Business model innovation is an important lever for change to tackle pressing sustainability issues. In this paper, ‘sufficiency’ is proposed as a driver of business model innovation for sustainability. Sufficiency-driven business models seek to moderate overall resource consumption by curbing demand through education and consumer engagement, making products that last longer and avoiding built-in obsolescence, focusing on satisfying ‘needs’ rather than promoting ‘wants’ and fast-fashion, conscious sales and marketing techniques, new revenue models, or innovative technology solutions. This paper uses a case study approach to investigate how companies might use sufficiency as a driver for innovation and asserts that there can be a good business case for sufficiency. Business models of exemplar cases are analysed and insights are gained that will contribute to future research, policy makers and businesses interested in exploring sufficiency.
Sarinya L. Suttharattanagul, Nisit Manotungvorapun
In recent years, sustainability has been receiving attention across various academic disciplines and businesses, particularly in consumer behavior. However, limited studies employ the sociological perspective and brand image to understand how sustainable consumption can lead to purchase decisions. By drawing upon a synthesis of existing literature, this study explores an integrated model with social aspects, branding, and quality perception to examine the relationship between sustainability consumption value and purchase intentions. A quantitative approach is adopted to investigate this relationship among Thai Gen Z respondents. An online survey was conducted by a non-probability sampling method comprising 1,541 respondents, aiming to capture the attitudes, behaviors, and intentions of this demographic segment toward sustainable consumption. The findings reveal the significant relationship between sustainable consumption and purchase intention, especially the effect of brand image on the decision-making process. The findings offer valuable implications for businesses and marketers seeking to align their strategies with evolving preferences for sustainability brand image, fostering a more sustainable and responsible consumption. Additional analysis of genders recommends practical implications for marketing communications. Directions for future research are suggested for academic and consumer research.
This study was conducted to design a green industry-based business model for Micro, Small, and Medium Enterprises (MSMEs) using the Business Model Canvas approach for Naura Kitchen. Waste is a significant issue in Bali Province. One of the waste-producing sectors is the home industry. Therefore, it is necessary to implement green practices in the MSME sector to reduce waste. The research method in this study uses a qualitative approach, grounded in the Business Model Canvas. Nine elements map the Naura Kitchen business model based on the green industry. The results of this study yield the concept of nine Business Model Canvas elements: main activities, primary resources, principal partners, distribution channels, customer relationships, customer segments, revenue streams, cost structures, and value propositions. This concept produces an overview of the implementation of the green industry in the Naura Kitchen business. The main activity in this business is the production of ecoprint colors from recycled materials
Environmental Management Accounting (EMA) is crucial to ensure business sustainability. EMA helps businesses monitor and assess environmental activities by providing financial and non-financial information and supporting strategic decision-making. With the increasing concern for the environment and social responsibility, EMA has become a valuable tool in optimizing economic performance and minimizing environmental impact. By analyzing data from the Scopus database from 2000 to August 2024 with a total of 245 articles and applying the bibliometric analysis method, the study identified essential topics of EMA such as theoretical framework, practical application, and relationship with environmental and financial performance of businesses. Emerging research trends include the circular economy and green innovation, forecasting that EMA will become increasingly crucial in helping companies achieve sustainable development goals.
While consumers experience shopping convenience through personalized recommendations, they are increasingly concerned about their consumption behavior being manipulated, leading to psychological resistance towards personalized recommendations. As such, research on how personalized recommendation services influence consumers’ perceptions of self-determination (which further influences their intentions to purchase) is called for. To address the gap, this current research adopts a self-determination perspective to investigate how the three basic psychological needs of self-determination (autonomy, competence, and relatedness) mediate the relationship between personalized recommendations and consumers’ purchase intentions. Moreover, this research examines whether the influence of personalized recommendations on consumers’ perceptions of self-determination is contingent upon product categories. The results of a hypothetical scenario experiments study demonstrate that competence and relatedness are critical mediators between recommendation novelty and purchase intention. The results also reveal that the impact of recommendation diversity on autonomy is contingent upon product categories, while the influence of recommendation novelty on relatedness is also contingent on product categories. This study contributes to the literature on personalized recommendations by providing an underlying mechanism for the influence of personalized recommendations on consumers’ purchase intention from the self-determination perspective, and especially by unravelling how the influence of personalized recommendations on consumers’ perceptions of self-determination is contingent upon product categories.
O design de moda é um sistema complexo, que possui como alicerce a gestão estratégica de informações provenientes da pesquisa e da utilização de dados para a criação de novos produtos do vestuário. Com a evolução da tecnologia, bem como as mudanças no mercado de moda e nas dinâmicas de vida das pessoas, surgem demandas personalizadas de consumo. Para que os processos do design de moda sejam adequados às demandas cada vez mais personalizadas, faz-se necessário revisitar pensamentos projetuais para a criação de produtos do vestuário. O objetivo deste artigo consiste em realizar a proposta de um framework para o design de moda, com ênfase nos processos de criação de novos produtos do vestuário para o contexto da customização em massa. Utilizou-se como metodologia as pesquisas bibliográfica e documental, para relacionar as bases teóricas do design de moda e da customização em massa com as práticas encontradas em diferentes setores da indústria têxtil e de confecção. Como resultado, o frameworkevidencia a necessidade da valorização do envolvimento do cliente, principalmente durante o processo de design. Isso irá possibilitar identificar tanto os componentes comuns, quanto as diferentes perspectivas que ampliarão o potencial criativo no design de produtos de vestuário para a customização em massa.
Dwi Ratna Puspita Sari, Sirli Fahriah, Kurnianingsih
et al.
Gold is one of the most favored investment instruments due to its stability and its ability to preserve value against inflation. However, its price movements are volatile and influenced by various global economic factors, currency exchange rates, and geopolitical conditions, making gold price forecasting a significant challenge. This study aims to develop a gold price forecasting system using the Long Short-Term Memory (LSTM) algorithm, a variant of the Recurrent Neural Network (RNN) that excels in processing time-series data. The dataset consists of historical daily gold buying and selling prices from 2015 to 2025, collected from Yahoo Finance, Logam Mulia, and the official website of Bank Indonesia. The modeling process follows the CRISP-DM methodology, which includes business understanding, data preparation and exploration, modeling, and evaluation stages. Time Series Cross Validation (TSCV) is used to validate the model. LSTM performance is compared with other models such as GRU, CNN-1D, and Simple RNN to identify the best-performing architecture. Evaluation results indicate that LSTM achieved the highest performance with an R² score of 0.99 for selling prices and 0.98 for buying prices on the final test dataset. The system is deployed online, making it accessible in real-time. This research is expected to assist investors, financial analysts, and the general public in making smarter investment decisions based on valid historical data and advanced forecasting technology.
Information technology, Electronic computers. Computer science
Georges Dupret, Konstantin Sozinov, Carmen Barcena Gonzalez
et al.
Making ideal decisions as a product leader in a web-facing company is extremely difficult. In addition to navigating the ambiguity of customer satisfaction and achieving business goals, one must also pave a path forward for ones' products and services to remain relevant, desirable, and profitable. Data and experimentation to test product hypotheses are key to informing product decisions. Online controlled experiments by A/B testing may provide the best data to support such decisions with high confidence, but can be time-consuming and expensive, especially when one wants to understand impact to key business metrics such as retention or long-term value. Offline experimentation allows one to rapidly iterate and test, but often cannot provide the same level of confidence, and cannot easily shine a light on impact on business metrics. We introduce a novel, lightweight, and flexible approach to investigating hypotheses, called scenario analysis, that aims to support product leaders' decisions using data about users and estimates of business metrics. Its strengths are that it can provide guidance on trade-offs that are incurred by growing or shifting consumption, estimate trends in long-term outcomes like retention and other important business metrics, and can generate hypotheses about relationships between metrics at scale.