Corporate data quality management in context

Presently, we are well aware that poor quality data is costing large amounts of money to corporations all over the world. Nevertheless, little research has been done about the way Organizations are dealing with data quality management and the strategies they are using. This work aims to find some answers to the following questions: which business drivers motivate the organizations to engage in a data quality management initiative?, how do they implement data quality management? and which objectives have been achieved, so far? Due to the kind of research questions involved, a decision was made to adopt the use of multiple exploratory case studies as research strategy [32]. The case studies were developed in a telecommunications company (MyTelecom), a public bank (PublicBank) and in the central bank (CentralBank) of one European Union Country. The results show that the main drivers to data quality (DQ) initiatives were the reduction in non quality costs, risk management, mergers, and the improvement of the company's image among its customers, those aspects being in line with literature [7, 8, 20]. The commercial corporations (MyTelecom and PublicBank) began their DQ projects with customer data, this being in accordance with literature [18], while CentralBank, which mainly works with analytical systems, began with data source metadata characterization and reuse. None of the organizations uses a formal DQ methodology, but they are using tools for data profiling, standardization and cleaning. PublicBank and CentralBank are working towards a Corporate Data Policy, aligned with their Business Policy, which is not the case of MyTelecom. The findings enabled us to prepare a first draft of a "Data Governance strategic impact grid", adapted from Nolan& MacFarlan IT Governance strategic impact grid [17], this framework needing further empirical support

CORPORATE GOVERNANCE AND EARNINGS MANAGEMENT: THE ROLE OF THE BOARD OF DIRECTOR

This study investigates the association between earnings management and corporate governance characteristics in the Chinese context. Chinese corporate governance system is improved in past two decades after deciding to move away from planned economy to market-oriented one. The data is collected from the Chinese A-listed firms for the period of 2008 to 2016 to investigate the impact of Board characteristics on Earnings management. Additionally, this study demonstrates if earnings management is still an issue in the Non-SOE or not?. This study finds that board size mitigates the earnings management, and board independence is not playing there due role in monitoring the top management. The board meetings are not so effective and therefore not contributing to mitigating the earnings management. CEO duality is not a big issue just like in developed countries. Furthermore, When segrgating the sample on the basis of ownership type, we find that, a board meeting is affective in SOE as compare to Non-SOE.Furthermore, board size substitutes the weak external governance mechanism and constrains Earnings management. Board meeting plays a complementary effect when external governance mechanism is strong. The findings of this study are significant for all stakeholders to analyze and to improve the board effectiveness and the financial reporting quality before making any decision

Data mining in medical records for the enhancement of strategic decisions: a case study

The impact and popularity of competition concept has been increasing in the last decades and this concept has escalated the importance of giving right decision for organizations. Decision makers have encountered the fact of using proper scientific methods instead of using intuitive and emotional choices in decision making process. In this context, many decision support models and relevant systems are still being developed in order to assist the strategic management mechanisms. There is also a critical need for automated approaches for effective and efficient utilization of massive amount of data to support corporate and individuals in strategic planning and decision-making. Data mining techniques have been used to uncover hidden patterns and relations, to summarize the data in novel ways that are both understandable and useful to the executives and also to predict future trends and behaviors in business. There has been a large body of research and practice focusing on different data mining techniques and methodologies. In this study, a large volume of record set extracted from an outpatient clinic’s medical database is used to apply data mining techniques. In the first phase of the study, the raw data in the record set are collected, preprocessed, cleaned up and eventually transformed into a suitable format for data mining. In the second phase, some of the association rule algorithms are applied to the data set in order to uncover rules for quantifying the relationship between some of the attributes in the medical records. The results are observed and comparative analysis of the observed results among different association algorithms is made. The results showed us that some critical and reasonable relations exist in the outpatient clinic operations of the hospital which could aid the hospital management to change and improve their managerial strategies regarding the quality of services given to outpatients.Decision Making, Medical Records, Data Mining, Association Rules, Outpatient Clinic.

Failing the market, failing deliberative democracy:How scaling up corporate carbon reporting proliferates information asymmetries

Corporate carbon footprint data has become ubiquitous. This data is also highly promissory. But as this paper argues, such data fails both consumers and citizens. The governance of climate change seemingly requires a strong foundation of data on emission sources. Economists approach climate change as a market failure, where the optimisation of the atmosphere is to be evidence based and data driven. Citizens or consumers, state or private agents of control, all require deep access to information to judge emission realities. Whether we are interested in state-led or in neoliberal ‘solutions’ for either democratic participatory decision-making or for preventing market failure, companies’ emissions need to be known. This paper draws on 20 months of ethnographic fieldwork in a Fortune 50 company’s environmental accounting unit to show how carbon reporting interferes with information symmetry requirements, which further troubles possibilities for contesting data. A material-semiotic analysis of the data practices and infrastructures employed in the context of corporate emissions disclosure details the situated political economies of data labour along the data processing chain. The explicit consideration of how information asymmetries are socially and computationally shaped, how contexts are shifted and how data is systematically straightened out informs a reflexive engagement with Big Data. The paper argues that attempts to automatise environmental accounting’s veracity management by means of computing metadata or to ensure that data quality meets requirements through third-party control are not satisfactory. The crossover of Big Data with corporate environmental governance does not promise to trouble the political economy that hitherto sustained unsustainability

Corporate Citizenship Behaviour and Rural Livelihoods: A Study on Multinational Corporations in Sri Lanka

Corporate citizenship seems a new conceptualization of the role of business in society. Disparity between rural and urban economy is growing and it is essential for multinational companies to address the issues benchmarking best practices. MNCs enter a nation with numerous guarantees but are they equipped for accomplishing objectives as a corporate citizen?  Although the role of MNCs on local firms has been measured at different industry levels, rural livelihood perspective is unkempt in developing economies. The study aims to identify the roles of MNCs in enhancing the rural livelihoods of the dairy farmers in Sri Lanka. The study was based on an exploratory approach, adopting a qualitative research design with a thematic data analysis. Data were collected through semi-structured interviews from twenty-five dairy farmers who were directly engaging with MNC subsidiaries operating in the rural community of Sri Lanka. The MNC has been able to enhance the rural livelihoods of the dairy farmers through generating of job opportunities and replacing conventional practices with modern technology, identifying the importance of knowledge management, understanding the value of quality, adhering to policies, standards and guidelines along with maintaining proper documentations and improving the return on invested capital. However, the MNC has failed to build strong relationships with local authorities and struggled to promote dairy farming as a main source of income. MNCs exercises in the developing context is still being contended and coordinating and compiling a policy framework with the local authorities could play a significant role in shaping the livelihoods of the dairy farmers. Keywords: Dairy Farming, Multinational Corporation, Rural livelihoods, Corporate Citizenship Cite this paper: Sachin Wijayasinghe, Vilani Sachitra. (2021), Corporate Citizenship Behaviour and Rural Livelihoods: A Study on Multinational Corporations in Sri Lanka, Vidyodaya Journal of Management, 7(1), 81-104

Innovation Initiatives in Large Software Companies: A Systematic Mapping Study

To keep the competitive advantage and adapt to changes in the market and technology, companies need to innovate in an organised, purposeful and systematic manner. However, due to their size and complexity, large companies tend to focus on maintaining their business, which can potentially lower their agility to innovate. This study aims to provide an overview of the current research on innovation initiatives and to identify the challenges of implementing the initiatives in the context of large software companies. The investigation was performed using a systematic mapping approach of published literature on corporate innovation and entrepreneurship. Then it was complemented with interviews with four experts with rich industry experience. Our study results suggest that, there is a lack of high quality empirical studies on innovation initiative in the context of large software companies. A total of 7 studies are conducted in such context, which reported 5 types of initiatives: intrapreneurship, bootlegging, internal venture, spin-off and crowdsourcing. Our study offers three contributions. First, this paper represents the map of existing literature on innovation initiatives inside large companies. The second contribution is to provide an innovation initiative tree. The third contribution is to identify key challenges faced by each initiative in large software companies. At the strategic and tactical levels, there is no difference between large software companies and other companies. At the operational level, large software companies are highly influenced by the advancement of Internet technology. Large software companies use open innovation paradigm as part of their innovation initiatives. We envision a future work is to further empirically evaluate the innovation initiative tree in large software companies, which involves more practitioners from different companies