Global journal of Business and Integral Security

Leading with AI for a Sustainable Future

Frank Soans — 2023-12-06

Humans have an innate desire to increase our capability. This has driven us to use technology to enhance our capability. We have not only enhanced our physical capabilities but our cognitive capabilities too and are continuing to expand it further through AI. This constant desire is driving changes in the way we experience the world around us, learn or even the jobs we do. This change can be disruptive, limited by inherent bias, or applied with limited visibility of its impact or the evaluation of the risks it can create. To ensure technological change is desired, designed, and developed with a sustainable outcome, Leadership is crucial. Leading with AI needs to be led by a moral compass of principles that will ensure sustainable change.

Algorithmic Ethics in Augmented Journalism

Fatima Khalid Farid — 2023-12-05

This study delves into the ethical ramifications of Artificial Intelligence (AI) integration in journalism, suggesting strategic frameworks for ensuring ethical and accountable AI usage in media. AI's role in journalism brings forth concerns of accountability, transparency, authorship, and biases (Diakopoulos, 2019; Carlson, 2015; O'Neil, 2016). Through a qualitative methodology, including discourse and content analysis, this research illuminates ethical dilemmas at the crossroads of media ethics and technological determinism (Boczkowski, and Anderson, 2017; Tandoc et. al., 2020). It unveils evolving ethical contours of AI-enhanced journalism, shedding light on broader societal shifts instigated by AI (Floridi et al., 2018; Lermann Henestrosa et al, 2023). The study proposes strategies for responsible AI application in media, contributing to scholarly discourse on media ethics, and providing insights for stakeholders navigating AI’s ethical intricacies in journalism. The findings encourage further discourse on preserving ethical integrity in AI-driven journalism, bolstering public trust and reinforcing journalism’s democratic essence in the digital epoch.

An EU-GDPR-Based Privacy Assurance Framework for Data Processors in the Software Package Implementation Industry in India

Premnath Rajagopalan — 2023-12-05

In the context of global data protection standards, such as the EU-GDPR, India's software package implementation industry encounters distinct challenges. This research delves into the hurdles faced by Indian software organizations when attempting to meet GDPR requirements and explores the alignment between their data processing practices and the mandates of the EU-GDPR and DPDP Act. Given the complexities of bridging European standards and Indian practices, the study aims to craft a privacy assurance framework tailored to India's unique context. This proposed framework will specifically cater to data processors in the software package industry, addressing their dual need to satisfy EU clients while navigating Indian regulatory specifics. Through this research, a deeper understanding of GDPR compliance within the Indian package implementation sector will emerge, offering solutions to foster heightened data protection measures in line with global standards.

Implications of the Globalizing Digital Retailing on Competition and Consumer Protection in COMESA Countries

Steven Kamukama Kabuganda — 2023-11-30

Digital retailing is among the major factors contributing to globalization of markets with a potential of boosting trade flows and transforming developing countries into global players. Despite this potential, developing countries are faced with digital retail competition and consumer protection related constraints which constrain competitiveness, efficient operations of the markets, full utilization of the technologies and optimizing consumer welfare. This situation is further exacerbated by the powerful global players whose practices with cross border effects cannot be easily addressed by countries due to jurisdictional limitations.
This dissertation therefore sought to identify the challenges faced by the countries in the Common Market for Eastern and Southern Africa (COMESA), policy options and implementation approaches needed to address cross border effects of digital retail to promote effective competition and ensure consumer welfare while harnessing the potential of ecommerce. This research followed a comprehensive literature review, formulation of a conceptual framework and research questions, collection of data from experts in selected COMESA countries using questionnaires and face-to-face interviews and conducting systematic data analysis to generate conclusions and recommendations.
The findings reveal that digital retail, aided by technology including complex algorithms and artificial intelligence, entails specific characteristics which enable platforms to gain economies of scale and scope, market power, winner-takes-all effects, network effects, lock-in effects and ability to operate in multiple or multisided markets which may enhance market concentration and
encourage practices with adverse effects on the market ecosystem. Further, the study reveals that COMESA countries are faced with digital retail associated anticompetitive practices and conducts that lower consumer welfare particularly in merger control, detection of cartels, vertical restraints, abuse of market power along with other unilateral restraints and consumer protection concerns. Furthermore, the study establishes that existing laws of COMESA countries are limited in scope to effectively address digital retail specific concerns, especially conducts that increase concentration, reduce contestation, or diminish consumer welfare.
This dissertation concludes that COMESA countries may need to enhance their institutional and legal framework, including the regional law to effectively regulate digital retail, address jurisdictional limitations and cross border effects on the Market.

Ecosystem Development Framework for Enterprise Data Science & Research Projects

Shitalkumar Rajendra Sukhdeve — 2023-11-30

This research examines the development of an enterprise ecosystem for data science and research projects to unleash the full potential of data and foster innovation. The study proposes a comprehensive framework encompassing objective setting, firm performance evaluation, capability development, and strategic alignment. Industry experts and data science leaders from telecom, e-commerce, and financial sectors form the sample for interviews, which serve as the primary data collection method. Grounded theory is utilized for data analysis and theory development. The findings underscore the significance of objective setting, performance evaluation, capability development, and strategic alignment in optimizing processes and achieving superior outcomes. Organizations can cultivate innovation and gain a competitive edge by adopting this framework. The research contributes to effective data-driven decision-making and offers valuable insights for businesses operating in today's dynamic landscape.

Real-Time Revenue Assurance Automation Technology: Factors Influencing Its Adoption by Retail Banks in Cameroon

David Tatang Ghogomu — 2023-11-30

Loan interest revenue of retail banks in Cameroon and the world at large constitutes a
material revenue source respectively amounting to billions in local currency (up to XAF 82 billion) and US dollars. It further has the particularity that it is a derived revenue which
lends itself, in its detennination, to human intervention thus susceptible to fraud that stack up to substantial financial losses to retail banks in Cameroon.
Additionally, recent technological advances such as computer, broadband networking,
internet, mobile telephone, Enterprise Resource Planning, sensors and extensible business reporting language which when combined are capable of initiating and finalizing bank
transactions almost instantaneously on a global reach thereby require faster management decision-making.
Against this backdrop, this study purposefully and empirically investigated the
factors influencing the adoption of real-time revenue assurance automation technology(RT-RAA T) by retail banks in Cameroon using the theory of reasoned action (TRA) and extended by culture.
Designed as an applied, explanatory, deductive, quantitative and cross-sectional research, self-administered questionnaires were used to collect primary data from a sampling frame of sixteen retail banks in Cameroon using simple random sampling which resulted in 135 respondents after respecting stratification criteria.
Microsoft Excel and GNU PSPP Statistical Analysis Software were used for data analysis.
The results found that attitude and subjective nonn, two of the constructs of the theory of reasoned action, Hofstede's (1994) national culture and its uncertainty avoidance dimension and market culture type of organizational culture were the significant factors influencing intention to use RT-RAAT withfo retail banks in Cameroon. The TRA singularly explained 60% of the variation in intention to use RT-RAA T compared to TAM at 40% in other studies. When extended by culture, it explained 81 % of variations in ITU.
The study respectively mapped national and organizational culture landscapes of Cameroon and its retail banks. It found Cameroon to be high on three of the four cultural dimensions of Hofstede's (1994) national culture index scale relative to world average and the dominant organizational culture of the retail banks to be hierarchy culture based on Cameron and Quinn's (I 999) OCAI framework.
The study was pioneering in its recommendation for preliminary predictions of technology adoption using Ji near models, such as proposed in this study, to be incuded as one of the key activities of the feasibility study stage of technology adoption generally and RT-RAAT in particular.

Aligning Emotions, Thoughts, and Feelings to Build a High-Performing Team

Monika Das — 2023-11-30

Creating a high-performing team is a goal shared by many organizations, as it directly impacts productivity, innovation, and overall success. This study aims to explore the alignment of emotions, thoughts, and feelings within teams and its impact on team performance. Specifically, the research focuses on the integration of emotional intelligence (EI), neuro-linguistic programming (NLP) techniques, and the establishment of psychological safety within the team. The research design involved a mixed-method approach, including surveys and interviews, conducted with a newly formed IT team comprising 28 members. The participants were divided into a test group and a control group, with data collected over a period of 10 weeks. The findings indicate a significant positive correlation between emotional intelligence and team effectiveness. The application of NLP techniques led to improvements in specific dimensions of emotional intelligence, such as rapport building, empathy, self-awareness, self-regulation, and psychological safety. Moreover, there was a strong positive correlation between psychological safety and team effectiveness. These results highlight the importance of aligning emotions, thoughts, and feelings within a team to foster high performance. By developing emotional intelligence skills and utilizing NLP techniques, teams can enhance communication, collaboration, and problem-solving. Creating an environment of psychological safety further contributes to team cohesion and productivity. The implications of this study are valuable for organizations seeking to optimize team dynamics and performance. By investing in the development of emotional intelligence skills, providing training in NLP techniques, and promoting psychological safety, organizations can cultivate high-performing teams. Future research is recommended to explore these concepts in different organizational contexts and diverse team compositions.

Modern Work Structure in Consulting - A Guide for Management Consultants to Address Their Clients’ Changing Work Structure

Lina Christensen — 2023-11-30

The purpose of this research is to provide a guide for how management consultants should engage
with their clients as companies are redefining their future operations with remote and in person
work. This research will utilize existing employee engagement studies and business psychology
literature, as well as a qualitative analysis of interviews with 25 Accenture consultants from 5
different career levels. While existing research about employees’ expectations about their work
structure is mapped to the psychological benefits and draw backs of these structures, there is
limited research about the effects of these on third party contractors like management consultants.
This research focuses on addressing that missing piece.

Deployable Explainability for NLP Use Cases in E-commerce

Vasarla Avinash — 2023-11-30

This dissertation examines deployable explainability for NLP use cases in e-commerce using Amazon Musical Instrument Reviews. As NLP models are utilised for sentiment analysis, product recommendation, chatbots, and e-commerce text classification, transparency and interpretability are becoming more crucial. We examine the pros and cons of explainability techniques in NLP models for e-commerce applications using Amazon Musical Instrument Reviews.
The literature review explains why natural language processing requires explainability and evaluates existing techniques. The Amazon Musical Instrument Reviews use case illustrates how explainable NLP may affect online company processes.
Additionally, a methodical plan for integrating explainability approaches into NLP models for e-commerce is provided. Collecting data, training a model, including an explainability technique, and evaluating performance are all part of this process. The benefits of deployable explainability are demonstrated using an Amazon Music Instrument Reviews analysis. The investigation includes such elements as data gathering, models for natural language processing, and explainability strategies. The evaluation of performance considers the model's precision and the clarity of the system's explanation.
The thesis addresses interpretability-performance trade-off, scalability, user acceptability, and ethical problems related to explainability in NLP models. These issues are addressed by simplifying and standardizing explainability approaches, boosting scalability and efficiency, resolving user acceptance and trust concerns, and ensuring ethical use of explainable NLP models.
This thesis sheds light on the challenges and opportunities of incorporating transparency and interpretability into NLP systems, contributing to deployable explainability in ecommerce NLP use cases. This study uses Amazon Musical Instrument Reviews to demonstrate how explainable NLP models may improve user confidence, decision-making, and regulatory compliance in e-commerce platforms.
Finally, the thesis emphasises the necessity to create and apply deployable explainability methodologies to a wide range of e-commerce use cases outside Amazon Musical
Instrument Reviews.

Global Semiconductor Shortage Triggering Obstructions and Production Delays

Arnab Chakraborty — 2023-11-30

The global COVID-19 pandemic served as an enormous shock that severely
disrupted the delicate supply-demand balance in semiconductor supply chains.
Lockdowns and travel restrictions temporarily halted production at overseas
chip fabrication facilities. This reduced supply was misaligned with a
sudden spike in semiconductor demand as consumer electronics purchases
surged while working and learning from home became widespread. However,
the automotive industry had drastically scaled back chip orders earlier
in the pandemic as car sales plunged. This mismatch resulted in acute
semiconductor shortages for automakers as production rebounded quicker
than anticipated amidst chip supply rigidity. The research analyzed this complex
interplay through extensive literature review, data analysis, and forecast
modeling.
The study synthesized perspectives from prior work examining supply
chain vulnerabilities and automotive industry impacts. A thematic analysis
identified key drivers including pandemic disruptions, supply-demand mismatches,
manufacturing concentration risks, automotive priority gaps, and
macroeconomic shifts. Financial impacts on major automakers were also reviewed.
An original dataset on country-level automotive production over
22 years provided empirical insights. Descriptive analysis evidenced dramatic
declines in 2020 output across all major producing economies from
pandemic disruptions. China maintained production leadership despite the
crisis. Sophisticated forecasting using LSTM neural networks was conducted
to predict recovery timeframes and enhance preparedness.
Key implications highlighted supply chain resilience strategies and collective
action as imperative for managing systemic shortages. The deepening
necessity and complexity of semiconductor supply chains poses risks
from over-optimization without agile buffers. For the automotive industry,
rethinking innovation roadmaps, increasing visibility, strategic stockpiling,
and creative engineering is needed to navigate disruptions. The research limitations
around constrained forecasting, conceptual materials analysis, aggregated
data usage, and geographically limited literature review were acknowledged.
Recommendations included addressing these gaps through
expanded techniques, proprietary data access, and globally diverse information
sources. Overall, the study contributed timely perspectives to aid stakeholders
across electronics supply chains seeking to enhance resilience while
upholding rapid innovation. The insights generated can help the automotive
and semiconductor industries be better positioned to navigate future crises.