Assessing Sustainable Consumption in Packaged Food in Indonesia: Corporate Communication Drives Consumer Perception and Behavior

Assessment of Key Feeding Technologies and Land Use in Dairy Sheep Farms in Spain

Familiar mixed dairy sheep farm is the most widespread system in the Mediterranean basin, in Latin America and in developing countries (85%). There is a strong lack of technological adoption in packages of feeding and land use in small-scale farms. To increase competitiveness, it would be of great interest to deepen the knowledge of how innovation was selected, adopted, and spread. The objective of this research was to select strategic feeding and land use technologies in familiar mixed dairy sheep systems and later assess dairy sheep farms in Spain. This objective was assessed by combining qualitative and quantitative methodologies. In the first stage, with the aim to identify and select the appropriate technologies, a panel of 107 experts in dairy sheep production was used. A questionnaire was applied to all of them with successive rounds using Delphi methodology. Later, these technologies were grouped by principal components analysis (PCA) and cluster analysis (CA). In a second stage the technological results from a random sample of 157 farms in the Center of Spain were collected. The technologies selected were linked to the technological adoption level of the farms in Castilla la Mancha by a multiple regression model. Ten technologies were selected by the 107 experts. Four factors were retained by PCA that explained at 67.11% of variance. The first factor is related to feeding strategies, the second to land use for livestock production, the third to efficient management of land resources or ecoefficiency and the fourth to by-products use. The expert evaluation was grouped in three clusters using the Ward’s method and the squared Euclidean distance measure, where the second showed higher values in the adoption level of each technology. The multiple regression model explained the relationship between the technologies and the technological level of the farms (R2 73.53%). The five technologies selected were: use of unifeed (1), supplemental feeding (5), grazing (6), raw materials production (7) and sustainable use of water and soil (10). These ten technologies identified can be directly extended to small-scale dairy farms from other countries in the Mediterranean basin and Latin America. This technological selection was supported from the broad and diverse panel of experts used. Besides, five technologies identified by the quantitative model will be able to be taken into account for the development of public innovation policies. They are direct technologies and easy to apply on the farm and seeking increased viability through innovation vs. intensification.

“Why Do We Buy Green Products?” An Extended Theory of the Planned Behavior Model for Green Product Purchase Behavior

Many consumers are concerned about environmental issues and have expressed interest in purchasing green products. However, actual sales of green products are still not as high as expected. Therefore, marketers of green products may need to investigate the factors driving green purchase behaviors. In this study, we proposed an extended theory of planned behavior (TPB) model that links consumers’ environmental concerns, perceived image of the company, consumer innovativeness, and environmental knowledge with green product purchase behavior. We applied a quantitative approach to collect the data via online questionnaires through Amazon MTurk. With 974 useable samples, the data were analyzed with structural equation modeling (SEM) using Smart PLS. The results showed that green purchase intention positively and significantly affects green purchase behavior. Moreover, the multigroup analysis revealed that the direct influence of green purchase intention on green purchase behavior is higher in developing countries than in developed countries. Regarding the direct effect on green purchase intention, attitude toward green products, perceived consumer effectiveness (PCE), environmental concern, and company’s perceived green image are significant antecedents of the intention to purchase, with attitude toward green products being the most robust antecedent among the three. However, subjective norms do not act as a direct antecedent of purchase intention. For the indirect effect on green purchase intention, four main antecedents (attitude toward green products, subjective norms, PCE, and environmental concerns) indirectly impact purchase intention via the mediating role of the perceived green image of the company. This study contributes to existing literatures via extending the TPB model. Regarding attitude-intention-behavior model, we found that environmental concern complements the model as an antecedent of green purchase intention. Moreover, a company’s perceived green image mediates the relationship between four antecedents and green purchase intention. Therefore, marketers of green products may also enhance future purchases by promoting the green image of the company. Particularly, we found that environmental knowledge positively moderates the relationship between environmental concern and a company’s perceived green image. We added on the empirical evidence that PCE plays a crucial role in stimulating green purchases as its direct positive influence on green purchase behavior is larger than that of green purchase intention. Moreover, consumer innovativeness positively moderates the relationship between PCE and green purchase intention.

Analyzing the Adoption Challenges of the Internet of Things (IoT) and Artificial Intelligence (AI) for Smart Cities in China

Smart cities play a vital role in the growth of a nation. In recent years, several countries have made huge investments in developing smart cities to offer sustainable living. However, there are some challenges to overcome in smart city development, such as traffic and transportation management, energy and water distribution and management, air quality and waste management monitoring, etc. The capabilities of the Internet of Things (IoT) and artificial intelligence (AI) can help to achieve some goals of smart cities, and there are proven examples from some cities like Singapore, Copenhagen, etc. However, the adoption of AI and the IoT in developing countries has some challenges. The analysis of challenges hindering the adoption of AI and the IoT are very limited. This study aims to fill this research gap by analyzing the causal relationships among the challenges in smart city development, and contains several parts that conclude the previous scholars’ work, as well as independent research and investigation, such as data collection and analysis based on DEMATEL. In this paper, we have reviewed the literature to extract key challenges for the adoption of AI and the IoT. These helped us to proceed with the investigation and analyze the adoption status. Therefore, using the PRISMA method, 10 challenges were identified from the literature review. Subsequently, determination of the causal inter-relationships among the key challenges based on expert opinions using DEMATEL is performed. This study explored the driving and dependent power of the challenges, and causal relationships between the barriers were established. The results of the study indicated that “lack of infrastructure (C1)”, ”insufficient funds (C2)”, “cybersecurity risks (C3)”, and “lack of trust in AI, IoT” are the causal factors that are slowing down the adoption of AI and IoT in smart city development. The inter-relationships between the various challenges are presented using a network relationship map, cause–effect diagram. The study’s findings can help regulatory bodies, policymakers, and researchers to make better decisions to overcome the challenges for developing sustainable smart cities.

Critical Success Factors to Improve the Business Performance of Tea Drink Chains

By determining critical success factors (CSFs), this study intends to identify indicators and offer suggestions for existing owners or operators to improve their critical works in business management, as well as for newcomers interested in investing or studying the chain business. To achieve the purpose of this study, document analysis, expert interviews, and the Delphi technique were used to verify that there are five-dimension indicators to explore the CSFs for tea drink chains. The critical success items in this study are as follows: (1) Regarding “brand image management”, the most critical factors unanimously agreed by the professionals are the certification of raw materials and the guarantee of food safety, which are mainly affected by the recent food safety crisis. (2) Regarding “quality and cost control”, the sub-items of raw material supplier’s stability and timeliness are the most important factors. (3) Regarding the “location of business”, the most important factors are the sub-items of visibility, convergence of people flow, and the structural consumption characteristics of the population in the area. Although these are not considered to be the most important factors in the questionnaires by professionals, the professors considered these sub-items to be the most critical factors in the dimension and should provide valuable references for CSFs. (4) Regarding “personnel training and customer satisfaction”, the sub-item of maintaining hygiene in shop fronts is the most important critical factor, followed by the attitude and affinity of the staff, as well as their clothing, appearance, and cleanliness. (5) Regarding the “marketing mode and promotion mode”, the two important sub-items include new product tasting and the irregular launch of new seasonal products.