Several earlier studies have investigated the attitudes and intentions of consumers towards sustainability within both a general (Kim et al., 1998; Nicholls, 2002; Berry & McEachern, 2005) and fashion context (Bray et al., 2011; Henninger et al., 2016; Hosseiunpour et al., 2016; Joergens 2006; Joy et al., 2012; McNeill and Moore, 2015; Reimers et al., 2016; Ritch, 2020; Tey et al., 2018; Bianchi and Gonzalez, 2021). However, there is a paucity of research from the perspective of children (Heo and Muralidharan, 2019; Ritch, 2019; Su et al., 2019; Watkins et al., 2019; Blazquez et. al., 2020; Niinimaki et al., 2020; Riesgo S. B., et al., 2022). There were predictions in 2020 that the global childrenswear market would be worth US$252.2 billion, and was proven to be more resilient than the general fashion sector during the COVID-19 pandemic (Mintel, 2021). Furthermore, the pandemic has seen prominence given to sustainability issues, with consumers increasingly prioritising brands with sustainable credentials (Euromonitor, 2022), yet little is known about children’s attitude towards sustainability. This paper aims to address this shortcoming, by assessing children’s awareness of sustainability. A Theoretical Model is proposed: Children’s sustainability awareness stages infused by educational third places.

Generative artificial intelligence (AI) tools such as ChatGPT, Dall-E, and Steve AI can facilitate instant content creation. As an illustration, a fashion brand can simply command ChatGPT to: “Write an Instagram caption about the importance of good clothes during winter in two hundred words”. In seconds, ChatGPT generates the output. This minimum effort and short production time in the usage of generative AI may enhance content marketing outcomes. However—as warned by scholars (Bruyn et al., 2020)—not only is it advantageous for businesses, AI can also be disastrous. Underpinned by this, and the fact that generative AI and content marketing are nonexistent in the pertinent literature (see streams of research on AI in marketing e.g., Eriksson et al., 2020; Huang & Rust, 2021, 2022; Vlačić et al., 2022), this research aims to explore the potential benefits and drawbacks of generative AI for content marketing.

Electronic waste (E-waste) has become a long-standing global concern. People are purchasing new affordable and improved technologies long before the end-of-life (EOL)’s of their old devices, which is leading to overconsumption and growing volumes of e-waste. At present, official data indicates that 80% of the volume of e-waste is not formally treated globally. The complex nature of e-waste recycling processes is a significant challenge.

Consumers visiting platforms that host user-generated content (UGC) not only consume content but also generate content by investing time and effort. This paper seeks to examine a UGC platform's content provision strategy: how a UGC platform can motivate consumers to generate UGC and how it can manage the balance between UGC and platform's own content. As UGC and the platform's own content perform the same function, one may be inclined to think that the two types of content are substitutes. Our analysis shows that they could function as strategic complements. This is because increasing the platform's own content provision raises the quality of content on the platform, motivates more consumers to join the platform, and increases the total UGC provision on the platform. The fact that consumers dislike advertising could lead us to believe that consumers will be less motivated to generate UGC if ad space increases. On the contrary, we find that consumers may be motivated to increase UGC provision to make up for the loss in enjoyment and increase the overall quality of contents on the platform. The public good characteristics of UGC could prompt us to think that UGC provision on the platform will be less than the socially optimal level. Our analysis identifies conditions when the total provision of UGC can be more than the social optimum. One may wonder whether it is profitable for a UGC platform to completely dispense with its own content. We find that it is always profitable for the UGC platform to offer some of its own content. This is because when consumers spend more time consuming the content, the platform can monetize their attention and earn higher ad revenue.

Non-fungible tokens (NFTs) exploded onto the global digital landscape in 2020, spurred by pandemic-related lockdowns and government stimulus (Ossinger, 2021). An NFT is a unit of data stored on a blockchain that represents or authenticates digital or physical items (Nadini, 2021). Since it resides on a blockchain, NFTs carry the benefits of decentralization, anti-tampering, and traceability (Joy et al., 2022). Fashion brands quickly capitalized on these features, launching fashion NFT collections and garnering significant profits from the sale of fashion NFTs in 2021 (Zhao, 2021). For example, Nike’s December 2021 acquisition of RTFKT (pronounced “artifact”) resulted in USD 185 million in sales less than a year after their acquisition (Marr, 2022).

Today, the metaverse is everywhere; it has become a major buzzword. The term was first coined in an American writer’s 1992 science fiction novel, Snow Crash, as a portmanteau of meta (Greek prefix meaning beyond) and universe. Nearly three decades later today, it is no longer the setting of a science fiction epic. Rather, it is becoming as real as the physical world. In the current time, the metaverse is used as a concept to describe a seamless convergence of the physical and digital worlds, or a virtual community where people can work, play-to-earn, transact and socialize (J.P. Morgan, 2022).

This paper investigates the number of scatterings a photon undergoes in random walks before escaping from a medium. The number of scatterings in random walk processes is commonly approximated as τ + τ 2 in the literature, where τ is the optical thickness measured from the center of the medium. However, it is found that this formula is not accurate. In this study, analytical solutions in sphere and slab geometries are derived for both optically thin and optically thick limits, assuming isotropic scattering. These solutions are verified using Monte Carlo simulations. In the optically thick limit, the number of scatterings is found to be 0.5 τ 2 and 1.5 τ 2 in a sphere and slab, respectively. In the optically thin limit, the number of scatterings is ≈ τ in a sphere and ≈ τ (1 − γ − ln τ + τ ) in a slab, where γ ≃ 0.57722 is the Euler-Mascheroni constant. Additionally, we present approximate formulas that reasonably reproduce the simulation results well in intermediate optical depths. These results are applicable to scattering processes that exhibit forward and backward symmetry, including both isotropic and Thomson scattering.

Mesophase pitch is a unique graphitizable material that has been used as an important precursor for highly graphitic carbon materials. In the current study, we propose to consider a spinnable mesophase pitch as a lyotropic liquid crystalline solution composed of solvent components and liquid crystalline components, so-called mesogen or mesogenic components. Among mesophase pitches, the supermesophase pitch is defined as a mesohpase pitch with 100% anisotropy, and can only be observed in pitches with a proportion of mesogenic components exceeding the threshold concentration (TC). We also examined the critical limit of AR synthetic pitch and 5 experimental spinnable mesophase pitches (SMPs). Then, we examined the effect of the solvent component on the minimum required amount of mesogenic component using a selected solvent component instead of their own solvent components. AR pitch showed 100% anisotropy with the least amount of its mesogenic component, THF insoluble components, of 60 wt.%. The solvent component, THF soluble components, extracted from AR-pitch, which has a molecular weight pattern similar to that of the original material but more amount of naphthenic alkyl chains, showed better solvent functionality than those of other THF solubles (THFSs) from other as-prepared spinnable mesophase pitches. This is why a lower amount of AR THFS can produce a supermesophase pitch when combined with the THFI (mesogenic components) of other experimental mesophase pitches. As a result of the current analysis, we define the mesogens as molecules that not only readily stack, but also maintain stacking structures in a fused state in the solution. The solvent component, on the other hand, is defined as molecules with a structure that readily decomposes in a fused state in the solution.