Apple ownership in urban India has become increasingly associated with status signaling, identity expression, and aspirational consumption. This study examines relationships among brand attachment, self-congruity, social comparison, and materialism in shaping consumer behavior among Apple users, employing a two-phase mixed-methods design. In the first phase, 328 Apple users aged 18 to 35 completed standardized measures of brand attachment, self-congruity, social comparison, materialism, and financial stress. Brand attachment emerged as a strong predictor of behavioral outcomes, including repeat purchase intention and willingness to pay a premium. Self-congruity was positively associated with brand attachment and demonstrated complementary mediation in the relationship between materialism and brand attachment, indicating that materialistic consumers may develop stronger attachment when the brand aligns with their desired self-image. Social comparison and financial stress were associated with aspects of aspirational consumption, suggesting that prestige-oriented consumption may involve concurrent psychological and financial tensions for some consumers. In the second phase, 15 participants were interviewed regarding their motivations for Apple purchases, perceptions of social standing, identity, and post-purchase experiences. The qualitative findings indicated that Apple consumption functions as an identity marker, reinforces social comparison and prestige pressure, and generates emotional attachment beyond functional utility. Taken together, the findings suggest that prestige technology consumption extends beyond functional benefits to encompass identity construction, social positioning, and emotional attachment. This study contributes to understanding prestige consumption within an emerging-market context and discusses implications for brands, consumer well-being, responsible consumption, and policy where aspirational purchasing may interact with household financial pressures.
This study investigates how macroeconomic variables, such as interest rates and exchange rates, are associated with herding behavior in Borsa Istanbul (BIST). Thus, it uncovers the asymmetric nature of herding behavior by using the Cross-sectional Absolute Deviation of returns (CSAD) methodology on a comprehensive dataset of 545 stocks from 2011 to 2021. Based on a set of hypotheses derived from the literature, the findings reveal that herding behavior is predominantly a feature of rising markets and small-cap stocks, while it is notably absent in large-cap stocks and during market downturns. Besides, the study identifies that interest rate increases and Turkish Lira (TL) appreciation are primary macroeconomic signals associated with herding behavior. In addition to all these, the effect of monetary policy is conditional: as the interest rate decreases, herding behavior is associated with only rising markets. The study highlights a strong interaction between policy and investor sentiment in this context. These results are consistent with the view that herding behavior can reflect a rational (or quasi-rational) response to fundamental economic signals under extreme uncertainty, although the CSAD methodology does not allow a definitive distinction between rational and intentional herding.
Return predictability in emerging equity markets depends on the criterion used to evaluate a forecast. This study examines daily Dhaka Stock Exchange (DSE) index returns using 1,173 observations from January 2020 to December 2024. The design combines one-observation-lagged technical signals and macro-financial controls with a common inferential sample, heteroskedasticity- and autocorrelation-consistent inference, coefficient-stability checks, false-discovery-rate adjustment, and a strict 2024 holdout excluded from model training, preprocessing, and tuning. Moving Average Convergence Divergence (MACD), Bollinger Band, and Stochastic signals remain statistically significant across Newey–West, autoregressive-error, robust, and median-regression specifications; candlestick evidence is marginal after multiple-testing adjustment. Out-of-sample evidence is more restrictive. The historical-mean benchmark produces the lowest 2024 root mean squared error (0.002024). LASSO (0.003105), XGBoost (0.003363), Random Forest (0.003699), and OLS (0.004035) all have negative out-of-sample R-squared relative to that benchmark. Diebold–Mariano tests show that the machine-learning forecasts are significantly worse than the historical mean under squared-error loss, although they significantly outperform OLS. The three machine-learning models nevertheless achieve 77.05
Reducing post-harvest losses increases marketable grain without constituting a new round of cultivation. This study estimates the gross monetary value of potentially recovered soybean and maize output in Brazil and uses an input-output model only as an illustrative demand-activation exercise. Using 2022 FAOSTAT data, Brazil is benchmarked against 11 leading producers under arithmetic-mean, production-weighted, median, and best-performer definitions. Under the arithmetic-mean benchmark, potentially recovered output is approximately 7.0 million tonnes of maize and 5.6 million tonnes of soybean, valued at BRL 8.94 billion and BRL 15.31 billion, respectively, in 2022 prices. After deflation to 2018 prices, the combined gross recovered-output value is BRL 13.35 billion. If 100
Disruptions require manufacturing organisations to protect critical operations while accessing external resources without committing to excessive redundancy. This study examines how buffering and bridging responses are combined across disruption phases and under what resource, informational and financial conditions their complementarity is plausible. An abductive qualitative reanalysis of seven semi-structured interview records from Malaysian manufacturing and manufacturing-support organisations was conducted using resource dependence theory and within- and cross-case analysis. The findings identify selective asset protection, external supplier and logistics mobilisation, and information-supported coordination as interrelated but unevenly evidenced responses. Temporal evidence is clearest in two cases: one shows differentiated protection and recovery phases, while another reports initial inventory protection alongside external coordination. Three overlapping analytical orientations—asset-buffered protection, relationally bridged response and digitally supported hybrid coordination—describe the relative prominence of these practices rather than mutually exclusive configurations. Resource criticality conditions the potential value of protective capacity; digital information is useful when translated into feasible actions; and financial endurance constrains the sustainability of response combinations, particularly during prolonged disruption. The study refines resource dependence theory by specifying conditional, resource-specific and temporally differentiated buffering–bridging complementarity, articulated through four propositions. For managers, it supports aligning critical-resource protection with usable alternatives and sustainable financial commitments. Because the evidence derives from retrospective single-informant accounts, the proposed relationships require longitudinal and comparative testing.
