Mudit Shukla, Divya Tyagi and Sushanta Kumar Mishra
Based on the conservation of resources theory, this study aims to investigate if the fear of career harm influences employees’ knowledge-hoarding behavior. The study further…
Abstract
Purpose
Based on the conservation of resources theory, this study aims to investigate if the fear of career harm influences employees’ knowledge-hoarding behavior. The study further examines felt violation as the predictor of employees’ fear of career harm. The study also explores leader-member exchange as a boundary factor influencing the effect of felt violation on employees’ fear of career harm.
Design/methodology/approach
The data were collected in three waves from 402 professionals working in the information technology industry in Bengaluru, popularly known as the Silicon Valley of India.
Findings
The findings indicate fear of career harm as a critical predictor of employees’ knowledge-hoarding behavior. Moreover, felt violation indirectly impacts knowledge-hoarding behavior by enhancing employees’ fear of career harm. The adverse effect of felt violation was found to be stronger for employees with poor-quality relationships with their leaders.
Practical implications
The study carries important managerial implications as it uncovers the antecedents of knowledge hoarding. First, the human resource department can devise specific guidelines to ensure that the employees are treated the way they were promised. They can also organize training opportunities and mentoring so that the employees’ performance and growth do not get hampered, even if there is a violation. Moreover, such cases should be addressed in an adequate and expedited manner. More significantly, leaders can compensate for the failure of organizational-level levers by developing quality relationships with their subordinates.
Originality/value
The study advances the existing literature on knowledge hoarding by establishing a novel antecedent. Furthermore, it identifies how the employee-leader relationship’s quality can mitigate the adverse effect of felt violation.
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Mudit Shukla, Divya Tyagi and Jatin Pandey
During the COVID-19 pandemic, organizations undertook initiatives such as safety coaching to ensure the safety of their employees and to prevent the spread of the disease…
Abstract
Purpose
During the COVID-19 pandemic, organizations undertook initiatives such as safety coaching to ensure the safety of their employees and to prevent the spread of the disease. However, the question arises if such measures can have a spill-over effect on other important work-related outcomes. Hence, the objective of the current study is to uncover the impact of safety coaching on one such outcome, i.e. work engagement.
Design/methodology/approach
In this study, the authors developed a quantitative model with the help of the social exchange theory. The responses of 250 working professionals captured using a three-wave study were analyzed using the SPSS PROCESS macro.
Findings
The authors found that safety coaching does not directly affect work engagement. It is only when safety coaching is perceived to be effective or appropriate and/or invokes organizational trust that it significantly affects organizational members' work engagement.
Practical implications
This study motivates practitioners to adopt safety coaching by highlighting the benefits that it has to offer beyond safety-related behavior. Moreover, this study discusses mechanisms that can aid organizations in facilitating organizational trust and satisfaction with corporate philanthropic COVID-19 response among employees.
Originality/value
This is one of the first studies that examines the spillover effect of safety coaching on other work-related outcomes. It also uncovers novel antecedents of satisfaction with corporate philanthropic COVID-19 response and organizational trust.
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A zero-day vulnerability is a complimentary ticket to the attackers for gaining entry into the network. Thus, there is necessity to device appropriate threat detection systems and…
Abstract
A zero-day vulnerability is a complimentary ticket to the attackers for gaining entry into the network. Thus, there is necessity to device appropriate threat detection systems and establish an innovative and safe solution that prevents unauthorised intrusions for defending various components of cybersecurity. We present a survey of recent Intrusion Detection Systems (IDS) in detecting zero-day vulnerabilities based on the following dimensions: types of cyber-attacks, datasets used and kinds of network detection systems.
Purpose: The study focuses on presenting an exhaustive review on the effectiveness of the recent IDS with respect to zero-day vulnerabilities.
Methodology: Systematic exploration was done at the IEEE, Elsevier, Springer, RAID, ESCORICS, Google Scholar, and other relevant platforms of studies published in English between 2015 and 2021 using keywords and combinations of relevant terms.
Findings: It is possible to train IDS for zero-day attacks. The existing IDS have strengths that make them capable of effective detection against zero-day attacks. However, they display certain limitations that reduce their credibility. Novel strategies like deep learning, machine learning, fuzzing technique, runtime verification technique, and Hidden Markov Models can be used to design IDS to detect malicious traffic.
Implication: This paper explored and highlighted the advantages and limitations of existing IDS enabling the selection of best possible IDS to protect the system. Moreover, the comparison between signature-based and anomaly-based IDS exemplifies that one viable approach to accurately detect the zero-day vulnerabilities would be the integration of hybrid mechanism.
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Divya Divya, Riya Jain, Priya Chetty, Vikash Siwach and Ashish Mathur
The paper focuses on bridging the existing literature gap on the role of leadership in influencing employee engagement considering the advancement in technologies. With this, the…
Abstract
Purpose
The paper focuses on bridging the existing literature gap on the role of leadership in influencing employee engagement considering the advancement in technologies. With this, the author explores how the three critical elements of service-based companies' business environment-artificial intelligence (AI) success, employee engagement, and leadership are interlinked and are valuable for raising the engagement level of employees.
Design/methodology/approach
A purposive sampling strategy was used to select the employees working in the respective companies. The survey was distributed to 150 senior management employees but responses were received from only 56 employees making the response rate 37.33%. Consequently, an empirical examination of these 56 senior management employees belonging to service-based companies based in Delhi NCR using a survey questionnaire was conducted.
Findings
The PLS-SEM (partial least squares structured equation modelling) revealed that AI has a positive role in affecting employee engagement levels and confirmed the mediation of leadership. The magnitude of the indirect effect was negative leading to a reduction in total effect magnitude; however, as the indirect effect model has a higher R square value, the inclusion of a mediating variable made the model more effective.
Research limitations/implications
This study contributes to extending the existing knowledge of the academicians about the relationship theory of leadership, AI implementation in organizations, AI association with leadership and AI impact on employee engagement. The author extends the theoretical understanding by showing that more integration of AI-supported leadership could enable organizations to enhance employee experience and motivate them to be engaged. Despite its relevance, due to the limited sample size, focus on a specific geographic area (Delhi NCR) and the constraint of only using quantitative analysis, the findings open the scope for future research in the form of qualitative and longitudinal studies to identify AI-supported leadership roles.
Practical implications
The study findings are beneficial majorly for organizations to provide them with more in-depth information about the role of AI and leadership style in influencing employee engagement. The identified linkage enables the managers of the company to design more employee-tailored strategies for targeting their engagement level and enhancing the level of productivity of employees. Moreover, AI-supported leadership helps raise the productivity of employees by amplifying their intelligence without making technology a replacement for human resources and also reducing the turnover rate of employees due to the derivation of more satisfaction from existing jobs. Thus, given the economic benefit and societal benefits, the study is relevant.
Originality/value
The existing studies focused on the direct linkage between AI and employee engagement or including artificial intelligence as a mediating variable. The role of leadership is not evaluated. The leadership enables supporting the easy integration of AI in the organization; therefore, it has an important role in driving employee engagement. This study identifies the contribution of leadership in organizations by providing the means of enhancing employee satisfaction without hampering the social identity of the company due to the integration of AI.
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Zeinab Hosseini, Mohammad Taghi Ghaneian, Mahin Ghafourzade and Abbasali Jafari Nodoushan
This paper aims to evaluate the bioremediation [chemical oxygen demand (COD) and color removal] of the effluent from the cardboard recycling industry in Yazd, central province of…
Abstract
Purpose
This paper aims to evaluate the bioremediation [chemical oxygen demand (COD) and color removal] of the effluent from the cardboard recycling industry in Yazd, central province of Iran, using mixed fungal culture.
Design/methodology/approach
First, the effluent samples from the cardboard recycling industry were cultured on potato dextrose agar medium to isolate native fungal colonies. The grown colonies were then identified using morphological macroscopic and microscopic characteristics to choose the dominant fungi for bioremediations. The mixed cultures of Aspergillus niger, Aspergillus flavus and Penicillium digitatum were finally used for bioremediation experiments of the cardboard recycling industry. A suspension containing 1 × 106 CFU/ml of fungal spores was prepared from each fungus, separately and their homogenous mixture. Sewage samples were prepared and sterilized and used at 25%, 50% and 90% dilutions and pH levels of 5, 7 and 8 for bioremediation tests using mixed fungal spores. Following that, 10 ml of the mixed fungal spores were inoculated into the samples for decolorization and COD removal and incubated for 10 days at 30°C. The amount of COD removal and decolorization were measured before incubation and after 3, 6 and 10 days of inoculation. In this research, the color was measured by American Dye Manufacturer Institute and COD by the closed reflux method. The results of the present study were analyzed using SPSS 21 statistical software and one-way ANOVA tests at p-value < 0.05.
Findings
The results of this research showed that the mean decolorization by mixed fungal culture over 10 days at pH levels of 5, 7 and 8 were 44.40%, 45.00% and 36.84%, respectively, and the mean COD removal efficiency was 71.59%, 73.54% and 16.55%, respectively. Moreover, the mean decolorization at dilutions of 25%, 50% and 90% were 45.00%, 31.93% and 30.53%, respectively, and the mean COD removal efficiency was 73.54%, 62.38% and 34.93%, respectively. Therefore, the maximal COD removal and decolorization efficiency was obtained at dilution of 25% and pH 7.
Originality/value
Given that limited studies have been conducted on bioremediation of the effluent from the cardboard recycling industry using fungal species, this research could provide useful information on the physicochemical properties of the effluent in this industry.
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B. Omkar Lakshmi Jagan and S. Koteswara Rao
Doppler-Bearing Tracking (DBT) is commonly used in target tracking applications for the underwater environment using the Hull-Mounted Sensor (HMS). It is an important and…
Abstract
Purpose
Doppler-Bearing Tracking (DBT) is commonly used in target tracking applications for the underwater environment using the Hull-Mounted Sensor (HMS). It is an important and challenging problem in an underwater environment.
Design/methodology/approach
The system nonlinearity in an underwater environment increases due to several reasons such as the type of measurements taken, the speeds of target and observer, environmental conditions, number of sensors considered for measurements and so on. Degrees of nonlinearity (DoNL) for these problems are analyzed using a proposed measure of nonlinearity (MoNL) for state estimation.
Findings
In this research, the authors analyzed MoNL for state estimation and computed the conditional MoNL (normalized) using different filtering algorithms where measurements are obtained from a single sensor array (i.e. HMS). MoNL is implemented to find out the system nonlinearity for different filtering algorithms and identified how much nonlinear the system is, that is, to measure nonlinearity of a problem.
Originality/value
Algorithms are evaluated for various scenarios with different angles on the target bow (ATB) in Monte-Carlo simulation. Computation of root mean squared (RMS) errors in position and velocity is carried out to assess the state estimation accuracy using MATLAB.