ABSTRACT The fashion retail landscape has undergone a significant transformation through digitalization and evolving consumer expectations, with physical stores requiring reinvention to remain relevant. This study examines Zara’s strategic evolution from mainstream to approaching luxury positioning through architectural redesign, phygital integration, and user-generated content (UGC). Through a qualitative analysis of the store literature and the luxury trends, the observation of the store, and analysis of UGC, a case study of Zara Man Hermosilla has been analyzed and compared with Saint Laurent Babylone. In doing so, this research has elaborated a comprehensive conceptual framework that systematically integrates physical, digital, and social channels within the retail environment, providing architects, designers, and retailers with a structured approach to navigate the complexities of contemporary retail transformation, and addressing a critical gap in current scholarship. Second, the study advocates architectural design of the stores as a strategic instrument of brand repositioning, effectively dissolving traditional boundaries between mainstream and luxury market segments. Finally, it demonstrates how Zara serves as a pioneering case in establishing emergent value propositions within retail formats, not merely appropriating luxury design codes but strategically embedding them within an integrated experiential and phygital consumer journey that aligns with evolving consumer expectations and aspirational needs.

Broiler farming is a strategically important sector within Indonesia’s national food industry, driven by steadily rising per capita chicken meat consumption. This study aims to (1) evaluate the economic feasibility of the broiler farming enterprise “AA Farm” in Tanggulun Village, Kadungora District, Garut Regency, and (2) formulate a comprehensive marketing strategy to support its sustainable growth. A single-case-study design with a quantitative descriptive approach was employed. Primary data were gathered through in-depth interviews, direct field observations, and structured questionnaires, while secondary data were drawn from company financial records, market reports, and official government publications. Economic feasibility was assessed using Total Cost (TC), Total Revenue (TR), profit (π), Revenue–Cost (R/C) Ratio, and Break-Even Point (BEP) analyses. The findings reveal that “AA Farm” achieves its Break-Even Point at the 17th production cycle (≈17 months), with an annual Return on Investment (ROI) of 47% and an R/C Ratio of 1.40, confirming strong operational profitability. The marketing strategy, built on the Marketing Mix (4P) framework and SWOT analysis, encompasses targeted market segmentation across four consumer groups, cost-plus competitive pricing, a multi-channel distribution system, integrated digital and traditional promotion, and a customer relationship management (CRM) programme. These findings demonstrate that “AA Farm” is economically feasible and possesses substantial potential for sustainable growth when supported by disciplined marketing implementation and modern operational management.

Learning outcomes The case can be used in postgraduate or Executive programs to teach the concepts of managing growth for differentiated products in a Strategy course. The learning outcomes are as follows: analyse competitive positioning strategies for challenger brands in price-sensitive, fragmented markets; evaluate strategic growth options, including geographic expansion, market segmentation and product portfolio diversification for niche startup FMCG brands; understand the challenges of scaling ethnic/cultural products in emerging markets; apply strategic frameworks to assess trade-offs between mass-market and premium positioning; and examine operational and organisational challenges in transitioning from founder-led to professionally managed organisations. Case overview/synopsis Lahori Zeera, a cumin-infused fizzy drink, has achieved significant success, capturing market share in Tier 2 and Tier 3 Indian cities and outpacing global cola majors with its distinctive cumin-based flavour. It needed to build on its early success, and hence the brand now faced a strategic crossroads: Should it expand nationwide, or maintain its current focus on the northern parts of India? The decision is further complicated by intensifying competition from both multinational cola giants and agile local imitators, and it must choose whether to reinforce its ethnic or functional positioning or repackage itself to appeal to urban, premium consumers. Complexity academic level Postgraduate or Executive programs. Supplementary materials Teaching notes are available for educators only. Subject code CSS 11: Strategy.

The purpose of the study is to address the profile of tourists visiting Langkawi and determine the motives of tourists visiting Langkawi. Using a quantitative research approach, data were collected from 340 respondents visiting Langkawi using the Leisure Motivation Scale (LMS) [1]. Data were analysed using descriptive statistics and t-test. The results showed that therewere two kinds of tourists visiting Langkawi: sports tourists and non – sports tourists. The majority of sports tourists (72.9%) from the age of 22 up till 28 years of age was more interested in sports tourism and was found to be losing their interest in sports tourism at around the age of 28 to 33 years of age. Results also showed that there are no significant differences in terms of motives between sports tourists and non-sports tourists traveling to Langkawi. The information obtained from this study can be used by people in the sports tourism industry to implement it into their marketing segmentation by targeting the right type of tourist coming to Langkawi.

Rental housing markets in Germany and other European countries are experiencing profound transformations, generating new forms of inequality. While existing research often emphasises binary divisions, such as between owners and tenants, or across generations, it rarely captures the layered stratifications within the rental sector itself. This paper uses original household survey data from Leipzig-Connewitz ( n = 427), a neighbourhood marked by post-socialist restructuring and rapid investment influx, to explore how socio-temporal inequalities are shaped by residence duration and landlord type. The analysis develops an empirically grounded typology of rental housing experiences, ordered along a continuum from stability to vulnerability: (i) residents in public and non-profit housing enjoying security and rent affordability; (ii) affluent tenants in high-end private rentals, largely shielded from market pressures; (iii) tenants in corporate housing who face insecurity despite high incomes; and (iv) households in the financially fragile segment of the private rental market, with low rents but limited economic security. These types reflect how tenure structures intersect with socio-economic and temporal dimensions to produce differentiated patterns of housing security and vulnerability. By moving beyond conventional tenure binaries, the research provides a nuanced understanding of contemporary rental markets, particularly in housing systems with high tenant protection and significant non-profit segments. The typology developed here contributes to international housing debates by highlighting how post-reunification legacies, institutional diversity and temporal positioning shape uneven tenant experiences. It provides a conceptual lens for analysing stratification within rental-dominated contexts across East Germany and other regulated housing regimes facing significant investment dynamics.

Behavioral finance theory predicts that Processing Fluency—the subjective ease of parsing a nominal stimulus—should systematically influence investor attention and asset pricing through heuristic-based decision making. Yet modern equity markets, increasingly dominated by High-Frequency Trading (HFT) and algorithmic execution, provide powerful near-instantaneous arbitrage forces that should neutralize any pricing premium arising from superficial nominal cues. Whether cognitive biases such as the “Ticker Fluency” effect and the “Alphabet Effect” persist in this algorithmic environment or have been fully arbitraged away remains an open empirical question with direct implications for the boundary conditions of Processing Fluency Theory. We address this gap by applying a deterministic Heuristic Fluency Score—based on vowel density and consonant cluster penalties—to all 492 S&P 500 constituents over 752 trading days (January 2021–January 2024), estimating individual stock Fama-French 3-Factor Alphas via daily time-series regressions, and testing whether fluency or alphabetical rank explains cross-sectional variation in abnormal returns after controlling for Liquidity, Amihud illiquidity, and GICS Sector Fixed Effects. To guard against Selection Bias, we explicitly contrast a biased illustrative case study (N=25, 2019–2024) against the rigorous full-market analysis. We find no statistically or economically significant effect: the Fluency Score coefficient is β=0.0036 (p=0.495) and the Alphabet Rank coefficient is β=−0.0027 (p=0.642), with the results robust to all tested parameterizations (λ∈[0.05,0.20]; p>0.50 throughout). These findings establish a boundary condition of Processing Fluency Theory: in algorithm-dominated, highly liquid large-cap markets, cognitive biases in nominal cues are fully absorbed by arbitrage, and ticker symbols function as neutral identifiers rather than heuristic signals. Residual effects, if any, are more likely to manifest in attention-based or volume-related outcomes, or in less institutionalized market segments where algorithmic participation is lower.

Medical debt burdens an estimated 20 million Americans and may contribute to unmet needs for healthcare. To examine if medical, mental health, or dental needs are differentially sensitive to medical debt and if this varies by type of health insurance. Cross-sectional, nationally representative survey. U.S. adult participants in the 2023 National Health Interview Survey. Self-reported medical financial hardship ("medical debt") and probability of deferred care in the past year for (a) medical, (b) mental health, and (c) dental needs, among adults with medical debt, compared to adults without medical debt. The overall prevalence of past-year medical debt was 10.7% [95% CI: 10.3, 11.2] and was high across all insurance market segments: 19.5% [17.5, 21.8] among uninsured adults, 12.6% [11.3, 14.1] among adults with Medicaid, 9.3% [8.8, 9.9] among adults with commercial insurance, and 8.1% [7.2, 9.2] among adults with Medicare. Medical debt was associated with a 24.6 [22.4, 26.8] percentage point (pp) increase in the probability of deferred dental care, 17.6pp [15.9, 19.4] increase in the probability of deferred medical care, and 9.3pp [7.9, 10.7] increase in the probability of deferred mental healthcare. Associations were largely consistent by health insurance category, although the association between medical debt and deferred medical care was significantly higher (P = 0.008) among uninsured adults (32.5pp [25.6, 39.4]) than adults covered by commercial insurance (16.9 [14.7, 19.1]). Medical debt was consistently associated with deferred care, with dental care most commonly deferred, followed by medical care and then mental healthcare. This association remained mostly consistent across all types of health insurance. Policy interventions that aim to address financial barriers to care and the accompanying burden of medical debt may mitigate the health and economic consequences of delayed and forgone care.

The rapid development of big data is profoundly reshaping the insurance industry, while traditional actuarial methods face obvious limitations in dealing with high-dimensional, nonlinear, and unstructured data. This paper systematically reviews the current application status of machine learning (ML) in the insurance industry under the background of big data, focusing on four key areas: fraud detection, risk assessment and management, claims issues, and customer segmentation. Integrated empirical research shows that ML methods generally outperform traditional statistical models in terms of prediction accuracy, operational efficiency, and decision support. Meanwhile, this paper identifies four types of real-world challenges that restrict the responsible deployment of machine learning: data quality issues, regulatory and ethical constraints, privacy and security concerns, as well as organizational and operational barriers. Based on the literature review, this paper holds that machine learning does not replace actuarial science, but rather substantially enhances its predictive ability, operational efficiency and strategic decision-making level. Responsible technology application must simultaneously promote the construction of transparency, fairness and compliance. This paper constructs an analytical framework that integrates technical capabilities with practical constraints, providing a structured reference for subsequent research and practical guidance for insurance companies and policymakers to promote the sustainable and compliant application of machine learning.

ABSTRACT This study investigates the role of employers in facilitating refugees’ entry into the labour market and their prospects for sustainable employment within the low-wage sector. It does so through the lens of employer logics, manifested in HRM practices, HR philosophies, and other organisational characteristics. Focusing on three Swedish retail companies, the study identifies three distinct employer logics – one dominant logic in each firm – in the low-wage sector: the logic of efficiency, the logic of community engagement, and the logic of diversity. These logics determine who is hired, who is excluded, and who is offered opportunities for upward mobility versus those who remain stagnant positions. Applying resource dependency and labour market segmentation theories, the study also highlights the tensions created by employer logics in the low-wage sector regarding the labour market integration of refugees. A central tension lies in opening doors to refugees, but only partially: while opportunities for labour market entry are created, access to stable, long-term employment remains limited. This and other identified tensions have implications not only for individual refugees’ employment prospects but also for the reproduction or potential reduction of labour market segmentation into ’primary“ and ”secondary’ workforces.

ABSTRACT This study examines how intergenerational marketing can strengthen connections between skipped generations, particularly young adults, and older consumers. Using qualitative interviews with cohorts of young adults, middle-aged adults, and older adults, it explores perceptions of marketing narratives aimed at aging consumers. Findings highlight both opportunities and challenges in leveraging marketing to foster respect, understanding, and shared identity across age groups. By framing marketing as a relational practice rather than a transactional tool, the study contributes to intergenerational theory. It offers practical guidance for brands and policymakers seeking to promote solidarity across generations. It underscores the importance of recognizing diverse perspectives within and between generational groups, positioning older consumers not simply as market segments but as active participants in intergenerational relationships. These insights inform the design of inclusive marketing strategies that reinforce connection and belonging across familial and societal contexts.

Human–technology relationships have become core strategic capabilities and enablers of enterprise sustainability. Contemporary interactions between humans and technology are undergoing a profound transformation toward a more human-centric and value-oriented paradigm, aiming for Industry and Society 5.0, a shift that is particularly salient in banking. The influence of customer experience quality on the strategic foundations of enterprise management is being fundamentally redefined. The purpose of this research is to assess the influence of customer experience on marketing outcomes in the banking industry. To analyze the directions, strengths, and statistical significance of relationships, structural equation modeling (SEM) using partial least squares (PLS) was employed. The research model was tested on a sample of 616 valid responses from customers of banking services in Serbia. The research shows that customer experience positively impacts customer satisfaction, behavioral loyalty intentions, and word-of-mouth, making it a strong predictor of marketing outcomes. The moderating roles of gender, customer segment, and respondents’ regional affiliation were tested, identifying variables that moderate significant relationships between customer experience and marketing outcomes, unveiling detailed insights into demographic and segmentation disparities. The findings offer robust empirical support for managerial decision making in customer experience enhancement initiatives.

Corporate social responsibility (CSR) theory emphasizes both CSR characteristics in products and CSR activities to enhance relationships with buyers. However, there are theoretical gaps regarding the factors that influence firms' CSR strategy choices in a competitive setting. This study develops a biform game model to study the trade-off between product-oriented strategies and relationship-oriented CSR strategies. The model demonstrates that factors such as information asymmetry, product value, market segment size, transaction costs, bargaining power, and other market conditions significantly influence firms' CSR strategies. It identifies conditions under which strategic heterogeneity, strategic homogeneity, and a parameter space with multiple strategic heterogeneity equilibria can exist. When the value derived from product- and relationship-oriented strategies is significantly high, firms tend to adopt these respective strategies. In a low information asymmetry context, firms with lower bargaining power are more likely to choose a relationship-oriented strategy, whereas in a high information asymmetry context, these firms are inclined to adopt a product-oriented strategy. Regardless of the level of information asymmetry, firms with larger market scales tend to favor a product-oriented strategy. Additionally, the model integrates the synergy between business strategy and CSR strategy, which further shapes the trade-offs firms encounter in their CSR strategy choices. This study offers new insights into CSR strategy choices and the resulting market outcomes in competitive environments.

Abstract This study examines labour market dynamics and livelihood sustainability in India by analysing pooled microdata from the periodic labour force survey (PLFS) for the period 2017–18 to 2023–24. It provides a comprehensive assessment of employment quality and welfare by jointly analysing informal employment, access to employment-linked social security, individual earnings, household consumption, and a multidimensional livelihood sustainability index (LSI). Survey-weighted models incorporating state and survey-year fixed effects indicate that informal employment remains widespread and has increased over time, despite rising average incomes and consumption. Informality is consistently associated with significant disadvantages in earnings, household welfare, and overall livelihood sustainability. Education and access to social security emerge as important protective factors. However, their effects vary substantially across regions and socioeconomic groups. A Blinder-Oaxaca decomposition of earnings differentials suggests that a significant portion of the formal-informal wage gap is attributable to structural differences in returns to observable characteristics, reflecting persistent labour market segmentation rather than differences in endowments alone. The findings underscore enduring spatial disparities and the limited capacity of employment growth, by itself, to improve livelihoods. Sustainable development policies must therefore prioritise employment quality, expansion of social protection, and region-specific labour market interventions alongside job creation.

Purpose Grounded in the Elaboration Likelihood Model (ELM), this study investigates how central and peripheral cues affect Generation Z (Gen Z) travelers’ adoption of electronic word-of-mouth on social media and their destination choice. Design/methodology/approach A convenient sample of 621 Gen Z travelers was collected in Hanoi, Vietnam. SmartPLS ver. 4.0 was used for testing the proposed hypotheses. Findings The results indicate that both central and peripheral cues play significant roles in predicting Gen Z travelers’ adoption of eWOM on social media and their choice of destinations. The quality of arguments and informativeness significantly enhances eWOM’s usefulness in the central route. The source expertise, credibility and information volume in the peripheral route positively affect eWOM credibility, in which the source’s expertise shows the most substantial impact. Furthermore, the peripheral route predominantly influences Gen Z travelers’ eWOM adoption (EA) and destination choice. Originality/value This is one of the pioneering studies to assess the relative importance of the central and peripheral cues on EA, hence the impacts on destination choice of Gen Z travelers. The findings provide valuable insights for identifying effective communication channels tailored to Gen Z tourists, thereby enabling the development of efficient and targeted eWOM strategies to attract this market segment effectively in the future.

Reducing alcohol availability has been identified as a potential approach to reduce alcohol use and related harm. In order to regulate alcohol availability at a local level, it is important to involve communities in decision-making and ensure interventions are locally acceptable and appropriate. To do this, we need to improve our understanding of how residents conceptualise alcohol availability and its impact on behaviours. This paper uses data collected in Scotland, a country with particularly high levels of alcohol-related harm, to explore the perspectives of residents, on local alcohol availability and how it might affect drinking behaviours. We conducted 11 online focus groups with 45 participants, living in nine strongly contrasting neighbourhoods in Scotland, characterised by varying levels of alcohol retail density change, urbanity and deprivation. We explored participants' perceptions of their local alcohol environment and alcohol availability, and any perceived relationship between alcohol availability and alcohol-related behaviours. Participants challenged established notions that alcohol availability is characterised primarily by density of alcohol outlets. According to our participants, availability is about accessibility, ease of purchase and ubiquity of alcohol. Residents drew distinctions between areas of varying deprivation and conceptualised alcohol availability as complex, characterised by market segmentation, and related to price, advertising and the wider environment. This is one of the few papers to explore residents' perspectives of local alcohol availability and its relationship with alcohol use. It highlights that residents view alcohol availability as encompassing more than just the physical presence of outlets, recognising also the variety of outlet types and the connections between availability, pricing, and advertising. Policies to reduce local availability should consider residents' perspectives and account for contextual factors such as shifts in the retail landscape and the availability of alcohol-free recreational alternatives.

The US spends hundreds of billions of dollars annually on health insurance administration, but variation in administrative costs and profits remains inadequately understood. We used regulatory filings to estimate state-level, all-plan administrative spending and profit in 2023 for each of three market segments: fully insured commercial coverage, Medicaid, and self-funded commercial coverage. Across state markets for fully insured coverage, median all-plan administrative spending was $599 per person per year, with median profits of $63 per person per year. Administrative costs for self-funded coverage were substantially lower (median, $285 per person per year), as were profits ($22 per person per year). Administrative spending was highest in Medicaid. In states with Medicaid managed care, administration and plan profit accounted for $772 per person per year. Total spending was about $250 per person per year lower in states without Medicaid managed care. Per person per year spending was highly variable across states for all segments. Conservatively, reducing administrative spending and profits in states with above-median levels to the cross-state median could save approximately $6billion, $9billion, and $4billion in the fully insured, Medicaid, and self-funded segments, respectively.

A generalized Vidale–Wolfe model on existing and new customers advertising strategy in a segmented market

Perovskite solar cells (PVSCs) have rapidly advanced as one of the most promising photovoltaic technologies, achieving certified power conversion efficiencies comparable to traditional Si-based solar cells. Despite this progress, widespread commercialization of PVSCs is still hindered by challenges in upscaling high-performance devices, ensuring long-term operational stability, and achieving cost-effectiveness. This perspective article overviews the status, key strategies, and persistent hurdles in addressing these barriers, with a particular focus on the commercialization pathways of perovskite photovoltaic technology tailored to niche applications. The potential of perovskite-based tandem solar cells, alignment with specialized market segments, and the role of government supportive policies and pioneering companies are discussed. By outlining targeted development roadmaps and showcasing successful commercialization efforts, this perspective offers a multidimensional view of advancing PVSCs from laboratory breakthroughs to viable renewable energy technologies.

Business Strategy Planning at PT Kurniawan Sejati Sejahtera is carried out in response to the increasingly fierce market competition in the bottled water industry. In recent years, the company has experienced fluctuations and downward trends in sales, underscoring the need for a more adaptive and competitive business strategy to sustain growth and strengthen its market position. In addition, the bottled water industry in Indonesia continues to grow rapidly, with many companies and brands competing in the same market segment, further intensifying competitive pressures. This research aims to identify the company's internal and external factors as the basis for strategy formulation, develop appropriate and effective business strategies, determine strategic priorities in the face of market competition, and map the relationship between business strategy and business processes. The methods used in this study include Value Chain Analysis to identify internal factors, Porter's Five Forces Analysis to analyze external factors, SWOT Analysis to formulate alternative strategies, and Multi-Attribute Utility Theory (MAUT) to determine strategic priorities. The results of the study show that priority strategies include maintaining the current product price while maintaining quality, increasing promotional activities through radio, social media, and endorsement services, maintaining good and mutually beneficial relationships with suppliers, choosing backup drivers, providing discounts or bonuses for regular customers, actively seeking partnership programs, continuing to develop attractive packaging design innovations, and expanding distribution channels for convenience stores. At the end of this study, a strategic map that connects business processes with goals is developed to support the implementation of effective strategies.

The study is concerned with the complexities of the coffee supply chain, which includes the stages of farming and the entire end-to-end retail process. It explores crucial areas such as cultivation practices, processing provisions, export-import logistics, roasting, packaging, distribution, and retailing. The difficulties and issues in each stage are analyzed, focusing on quality management, sustainability initiatives, logistical complexity, and market instability. Simultaneously, the paper employs machine learning algorithms, viz., linear Regression, kNN, and decision trees, to process the data related to coffee beans. Linear Regression is used to forecast market trends, consumer preferences, and price changes based on historical data. Market segmentation, consumer behavior prediction, and regional preferences in coffee drinking are analyzed using the kNN algorithm. In the meantime, decision trees are used to determine significant factors affecting coffee preference, market division, and predictive analytics. The data undergoes a stringent examination using the chosen machine learning algorithms to extract information about the complex trends and associations of the coffee industry. Findings from such analyses provide insight into the predictive power and usability of each machine learning method in addressing specific issues related to coffee bean data. The results provide helpful information on streamlining supply chain management practices, improving quality control mechanisms, and understanding consumer preferences in the dynamic coffee industry. The research highlights the significance of using machine learning methods to obtain practical insights from coffee-related data, supporting better management and decision-making in the supply chain. The core challenge in deploying such data-driven models across a multi-stakeholder supply chain spanning various Internet services lies in the integrity and privacy of shared commercial data. To address this critical concern, this paper proposes an extended framework that integrates two pivotal Internet Service and Security technologies. Firstly, we implement a lightweight Blockchain layer to ensure the integrity, immutability, and non-repudiation of key supply chain event data (e.g., harvest volume, quality scores) used as input for model training. Secondly, we introduce a Federated Learning (FL) approach to collaboratively train the demand forecasting model across decentralized regional data silos, thereby preserving the privacy of sensitive local information. This secure, distributed architecture demonstrates a novel path toward trustworthy and privacy-compliant data utilization in global agricultural supply chains, marking a significant contribution to the fields of Internet services and information security.