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Devising a Digital Marketing Measurement Framework for Dove and Tresemme
No full textThe project is a synthesis of two applied concepts. It draws information on case studies and analysis of data to come up with a Data Collaboration & Governance Handbook in the context of digital marketing at Unilever Pakistan for the brands Dove and TRESemmé. Analysing such campaign situations as typical and identifying typical trends in data flows, quality problems, communications across teams, this work reveals pronounced principles to structure roles, duties, naming, and collaboration procedures.
The deliverable gives the leadership and teams simple rules to follow that could include elements such as data-sharing protocols, quality-check procedures, documentation schedules, and communication frequencies, without creating dashboards or conducting technical installations. The presentation to follow provides practical templates and governance routines, as well as illustrated examples based on cases analysed, to make sure that reliable insights and wise decisions are performed during the upcoming digital initiatives
Isolation Forest for Attrition Analysis
No full textThis Experiential Learning Project (ELP), undertaken by a team of final-year undergraduate students at the Institute of Business Administration (IBA), Karachi, was conducted in collaboration with Habib Bank Limited (HBL). The primary objective of the project was to assist HBL in developing a machine learning-based system to support employee attrition analysis. The deliverable comprised a fully operational Python-based code capable of aiding in predictive analysis for attrition using historical data, thereby enabling HBL to identify potential retention risks and strengthen workforce management strategies. The data for the project was provided by HBL under strict confidentiality agreements and was accessed exclusively on-site due to the sensitive nature of employee information. The dataset included records of employees who had exited the organization during 2023, 2024, and early 2025. Key variables included demographic information, job function, grade, performance and value ratings, and the reason for exit. Importantly, the dataset did not include records of retained employees, necessitating the use of an unsupervised machine learning approach. The team employed the Isolation Forest algorithm, an anomaly detection technique designed to identify data points that deviate significantly from the norm. The model was trained on the cleaned and standardized dataset and configured to flag the top 10% most anomalous exits based on their profiles. These anomalies were interpreted as employee exits that were unusual in comparison to others in the dataset. Integration of the model with preprocessing logic allowed the code to operate independently on future internal datasets without requiring manual data cleaning. A post hoc statistical analysis was conducted on the anomaly scores using one-way ANOVA and Tukey’s HSD test to determine whether the ‘Leave Reason’ field was significantly associated with anomaly status. The results revealed that departures tied to organizational issues, such as internal grievances or dissatisfaction with policies, were more likely to be classified as anomalous. These findings suggest that certain categories of attrition may serve as early warning signals of internal dysfunction or policy gaps. All statistical assumptions required for valid ANOVA were tested and satisfied, adding robustness to the results. Furthermore, the project addressed the ethical considerations of using machine learning in HR contexts, including transparency, bias, interpretability, and responsible data handling. Overall, the project demonstrates that data-driven approaches can enhance attrition diagnostics, even when constrained by partial datasets. The final codebase allows HBL to apply anomaly detection to future exit data, enabling proactive responses to atypical departures. Beyond technical success, the project also contributes to Sustainable Development Goal 8 (Decent Work and Economic Growth) by supporting more strategic, fair, and evidence-based HR practices. It aligns with the Behavioral Studies thought leadership area at IBA by combining psychological theories, statistical modeling, and artificial intelligence to address real-world organizational challenges
Market Influence to use AI on the Operational Performance of Small-Scale Food Restaurants: A Quantitative Study Based upon Technology Acceptance Model (TAM)
No full textMarket Influence to use AI on the Operational Performance of Small-Scale Food Restaurants: A Quantitative STUDY Based Upon TECHNOLOGY ACCEPTANCE MODEL (TAM)
Background:
This study is one of the initial studies that will relate use of AI with the increase in the operational performance of small Businesses through complying the postulates of TAM Model. Previously the model has rarely been used with the small businesses. However, the effect of external factors like technological requirement is always associated with use of TAM in the research. Therefore, this study is developed to leverage further research in the domain of marketing and research.
Problem:
Previous studies conducted in this domain are based upon developed and western sides of the world. Hence, this is one of the prime studies that focused extensively upon Asian markets. Similarly, previous no studies from Pakistan uses TAM model for understanding of operational performance of small businesses.
Methodology:
The philosophy of research accompanied in this study is epistemology, the philosophical stance is post-positivism and research strategy is survey. The purpose of research is correlational, study setting was non-contrived and unity of analysis is individual in nature in order to collect data from business owner using AI for the growth and better performance of their business. Thus, the data can not be collected from mass population. Therefore, in order to make the findings of this study effective and valued for masses this study collected data through quota sampling and sample size for the study is 100 respondents.
Analysis:
Analysis has been made through using structural equation modeling through SMART-PLS. The model of this study is higher level reflective-reflective model that reflects adequately upon the reliability and validity of the data. Empirical findings of the study indicated that the use of AI is important for the growth of business and it is also beneficial for the increase in operational performance of small businesses.
Limitations:
This study also has some theoretical and practical limitations as the study has been focused on small scale food restaurants. Hence, further studies may be conducted in understanding use of AI for other industries like textile, fabric and transportation.
Practical Implications:
is study is also conducted to make readers understand the use of AI for operational performance. Therefore, this study would act as the base of effective policy making to assess operational performance. Moreover, this study will also be providing better understanding of use of AI for small food businesses as most of the times studies are focused upon large-scale business. Hence, this unique point will aid the performance and significance of this study to masses
Social Implications:
This study is also important to make readers use AI for increase of social factors related with restaurant business through applying the theories of motivation and consumer behavior models.
Key Words: Artificial Intelligence, Small Businesses, SMEs, Operational Performance, Market, Food Restaurants, Technology Acceptance Model (TAM) & AI adoptio
Mastering gamified assessments: Tips to ace the Game and Land Your Dream Job.
No full textMastering gamified assessments: Tips to ace the Game and Land Your Dream Job.
The session provides an informative insight from the top 10 performers at the talent games.
Gamified assessments use game-based tools to evaluate candidates’ skills, personality, and problem-solving engagingly. Companies increasingly adopt them for enhanced engagement, deeper insights, scalability, and fairer hiring
Enhanced multigrid solver for anisotropic equations with non-standard components: 3-Color Jacobi, mesh-tripling, and Fourier Analysis
No full textIn this paper, the authors introduce an enhanced multigrid solver that offers an efficient solution method which is quite robust across a variety of boundary value problems. The solver’s theoretical foundation includes a framework for deriving optimal relaxation parameters, and features an auto-tuned, customizable meshing approach. It employs a hierarchical structure capable of handling various grid configurations, optimized through Local Fourier Analysis (LFA). Although primarily developed for the anisotropic diffusion equation, we extend the investigation to include the singularly perturbed convection diffusion equation; where we fine-tune meshing parameters, refine discretization techniques, and implement customized multigrid operators to address its unique challenges. Numerical experiments are included that demonstrate the solver’s robustness and efficiency, thereby making a strong case for its use across a wide range of second order elliptic problems
Dr. S Akbar Zaidi, Executive Director, featured on Express News\u27 morning show, EXPRESSO
No full textIBA Karachi is pleased to share that Dr. S Akbar Zaidi, Executive Director, was featured on Express News\u27 morning show EXPRESSO on May 16, 2025. In this insightful interview, Dr. Zaidi reflected on IBA’s 70-year legacy of academic excellence, its recent AACSB Accreditation, and IBA\u27s continued mission to provide world-class education grounded in integrity, impact, and inclusion. He emphasized how IBA distinguishes itself by offering academically rigorous programs with a substantially lower fee structure than other leading business schools, while ensuring access through a comprehensive financial assistance framework for deserving students
NeuralTrace
No full textNeuralTrace is an AI-powered memory assistance system designed to support individuals with cognitive impairments such as dementia and Alzheimer’s disease. The project\u27s main objective is to provide real-time, context-aware memory augmentation by combining speech recognition, object detection, and scene understanding within a unified mobile platform. By capturing and processing spoken input, visual cues, and spatial information, the system enables natural language recall of past conversations, object locations, and scheduled tasks. The solution is composed of four core modules: an audio analysis pipeline using Whisper and semantic embeddings; a hybrid scene classification model integrating YOLOv8 and ResNet-50; a React Native-based mobile frontend; and a FastAPI backend that supports asynchronous machine learning operations and caregiver alerts via geofencing. Experimental evaluations demonstrate strong performance, including 92.2% transcription accuracy, 89% query match rate, and 84.1% scene classification accuracy. Overall, NeuralTrace presents a scalable and privacy-conscious framework that addresses critical gaps in memory support tools. It lays the foundation for future developments such as multilingual support, emotion-aware recall, and integration with wearable devices
Harf Ba Harf - Urdu Transcription and Diarization
No full textConversational speech is what we call normal, everyday, spoken speech. To represent this audio data in a text form we require the methods of transcription and speaker diarization to navigate conversations and gain valuable insight from them. While products in English and other high-resource languages are abundant, Urdu users face a lack of integrated systems that perform these tasks. This gap limits accessibility and productivity for millions of speakers of the language, especially professionals, students, and the hearing-impaired. Using publicly available, fine-tuned models, we evaluate on public and locally sourced data sets to develop a complete product that is an accurate, intuitive and user-friendly package to Pakistan’s growing technology needs of Urdu transcription and speaker diarization
Winter protest
No full textThe Gilgit-Baltistan (GB) region has witnessed weeks of winter protests triggered by public grievances over unmet demands, poor infrastructure, lack of basic services, and unfulfilled promises made under earlier agreements. Tensions escalated when protestors stormed the offices of the Water and Power Development Authority (WAPDA) over issues surrounding the Diamer-Bhasha Dam construction, including displacement, compensation delays, and exclusion from benefits. The protests are not isolated but part of a broader discontent rooted in governance challenges and lack of representation. The region’s residents are demanding healthcare, education, land rights, utility services, and political inclusion—specifically seeking a Kashmir-like autonomous status. The situation underscores longstanding tensions between the center and the periphery, exacerbated by development-led displacement and a history of political neglect
Need for diversified sources of income
No full textIn his thought-provoking article, Muhammad Taha Tariq, an MBA from IBA Karachi, emphasizes the importance of having diversified income sources in today’s unstable economic environment. He critiques the common financial habits of young earners, including impulsive spending, status-driven purchases, and the tendency to prioritize social expectations over financial security. Taha highlights the importance of saving early, building emergency funds, and investing in avenues like mutual funds and dividend-paying stocks. Drawing wisdom from both modern financial principles and the Quranic lesson from Surah Yusuf, he advocates for self-discipline, long-term planning, and mindful generosity. The piece underscores that true financial stability stems from careful portfolio management, controlled lifestyle choices, and a conscious effort to save before spending