Organizational learning and project identity in a health and social care partnership

This developmental paper explores the idea that organizational learning in public sector settings is facilitated by localised project identities and inhibited by interventionist bureaucratic reforms. Drawing on a 7-year longitudinal study, the paper shows the organizational integration of previously separate health and social care services was predicated on the ability to cross previously rigid and impermeable institutional, professional and organizational divides. Whilst our findings suggest that organizational learning was predicated on close interaction between key personnel from two provider organizations, we also found that organizational learning was adversely affected by shifting policy priorities and by the power asymmetries that inhered in the broader institutional ecosystem of the NHS

Blame at Work: Implications for Theory and Practice from an Empirical Study

Existing work in the field of business ethics has explored how concepts in philosophy and other disciplines can be applied to blame at work, and considers blame’s potential impact on organisations and their employees. However, there is little empirical evidence of organisational blaming practices and their effects. This article presents an analysis of interviews with twenty-seven employees from a range of occupations, exploring their experience of blame, its rationale and impact. A diversity of blaming practices and perspectives is revealed, and in making sense of these the authors draw on recent theoretical developments—Skarlicki, Kay, Aquino, and Fushtey’s (2017) concept of ‘swift-blame,’ and Fricker’s (2016) notion of ‘communicative blame.’ The study also reveals a tension between a desire to avoid ‘blaming’ on the one hand, and a need for ‘accountability,’ on the other, and the authors explore the implications of the findings for organisations in seeking to ‘manage’ blame

HETEROTIC ESTIMATION AND ADAPTABILITY OF TOMATO HYBRIDS FOR FRUIT YIELD AND ITS RELATED TRAITS IN PAKISTAN.

Current study is emphasizing on the estimation of heterosis for different yield attributing traits and adaptability of tomato hybrids. It was performed in the research area of VCRP, HRI, NARC Islamabad during 2018-2019. Crossing was completed among six parents followed by line × tester. The analyzed data depicted significant differences (P ≤ 0.01) among all the characters.Due to desirable high negative heterotic values hybrids Peto-86 × Nagina and Riograndi × Roma were found suitable for breeding early maturing hybrids. For plant height maximum positive heterosis was observed in Riograndi × Nagina, for no. of cluster/plant in Naqeeb × Roma, for traits like flower cluster-1, fruit cluster-1, fruit length & width and single fruit weight in Naqeeb × Continental while for yield Riograndi × Continental showed maximum heterosis. Therefore among 9 tested hybrids Naqeeb × Continental was found to be highly preferable and recommended for utilization in different breeding programmes

Pressures for sub-supplier sustainability compliance: the importance of target markets in textile and garment supply chains

We propose that sub-supplier sustainability compliance in developing economies' textile and garment supply chains can be more effectively realized by understanding sub-suppliers' target markets. We introduce the concept of sub-suppliers' customer share of production as the share of production that sub-suppliers sell to “exporting” direct suppliers that cater to the international market vis-à-vis “local” direct suppliers that cater to the domestic market. Through this concept and qualitative evidence, we offer a model outlining that as sub-suppliers sell more to exporting direct suppliers, they encounter increased coercive, competitive, and collaborative pressures for sustainability compliance. This article contributes to the multi-tier sustainable supply chain management literature by illustrating how target markets exert pressures for sub-supplier sustainability compliance, and why some sub-suppliers are more inclined to invest in sustainability compliance, some decouple from it, and others invest beyond compliance. We conclude with business strategy guidelines for managers in textile and garment supply chains

Accommodating machine learning algorithms in professional service firms

Machine learning algorithms, as one form of artificial intelligence (AI), are significant for professional work because they create the possibility for some predictions, interpretations and judgements that inform decision making to be made by algorithms. However, little is known about whether it is possible to transform professional work to incorporate machine learning whilst also addressing negative responses from professionals whose work is changed by inscrutable algorithms. Through original empirical analysis of the effects of machine learning algorithms on the work of accountants and lawyers, this paper identifies the role of accommodating machine learning algorithms in professional service firms. Accommodating machine learning algorithms involves strategic responses that both justify adoption in the context of the possibilities and new contributions of machine learning algorithms and respond to the algorithms’ limitations and opaque and inscrutable nature. The analysis advances understanding of the processes that enable or inhibit the cooperative adoption of AI in PSFs and develops insights relevant when examining the long-term impacts of machine learning algorithms as they become ever more sophisticated

Clinical characteristics, management and outcome of major pulmonary embolism: an experience from a tertiary care center in Pakistan.

Objective: To evaluate the clinical characteristics, risk factors, management and outcome of major pulmonary embolism (PE) in a tertiary care center of Karachi. Methods: Medical records of all patients who underwent a spiral CT scan of the chest for suspected pulmonary embolism were reviewed between January 2000 and June 2007 at the Aga Khan University Hospital, Karachi. Patients having evidence of major pulmonary embolism on spiral CT scan were selected. Results: A total of 30 patients (10 males, 20 females) with mean age 52 ± 14.59 years were identified who fulfilled our predefined criteria for major pulmonary embolism. Risk factors for thromboembolism were identified in 22 (73%) patients, prolonged immobilization in 8 (27%) and recent surgery in 8 (27%) patients being the commonest. All patients were symptomatic on presentation. Tachypnea and tachycardia were present in 27 (90%) patients. Refractory hypoxia was present in 18 (60%) patients and 3 (10%) were hypotensive on presentation. On spiral CT scan, 8 (27%) patients had embolus in the main pulmonary trunk, 26 (87%) patients in main right pulmonary artery and 20 (67%) patients had left main pulmonary artery embolus. Echocardiography was done in 22 (73%) patients with the findings of right ventricular dysfunction in all of them. All patients except one were treated with anticoagulation with either heparin infusion or low molecular weight heparin. In addition, thrombolytics were given in 7 (23%) patients and five (17%) underwent surgical embolectomy. Four (13%) patients died during hospitalization with a total of 26 (87%) surviving till hospital discharge. Conclusion: Major pulmonary embolism is an uncommon but potentially life threatening entity. Early diagnosis and aggressive therapy improves the clinical outcome

Accommodating machine learning algorithms in professional service firms

Machine learning algorithms, as one form of artificial intelligence (AI), are significant for professional work because they create the possibility for some predictions, interpretations and judgements that inform decision making to be made by algorithms. However, little is known about whether it is possible to transform professional work to incorporate machine learning whilst also addressing negative responses from professionals whose work is changed by inscrutable algorithms. Through original empirical analysis of the effects of machine learning algorithms on the work of accountants and lawyers, this paper identifies the role of accommodating machine learning algorithms in professional service firms. Accommodating machine learning algorithms involves strategic responses that both justify adoption in the context of the possibilities and new contributions of machine learning algorithms and respond to the algorithms’ limitations and opaque and inscrutable nature. The analysis advances understanding of the processes that enable or inhibit the cooperative adoption of AI in PSFs and develops insights relevant when examining the long-term impacts of machine learning algorithms as they become ever more sophisticated. </jats:p

Bibliometric Mapping of Big Data (BD) in Higher Education (HE): Towards a comprehensive framework

Although big data (BD) has emerged rapidly over the last decade, its importance within higher education (HE) has remained scarce in academic literature. This research aims to develop a comprehensive framework for using big data in HE. We achieved our research objective by conducting a bibliometric analysis of the available literature on BD in HE published in the English language between 2013 -2021. A total of 4312 articles were considered for analysis. Our results showed that most studies focused on the technical specifications of BD, such as data mining and Hadoop. There was a slight reference to the operationality and management functions. However, it is pertinent to note that data privacy, advanced analytics, and machine learning were highlighted as emerging topics. It, therefore, suggests the importance of advanced data analytics and data privacy in establishing a comprehensive framework for managing BD in HE