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This paper proposes a novel approach to impose the Neumann boundary condition for isogeometric analysis (IGA) of Euler–Bernoulli beam with 1-D formulation. The proposed method is for only IGA in which it is difficult to handle the Neumann boundary conditions. The control points of B-spline are equivalent to nodes in finite element method. With 1-D formulation, it is not possible to accommodate multiple degrees of freedom in IGA. This case arises in the analysis of beams. The paper aims to propose a way to work around this issue in a simple way.

Neumann boundary conditions, which are even-order derivatives (example: double derivative) of the primary variable, are inherently satisfied in the weak form. Boundary conditions with an odd number of derivatives (example: slope) are imposed with the introduction of a new penalty matrix.

The proposed method can impose a slope boundary condition for IGA of a beam using 1-D formulation.

From the literature, it can be observed that the beam is formulated in 1-D by considering it as either a rotation-free element or a 2-D formulation by considering shear strain along with the normal strain. The work represents 1-D formulation of a beam while considering the slope boundary condition, which is easy and effective to formulate, compared with the slope boundary conditions reported in previous works.

The purpose of this study is to compare the classification learning ability of our algorithm based on boosted support vector machine (B-SVM), against other classification techniques in predicting the credit ratings of banks. The key feature of this study is the usage of an imbalanced dataset (in the response variable/rating) with a smaller number of observations (number of banks).

In general, datasets in banking sector are small and imbalanced too. In this study, 23 Scheduled Commercial Banks (SCBs) have been chosen (in India), and their corresponding corporate ratings have been collated from the Indian subsidiary of reputed global rating agency. The top management of the rating agency provided 12 input (quantitative) variables that are considered essential for rating a bank within India. In order to overcome the challenge of dataset being imbalanced and having small number of observations, this study uses an algorithm, namely “Modified Boosted Support Vector Machines” (MBSVMs) proposed by Punniyamoorthy Murugesan and Sundar Rengasamy. This study also compares the classification ability of the aforementioned algorithm against other classification techniques such as multi-class SVM, back propagation neural networks, multi-class linear discriminant analysis (LDA) and k-nearest neighbors (k-NN) classification, on the basis of geometric mean (GM).

The performances of each algorithm have been compared based on one metric—the geometric mean, also known as GMean (GM). This metric typically indicates the class-wise sensitivity by using the values of products. The findings of the study prove that the proposed MBSVM technique outperforms the other techniques.

This study provides an algorithm to predict ratings of banks where the dataset is small and imbalanced. One of the limitations of this research study is that subjective factors have not been included in our model; the sole focus is on the results generated by the models (driven by quantitative parameters). In future, studies may be conducted which may include subjective parameters (proxied by relevant and quantifiable variables).

Various stakeholders such as investors, regulators and central banks can predict the credit ratings of banks by themselves, by inputting appropriate data to the model.

In the process of rating banks, the usage of an imbalanced dataset can lessen the performance of the soft-computing techniques. In order to overcome this, the authors have come up with a novel classification approach based on “MBSVMs”, which can be used as a yardstick for such imbalanced datasets. For this purpose, through primary research, 12 features have been identified that are considered essential by the credit rating agencies.

The paper aims to relocate discussions on police stops and police interactions from the Anglophone world to the particularistic context of the post-colonial state of India. The paper further frames the everyday policing practices in a theoretical dialog between questions of legitimacy, accountability and tolerated illegalities. For that purpose, the author contextualizes the discussion in the post-colonial state of India, in the jurisdictions of two police stations (PSs), in the National Capital Territory of Delhi and the State of Kerala.

The author conducted ethnographic studies in one station each in Kerala and Delhi, India, from February to July 2019 and July 2019 to January 2020, respectively. The study mapped everyday power relations as the relations manifested within the site and jurisdiction of the PSs.

Through the research, the author found that to fully understand everyday practices of policing, especially police interactions and police stops, one must contextualize the police force within the administrative power-sharing relations, police force's accountability structures, legal procedures and class dynamics, which mark the terrain in which personnel function. In that terrain, the author found that the dialog between particularistic legitimacy, accountability and tolerated illegalities offered an important framework to interpret the everyday policing practices.

Through the paper, the author seeks to expand the analysis of ethnographic descriptions of policing by contextualizing them in the political economy of the state. In doing so, the author aims to provide a framework through which police interactions in post-colonial India could be understood

Social enterprises are organizations striving to address social issues. These enterprises sell products to be self-sustainable. Social entrepreneurial success depends on the intention of consumers to purchase social enterprise products. This study aims to assess and understand the consumer intention to buy social enterprise products.

This descriptive study integrates consumers’ emotional values with the theory of planned behaviour as a framework. The intention is to investigate the role of customer emotion in mediating the relationship between predictor and predicted variables. A mixed sampling technique is adopted to select sample units from the consumers with a consideration for regional balance. Data from 336 respondents were collected using a survey instrument administered online.

Structural equation modelling shows that among the three predictor variables, only attitude has a direct impact on consumer’s purchase intention, while subjective norms have an indirect impact through the mediating variable (emotional value). Behavioural control showed no effect on consumer’s purchase intention of products sold by social enterprises.

The results of this study have theoretical as well as practical implications. The success of social enterprises depends on the acceptance of their products by consumers. The social enterprise can strategize its marketing communications to strengthen the emotional values, attitudes and subjective norms of the consumers so that the market acceptance for these products can be increased.

Popularity in knowledge management has, unfortunately, not been matched by parallel empirical research on the processes, challenges and benefits of knowledge capture in small and medium enterprises (SMEs) in the construction industry, given the fact that 99 per cent of firms in the UK construction industry can be classified as SMEs. This paper aims to discuss the output of a research study, which is focused on knowledge capture in SMEs in construction industry. The paper also aims to present and discuss a computer‐based awareness tool on knowledge capture underpinned by Kolb's experiential learning theory.

The empirical study involved a total of 51 professionals from 26 SMEs in the construction industry. Grounded theory approach was adopted. Also, a content analysis was considered.

The results show that there is lack of awareness of complex issues associated with an effective knowledge capture process as well as ensuing benefits for SMEs in the construction industry. The effective implementation of knowledge capture in SMEs is partly dependent on the vision and flair of the owner/partners of the organisation. It is also determined by culture, structure, people, finance and technology, which warrants a coherent and structured approach.

A computer‐based awareness tool which is underpinned by Kolb's experiential learning theory.

Fostering employee engagement in large organizations is a formidable problem that gets even more challenging in a sluggish economy, when the standard lever of monetary incentives are not a viable option for boosting employee engagement and motivation. As the organization gets larger, building emotional connectedness or bonding becomes challenging as teams expand to operate in different time zones. The overwhelming pace of work in the modern workplace can also hamper bonding. Yet emotional connectedness, when present, serves as a catalyst in driving superior performance and employee loyalty. The culture of many large organizations discourages innovation and out-of-the-box thinking because their institutional structures encourage risk aversion. Even though large organizations are best positioned to absorb the ups and downs of intelligent risk-taking, their talent processes enforce conformity, legitimize mediocrity and penalize failed attempts at innovative thinking. Performance appraisals tend to promote employees who take the path of least resistance. Managers, of course, help perpetuate this risk-averse cycle of mediocrity. Either they have been conditioned to think only in a linear fashion or organizational systems perpetuate managerial insecurity at all levels. This insecurity manifests in several ways: managers may take credit for the work performed by a subordinate; shoot down ideas a subordinate may have; or deflect opportunities that a subordinate may get. Survival in such an environment is based on being average and staying within the system. As a result, the spirit of entrepreneurship is lost. The authors designed a creative and playful contest called “Maverick” to tackle employee engagement in large organizations. The contest deeper goals include: shifting culture and behavior, talent discovery, brand building and meaningful engagement. The impact of the program on a broader organizational culture parameters were assessed through a survey. The survey results validate the impact of the program.

The paper develops a conceptual approach that underlies the design of the Maverick program. Surveys were deployed to determine the perceived impact of the program on the broader culture.

The secret ingredient in employee engagement is gaining the “emotional share of wallet” of employees to drive meaningful, enduring organizational change. Emotional wallet share is the sweet spot that lies at the intersection of employees’ skill sets, their aspirations and the value they generate for the organization. Proactively identifying the sweet spot empowers an organization to capture employees’ emotional wallet share to identify enablers and catalysts that can unlock motivation and performance. The survey results indicate that the Maverick contest was perceived to have a positive impact on all the identified attributes. This is a testament to the program’s success as a pivotal driver of a positive organizational culture. Further, it validates that the Maverick contest identifies several levers that leaders can use to positively influence organizational culture.

The organizations can adapt the proposed conceptual framework in designing meaningful programs to tackle employee engagement and motivation.

The paper provides a meaningful framework to tackle employee engagement in large organizations. The Maverick approach is of interest to leaders of large organizations that are struggling to increase employee engagement with limited resources and that wish to foster creativity to drive innovation. The program offers a compelling way for talented professionals to meaningfully contribute to their organization that is agnostic to their position in the hierarchy. It gives employees the freedom to strive without being paralyzed by fear of failure; the chance to build their personal brand and pride; and a safe environment in which they can question received wisdom and attempt an unconventional approach to problem-solving. It creates a playful environment to bust stress, foster innovation and encourage an entrepreneurial mindset.

This paper offers a superior alternative to the standard gamification solutions that are routinely applied to business situations. Gamification mechanics work effectively in roles that are transactional, instead of roles that demand autonomy, mastery and a sense of purpose. Maverick program is designed while being mindful of the intrinsic motivation of the professionals.

The conventional talent management approach views human relationships as transactional, akin to the commodities that traverse through the supply chain maze. In short, there is a quid pro quo relationship between wage and services, depriving any role for other non‐monetary influences on this relationship. This naïve view of human behavior has distracted the fundamental purpose of talent management – to unlock the value of talent to organizations. Two fundamental drivers that have challenged this transactional view espoused by HR include technology and advances in neuroscience. The technological advancements have created a demand for highly skilled professionals who value autonomy and meaningful engagement. This has brought employee engagement within the focus of managers – a topic that had less significance in earlier decades. The transactional view of workplace relationships has been challenged by discoveries of human behavior by neuroscience. Human beings are wired to have emotions and perceptions, and a workplace is no exception. Reframing the issue through a simple‐yet‐powerful framework, fundamentals of talent management can be restored, paving the way for a meaningful design of organizations. This paper seeks to address these issues.

Using rigorous in‐depth secondary research about current talent practices, the report offers a novel framework to unlock the drivers of employee's motivation and performance. The framework serves as a diagnostic leadership tool to identify breakdowns and foster a meaningful conversation to restore the organization back to equilibrium. A holistic alternative that is agnostic to the rank of the employee, job role, and geography offers promise over the current practice of dealing with employee issues in fragmented manner.

The proposed framework helps identify the sweet‐spot that lies at the intersection of three fundamental drivers; employee's preferences on the type of work, employee's core competency and activities that are value‐adding to the organization. The sweet‐spot is the employee's emotional wallet that the organizations must proactively capture to unlock the true drivers of motivation and performance. The proposed framework serves as a diagnostic tool to meaningfully tackle breakdowns and restore organizations to equilibrium. The sweet‐spot provides the clue to design an effective organizational structure, identify the enablers and catalyst that can unlock employee motivation and performance.

There is a compelling need for today's organizations to refocus their energies to unlock the value of their talent to drive higher performance and motivation. Deploying the proposed framework will empower organizations to capture the “share of emotional wallet” that is critical to drive higher levels of employee engagement and motivation. Smarter organizational structures and job role can be meaningfully designed.

The proposed framework challenges conventional talent management views of human relationships as transactional akin to the commodities that traverse through the supply chain maze. This blind spot has deprived the organizations in unlocking the drivers of employee motivation and performance. Overcoming this blind spot empowers talent management to capture the emotional share of wallet instead of trying to perfect the delivery supply chain.

Despite new organizational complexities, the fundamental focus for talent management is to unlock the value of its resource. Despite the pristine appeal of this fundamental tenet of talent management, it is ironic that HR has drifted its focus from its core. Reframing the issue through a simple‐yet‐powerful framework, fundamentals of talent management can be restored, paving the way for a meaningful design of organizations. This is a paradigm shift for talent management to get back to basics of what really matters to the organizations.

The present study spotlights the single and multicriteria decision-making (MCDM) methods to determine the optimal machining conditions and the predictive modeling for surface roughness (Ra) and cutting tool flank wear (VB) while hard turning of AISI 4340 steel (35 HRC) under dry environment.

In this study, Taguchi L16 design of experiments methodology was chosen. The experiments were performed under dry machining conditions using TiSiN-TiAlN nanolaminate PVD-coated cutting tool on which Taguchi and responses surface methodology (RSM) for single objective optimization and MCDM methods like the multi-objective optimization by ratio analysis (MOORA) were applied to attain optimal set of machining parameters. The predictive models for each response and multiresponse were developed using RSM-based regression analysis. S/N ratios, analysis of variance (ANOVA), Pareto diagram, Tukey's HSD test were carried out on experimental data for profound analysis.

Optimal set of machining parameters were obtained as cutting speed: at 180 m/min., feed rate: 0.05 mm/rev., and depth of cut: 0.15 mm; cutting speed: 145 m/min., feed rate: 0.20 mm/rev. and depth of cut: 0.1 mm for Ra and VB, respectively. ANOVA showed feed rate (96.97%) and cutting speed (58.9%) are dominant factors for Ra and VB, respectively. A remarkable improvement observed in Ra (64.05%) and VB (69.94%) after conducting confirmation tests. The results obtained through the MOORA method showed the optimal set of machining parameters (cutting speed = 180 m/min, feed rate = 0.15 mm/rev and depth of cut = 0.25 mm) for minimizing the Ra and VB.

This work contributes to realistic application for manufacturing industries those dealing with AISI 4340 steel of 35 HRC. The research contribution of present work including the predictive models will provide some useful guidelines in the field of manufacturing, in particular, manufacturing of gear shafts for power transmission, turbine shafts, fasteners, etc.