify valuable insights from big data thereby to improve the effectiveness of decision-making and to gain competitive advantage, little empirical research has been undertaken to investigate the mechanisms through which analytics capabilities improve decision-making effectiveness and organisational competitiveness. This paper aims to reduce this research gap in the literature. Drawing on the resource-based view (RBV), it develops a research model to specify the interrelationships among information processing capabilities, resource heterogeneity, decision-making effectiveness, and competitive advantage. It then empirically tests the proposed model using structural equation modelling based on 232 responses collected from UK manufacturing companies. This paper has made several contributions to the research and practice of business analytics and decision-making. First, it advances the literatures by conceptually developing and empirically testing a path model linking information processing capabilities to decision-making effectiveness and competitive advantage. Second, it advances our knowledge by clarifying and testing the mediating role of decision-making effectiveness in affecting the relationship between information processing capabilities and competitive advantage. Third, it extends the RBV and decision-making literatures by explicating and testing the mediating role of resource heterogeneity in affecting the relationship between information processing capabilities and competitive advantage, and between information processing capabilities and decision-making effectiveness. Finally, this paper contributes to managers' and business analytics practitioners' knowledge by demonstrating the importance of improving decision-making effectiveness in gaining competitive advantage.
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Do top-performing companies use business analytics differently and why?It is suggested that companies that use business analytics perform better than those that do not in making strategic decisions and creating business value. However, little academic research based on theories exists to examine the extent to which companies differ in using business analytics and why this difference may contribute to company performance difference. To reduce this knowledge gap, this paper investigates the extent to which top and bottom performing companies differ in using business analytics by means of analysis of variance based on 232 responses collected from UK manufacturers, and seeks to explain how this use difference may be linked to performance difference drawing on the information processing view and path dependence theory. The research findings indicate that top-performing companies are three times more likely than bottom-performing companies to use business analytics and develop a data-driven environment simultaneously; and that the company differences regarding the use of business analytics and the resultant performance may be due to path dependence and how relevant organisational factors are designed. The study contributes to business analytics literature by providing empirical evidences and offering a theoretical-based understanding of business analytics, providing a foundation for future research. This study also has important managerial implications by demonstrating how business analytics can be used to improve performance.
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An analysis of the impact of business analytics on innovationThe advances in Big Data and Business Analytics (BA) have provided unprecedented opportunities for organizations to innovate. With new and unique insights gained from BA, companies are able to develop new or improve existing products/services. However, few studies have investigated the mechanism through which BA contributes to a firm's innovation success. This research aims to address this gap. From an information processing and use perspective, a research model is proposed and empirically validated with data collected from a survey with UK businesses. The evidence from the survey of 296 respondents supports the research model that provides a focused and validated view on BA's contribution to innovation. The key findings suggest that BA directly improves environmental scanning which in turn helps to enhance a company's innovation in terms of new product novelty and meaningfulness. However, the effect of BA's contribution would be increased through the mediation role of data-driven culture in the organization. Data-driven culture directly impacts on new product novelty, but indirectly on product meaningfulness through environmental scanning. The findings also confirm that environmental scanning directly contributes to new product novelty and meaningfulness which in turn enhance competitive advantage. The model testing results also reveal that innovation success can be influenced by many other factors which should be addressed alongside the BA applications.
