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Approaches for the systematic evaluation of nursing practice are outlined. Practical methods and steps in auditing care at ward and individual patient level are discussed. Standard‐setting at ward level is systematically described and examples given. Monitoring and measuring outcomes and individual patient unit (IPA) are also described.

This paper aims to create empirical evidence regarding shared value strategies recently propagated by Michael Porter and Mark Kramer.

The authors analyze a single case study of a collaboration between BASF, André Maggi Group and Fundação Espaço Eco in Brazil. The objective is to evaluate whether the applied strategy can be considered as a case of shared value creation.

The case study on the collaboration between BASF, FEE and the André Maggi Group does qualify as a shared value strategy, more precisely as a case of redesigning productivity in the value chain.

This single case study creates some evidence for shared value strategies; however, more research is needed to generalize the results.

The socio‐eco‐efficiency analysis offered by Fundação Espaço Eco creates a differentiation strategy for BASF in Brazil. The work enables BASF's clients to reduce negative impacts while increasing their financial, social and environmental performance.

This paper is the first empirical verification of the shared value concept. It demonstrates that shared value strategies do enhance financial as well as socio‐environmental performance and build stronger client relationships.

The purpose of this paper is to study whether the controversy because of brand crisis based on quality had any impact on consumer brand equity, brand image, brand reliability, brand perception of quality, perceived value, brand sentiments and purchase behavior.

The research methodology consisted of two types of data: primary and secondary data. The secondary research consisted of social media brand sentiments and financial analysis. The primary research focused on perception study of brand quality, consumer brand equity, brand reliability, brand image, purchase and brand switch behavior.

Maggi used social media extensively to address the issue and re-build the brand reliability and confidence among its users. Communication strategy adopted by focusing on the past experience of consumers and using them as a spokesperson generated a positive sentiment towards the brand under crisis.

While Maggi suffered the backlash because of the controversy across the length and breadth of India and was banned in a number of states, the author could conduct the primary research only in one city of the state of Maharashtra, Mumbai. The effectiveness of the survey was impacted because of the geographic limitations the author faced while collecting the responses. The survey would have definitely been more effective, with responses collected from different states and with more number of respondents. Fishbein is very old, from the 1980s, even though this theory has met the test of time. Application of the effect of experiences on experiential perceptions and how this influences value through networking could have been used to explain the same.

An important implication of this paper’s findings for practice, therefore, is that brands should incessantly strive to maintain the consumers’ level of trust, as it is essential for the preservation of the brand equity after a crisis. Crisis-stricken brands should safeguard their reputations from the negative effects of crises. It is even more important for any brand to act appropriately when the cause of the crisis is attributed to its actions and processes. Managers have to address the quality of products in case of brand crisis for restoring trust, image and reliability in the brand. Right type of communication to right targeted consumers will help in the restoration of the image, trust on the brand and bring back loyal customers. Managers have to build brand equity on a regular basis, as a strong brand can recover faster as seen from this paper.

This paper helps to upgrade the knowledge and understanding of the impact of the controversy on brand equity and image and how the crisis management strategy can be adopted to regain the mind share and equity. This paper will help the brands in the future to know how a crisis can be managed efficiently by drawing a cue from the strategies implemented by Maggi.

The purpose of this paper is to examine combinations of contract clauses in order to ascertain which combinations correlate to high operational performance (OP).

Two hypotheses were formulated from contracting theory and tested on data collected from 45 projects. Fuzzy set qualitative comparative analysis was used and validated with multiple regression and simulation.

The hypotheses were tested to determine whether combinations of classical, relational, and/or associational contract clauses correlate to high OP. The results show that whereas high OP correlates to combinations of relational and associational contract clauses, classical and relational clauses should not be combined.

Directions are proposed to guide future research in order to produce a more nuanced testing of contractual complementarity.

The managerial implications of the findings include a more thorough understanding of the use of contract clauses and of which clauses managers should combine to achieve high OP.

This study contributes to the theory of contractual incompleteness and complementarity, specifically in the context of project contracting. The analysis produced two theoretical implications: first, that better performing contracts are created when combining relational and associational contract clauses; and second, that in projects, relational and classical contract clauses are not complementary with regards to realizing high OP.

Purpose: This chapter aims to examine machine learning (ML) models for predicting credit card fraud (CCF).

Need for the study: With the advance of technology, the world is increasingly relying on credit cards rather than cash in daily life. This creates a slew of new opportunities for fraudulent individuals to abuse these cards. As of December 2020, global card losses reached $28.65billion, up 2.9% from $27.85 billion in 2018, according to the Nilson 2019 research. To safeguard the safety of credit card users, the credit card issuer should include a service that protects customers from potential risks. CCF has become a severe threat as internet buying has grown. To this goal, various studies in the field of automatic and real-time fraud detection are required. Due to their advantageous properties, the most recent ones employ a variety of ML algorithms and techniques to construct a well-fitting model to detect fraudulent transactions. When it comes to recognising credit card risk is huge and high-dimensional data, feature selection (FS) is critical for improving classification accuracy and fraud detection.

Methodology/design/approach: The objectives of this chapter are to construct a new model for credit card fraud detection (CCFD) based on principal component analysis (PCA) for FS and using supervised ML techniques such as K-nearest neighbour (KNN), ridge classifier, gradient boosting, quadratic discriminant analysis, AdaBoost, and random forest for classification of fraudulent and legitimate transactions. When compared to earlier experiments, the suggested approach demonstrates a high capacity for detecting fraudulent transactions. To be more precise, our model’s resilience is constructed by integrating the power of PCA for determining the most useful predictive features. The experimental analysis was performed on German credit card and Taiwan credit card data sets.

Findings: The experimental findings revealed that the KNN achieved an accuracy of 96.29%, recall of 100%, and precision of 96.29%, which is the best performing model on the German data set. While the ridge classifier was the best performing model on Taiwan Credit data with an accuracy of 81.75%, recall of 34.89, and precision of 66.61%.

Practical implications: The poor performance of the models on the Taiwan data revealed that it is an imbalanced credit card data set. The comparison of our proposed models with state-of-the-art credit card ML models showed that our results were competitive.