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This paper aims to study the color change kinetics of lac dye in response to pH and food spoilage metabolites (ammonia, lactic acid and tyramine) for its potential application in intelligent food packaging.

UV-Vis spectroscopy was used to study the color change of dye solution. Ratio of absorbance of dye solution at 528 nm (peak of ionized form) to absorbance at 488 nm (peak of unionized form) was used to study the color change. Color change kinetics was studied in terms of change in absorbance ratio (A528/A488) with time using zero- and first-order reaction kinetics. An indicator was prepared by incorporating lac dye in agarose membrane to validate the result of study for monitoring quality of raw milk.

Dye was orange-red in acidic medium (pH: 2 to 5) and exhibited absorbance peak at 488 nm. It turned purple in alkaline medium (pH: 7 to10) and exhibited absorbance peak at 528 nm. The change in absorbance ratio with pH followed zero-order model. Acid dissociation constant (pKa) of dye was found to be 6.3. Color change of dye in response to ammonia and tyramine followed zero-order reaction kinetics, whereas for lactic acid, the first-order model was found best. In the validation part, the color of the indicator label changed from purple to orange-red when the milk gets spoiled.

The study opens a new application area for lac dye. The results suggest that lac dye has potential to be used as an indicator in intelligent food packaging for detection of spoilage in seafood, meat, poultry and milk.

This paper aims to identify the key drivers of US sustainable stock price movements in both the short and long term, deploying a rich collection of variables corresponding to green finance, investor attention and sentiment, market fear and uncertainty, macroeconomic variables, common market risk factors, commodity markets and the carbon emission market.

The empirical analysis is based on two main methodologies. First, the elastic net penalized regression is utilized to select the factors most influential on the price formation of sustainable stocks. Second, short- and long-run dynamics of the chosen factors are examined using the dynamic simulations of the autoregressive distributed lag (DYNARDL) model.

Of 32 candidate variables, the elastic net chooses US renewable energy, European sustainable stock market, EU ETS emission allowances, public attention to sustainable finance, gold and European renewable energy as the most contributing factors to the price behavior of sustainable stocks. The DYNARDL estimation results reveal that US renewable energy, European sustainable stock market and EU ETS emission allowances are important determinants in the short and long term, while public attention (European renewable energy) tends to affect sustainable stock prices only in the short (long) run.

The corresponding short- and long-run effects of US renewable energy, EU ETS emission allowances and European sustainable stocks on US sustainable stock prices should induce policymakers to keep the price behavior of these factors under systematic review. The formulation of policy measures could serve to safeguard the sustainable stock market from the price vagaries in these influential markets.

Relevant literature often focuses on the reaction of sustainable stocks to mainstream assets and risk proxies, limiting analysis to a few factors and providing an incomplete understanding of the drivers behind sustainable stock prices. More comprehensive research is needed due to the lack of studies on the determinants of sustainable stock prices and the growing global demand for these investments. This paper aims to address this gap by examining the potential explanatory power of 32 candidate factors representing key players in the global economic and financial landscape.

Digital transformation is a foundational change in how firms operate and deliver value to customers by using digital technologies to create new business opportunities. The purpose of this study is to offer a conceptual framework by reorganizing the elements of digital transformation, including resources, technology, capabilities and performance, into a workable process and investigating how firms integrate these resources, build new capabilities and transform them into enhanced performance.

This framework builds three blocks: resource integration, organizational capabilities and outcomes, exploring the impact of resource integration on outcomes through organizational capabilities. For resource integration, this study adopts a resource-based view (RBV) and service-dominant logic (SDL) to integrate organizational resources, including information technology (IT)-based resources, which play a role in moderating the effect of resource integration. Moreover, the author argues that firms’ capabilities have two levels: higher-order capabilities and lower-order capabilities, which will convert these resources through the capabilities into organizational performance.

This framework is built to understand the process of digital transformation and its antecedents for firms’ performance in business environments. Drawing on RBV, it provides a more holistic perspective that has been linked to resource integration, organizational capabilities and outcomes at the firm level. In this way, the theoretical basis for diminishing implicitness associated with the current perspective of digital transformation can be strengthened.

This paper offers a coherent discussion of digital transformation and explains the process of digital transformation, thus advancing prior work. The major contribution is connecting the process of digital transformation through which firms integrate resources, i.e. digital technologies and valuable, rare, inimitable and nonsubstitutable (VRIN) and nonVRIN resources as well, to build organizational dynamic capabilities based on RBV and SDL.

The widespread integration of artificial intelligence (AI)-enabled personalization has sparked a need for a deeper understanding of its transformative potential. To address this, this study aims to investigate the mental models held by consumers from diverse cultures regarding the impact and role of AI-enabled personalization in their lives (i.e. individual well-being) and in society (i.e. societal well-being).

This paper uses the theories-in-use approach, collecting qualitative data via the critical incident technique. This data encompasses 487 narratives from 176 consumers in two culturally distinct countries, Belgium and Pakistan. Additionally, it includes insights from a focus group of six experts in the field.

This research reveals that consumers view AI-enabled personalization as a dual-edged sword: it may both extend and restrict the self and also contribute to an affluent society as well as an ailing society. The particular aspects of the extended/restricted self and the affluent/ailing society that emerge differ across respondents from different cultural contexts.

This cross-cultural research contributes to the personalization and well-being literature by providing detailed insight into the transformative potential of AI-enabled personalization while also having important managerial and policy implications.

With the growing popularity of digital engagement, this study explores the interrelationships among digital engagement, interactivity and engagement strategies from the perspective of practitioners.

Individual in-depth interviews were conducted with 27 practitioners who have been involved in marketing communication activities in Hong Kong.

It was found that practitioners interpreted digital engagement mainly from the cognitive and behavioral dimensions and organizations engaged with their target audiences with either transactional or transitional communications. Functional interactivity and medium interactivity were perceived as the basis of digital engagement.

This qualitative analysis enriches the extant literature in marketing and public relations by delineating the relationships between interactivity and the use of different levels of digital engagement strategies, as well as guiding practitioners in setting effective digital engagement strategies.

This study aims to test the Kuznets curve between economic growth and child labor, along with the influence of exports, household size and rural population in the context of Pakistan.

To achieve the research objective, this study applied the unit root test, bound co-integration test, and autoregressive distributive lags (ARDL) method for the period of 1972–2021.

The findings show an inverted U-shaped relationship between economic growth and child labor indicating that at the beginning stage of economic development, child labor increases due to lower per capita household and subsequently, in the long-run of economic development, child labor decreases due to the higher per capita households. Moreover, the results also show that exports, household size and rural population have a positive influence on increasing child labor.

The policymakers and government of Pakistan need to focus on long-term economic growth policies, ensure free quality education and cheap equipment which practices minimum manpower to reduce the threat of child labor.

Having long-run economic growth, the government of Pakistan need to equally benefit the households and the poor population to reduce child labor and enhance the social welfare of society.

To the best of the authors’ knowledge, this is the first study that investigates the Kuznets curve relationship between economic growth and child labor in the context of Pakistan. Moreover, this study contributes to the reduction in child labor through long-term economic growth in the context of Pakistan.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-05-2023-0387

This paper aims to propose a stable and precise recognition and pose estimation method to deal with the difficulties that industrial parts often present, such as incomplete point cloud data due to surface reflections, lack of color texture features and limited availability of effective three-dimensional geometric information. These challenges lead to less-than-ideal performance of existing object recognition and pose estimation methods based on two-dimensional images or three-dimensional point cloud features.

In this paper, an image-guided depth map completion method is proposed to improve the algorithm's adaptability to noise and incomplete point cloud scenes. Furthermore, this paper also proposes a pose estimation method based on contour feature matching.

Through experimental testing on real-world and virtual scene dataset, it has been verified that the image-guided depth map completion method exhibits higher accuracy in estimating depth values for depth map hole pixels. The pose estimation method proposed in this paper was applied to conduct pose estimation experiments on various parts. The average recognition accuracy in real-world scenes was 88.17%, whereas in virtual scenes, the average recognition accuracy reached 95%.

The proposed recognition and pose estimation method can stably and precisely deal with the difficulties that industrial parts present and improve the algorithm's adaptability to noise and incomplete point cloud scenes.

As a powerful mathematical analysis tool, the local fractional calculus has attracted wide attention in the field of fractal circuits. The purpose of this paper is to derive a new ℑ-order non-differentiable (ND) R-C zero state-response circuit (ZSRC) by using the local fractional derivative on the Cantor set for the first time.

A new ℑ-order ND R-C ZSRC within the local fractional derivative on the Cantor set is derived for the first time in this work. By defining the ND lumped elements via the local fractional derivative, the ℑ-order Kirchhoff voltage laws equation is established, and the corresponding solutions in the form of the Mittag-Leffler decay defined on the Cantor sets are derived by applying the local fractional Laplace transform and inverse local fractional Laplace transform.

The characteristics of the ℑ-order R-C ZSRC on the Cantor sets are analyzed and presented through the 2-D curves. It is found that the ℑ-order R-C ZSRC becomes the classic one when ℑ = 1. The comparative results between the ℑ-order R-C ZSRC and the classic one show that the proposed method is correct and effective and is expected to shed light on the theory study of the fractal electrical systems.

To the best of the authors’ knowledge, this paper, for the first time ever, proposes the ℑ-order ND R-C ZSRC within the local fractional derivative on the Cantor sets. The results of this paper are expected to give some new enlightenment to the development of the fractal circuits.

This paper applies a volume-price probability wave differential equation to propose a conceptual theory and has innovative behavioral interpretations of intraday dynamic market equilibrium price, in which traders' momentum, reversal and interactive behaviors play roles.

The authors select intraday cumulative trading volume distribution over price as revealed preferences. An equilibrium price is a price at which the corresponding cumulative trading volume achieves the maximum value. Based on the existence of the equilibrium in social finance, the authors propose a testable interacting traders' preference hypothesis without imposing the invariance criterion of rational choices. Interactively coherent preferences signify the choices subject to interactive invariance over price.

The authors find that interactive trading choices generate a constant frequency over price and intraday dynamic market equilibrium in a tug-of-war between momentum and reversal traders. The authors explain the market equilibrium through interactive, momentum and reversal traders. The intelligent interactive trading preferences are coherent and account for local dynamic market equilibrium, holistic dynamic market disequilibrium and the nonlinear and non-monotone V-shaped probability of selling over profit (BH curves).

The authors will understand investors' behaviors and dynamic markets through more empirical execution in the future, suggesting a unified theory available in social finance.

The authors can apply the subjects' intelligent behaviors to artificial intelligence (AI), deep learning and financial technology.

Understanding the behavior of interacting individuals or units will help social risk management beyond the frontiers of the financial market, such as governance in an organization, social violence in a country and COVID-19 pandemics worldwide.

It uncovers subjects' intelligent interactively trading behaviors.

We introduce decision support tools aimed at optimizing perishable food supply chain management, effectively balancing conflicting objectives such as the exporter’s product collection cost and the importer’s profit. This involves considering factors like perishability, selling price, discount rate, and order quantity to achieve optimal outcomes.

This study considered a three-echelon supply chain comprising farmers, a single exporter, and a single importer providing a single, random-lifetime, perishable product under deterministic customer demand. The proposed mathematical model derived the optimal order quantity, selling price, and discount rate for the entire supply chain. This integrated optimization model treats both demand and supply sides as a multi-objective problem, employing a nonlinear program and a two-stage capacitated vehicle routing problem formulation. Numerical examples and a case study focusing on Thailand durian supply chain were conducted to illustrate the approach of the proposed model.

Taking into account both the importer’s profit and the exporter’s product collection cost, the proposed integrated supply chain model and tools maximize profitability, minimizes waste, and meets demand by optimizing perishable product collection costs and proposing a discount system for selling prices.

Limited to a single perishable product in a three-echelon international food supply chain. Future research can explore different products and supply chain contexts.

The tools enhance decision-making for supply chain managers, improving efficiency, reducing costs, and enhancing customer satisfaction in the perishable food industry.

The proposed model aids in local workforce management by forecasting required manpower for upcoming seasons. By factoring in product quality and pricing, it ensures customers receive fresh products at fair prices. Furthermore, the near-zero waste concept enhances storage conditions at importers' facilities, contributing to improved environmental hygiene.

The integrated model and decision support tools offer a novel approach to address complexities and conflicting objectives in perishable food supply chains, providing practical insights for researchers and practitioners.