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The purpose of this paper is to study the impact of ethics and social responsibility on employees’ affective commitment in the context of small and medium-sized enterprises (SMEs).

This is a quantitative research. The authors employ multistage sampling technique, non-probability and judgmental sampling method. Data were collected through questionnaire survey to measure the respondents’ perceptions of the ethics and social responsibility, as well as internal corporate social responsibility (CSR) practices. The data obtained were analyzed through variance-based structural equation modeling (SEM), i.e., partial least square SEM.

The findings reveal that perceived role of ethics and social responsibility (PRESOR) and internal CSR are positively related to employees’ affective commitment. Job satisfaction is found to be mediating the relationship between PRESOR and affective commitment. The result also showed that internal CSR practices mediate the relationship between PRESOR and affective commitment.

There are few limitations in the present research. First, present research merely investigates the practices of ethics and social responsibility by SMEs and did not perform a comparison with larger organizations. Second, the use of non-probability sampling method is unable to generalize results for the entire population. Future research could address the shortcoming of present research in order to further contribute to the academic and business world.

The findings provide insights to entrepreneurs and SMEs on the manifestation of ethics and social responsibility in enhancing employees’ affective commitment. In turn, it reduces employees’ turnover intention and enhances SMEs sustainability to strive in the competitive environment.

These findings highlight the positive chain effects of enterprises in discharging their moral obligation as well as their social responsibility. The enterprises are benefited from the good reputation which may act as a magnet to attract talent-employees and also sustaining their enterprises through employees’ retention.

This research paper contributes to current knowledge by painting a better picture on the importance of ethics and social responsibility and internal CSR from the employees’ perception. As to date, employees’ perception of ethics and social responsibility and internal CSR practices are still under-investigated. Moreover, past research often focuses the impact of ethics and social responsibility in the larger corporation but scant research is conducted in the smaller organization such as SMEs.

The purpose of this paper is twofold: first, to provide a descriptive analysis of consumers’ ability to comprehend and use common price promotion information when they choose to do so (e.g. to find the least expensive price or to understand the savings amount); second, to identify which consumer groups (in terms of demographic characteristics) find price promotion comprehension particularly challenging.

An online questionnaire with 14 measures (four literacy, ten numeracy) was administered in the study. Data from 607 Australian consumers were analysed using descriptive, cross-tabulation, and multiple regression analysis via IBM SPSS analytics software.

On average, 20 per cent of the consumers surveyed were unable to comprehend the price promotion signage. On average, 13 per cent of the consumers were unable to carry out arithmetic tasks using the information on price promotion signage. Multiple regression models showed that income level was the main driving factor for the consumers’ price promotion literacy and numeracy levels.

The present study is the first exploratory examination of consumers’ levels of comprehension (literacy) and effective use (numeracy) regarding common types of price promotion communication. The use of online samples and data collection method overestimates the results effect.

This is a pilot field study to investigate whether levels of consumers’ price promotion literacy and numeracy are adequate for everyday decision making. The information can be used as evidence and justification for further research.

The purpose of this paper is to study the consumer opinion towards the low-cost airlines or low-cost carriers (LCCs) (these two terms are used interchangeably) industry in Malaysia to better understand consumers’ needs and to provide better services. Sentiment analysis is undertaken in revealing current customers’ satisfaction level towards low-cost airlines.

About 10,895 tweets (data collected for two and a half months) are analysed. Text mining techniques are used during data pre-processing and a mixture of statistical techniques are used to segment the customers’ opinion.

The results with two different sentiment algorithms show that there is more positive than negative polarity across the different algorithms. Clustering results show that both K-Means and spherical K-Means algorithms delivered similar results and the four main topics that are discussed by the consumers on Twitter are customer service, LCCs tickets promotions, flight cancellations and delays and post-booking management.

Gaining knowledge of customer sentiments as well as improvements on the four main topics discussed in this study, i.e. customer service, LCCs tickets promotions, flight cancellations or delays and post-booking management will help LCCs to attract more customers and generate more profits.

This paper provides useful insights on customers’ sentiments and opinions towards LCCs by utilizing social media information.

The purpose of this paper is to investigate the effect of including letter repetition commonly found within social media text and its impact in determining the sentiment scores for two major airlines in Malaysia.

A Sentiment Intensity Calculator (SentI-Cal) was developed by assigning individual weights to each letter repetition, and tested it using data collected from official Facebook pages of the airlines.

Evaluation metrics indicate that SentI-Cal outperforms the baseline tool Semantic Orientation Calculator (SO-CAL), with an accuracy of 90.7 percent compared to 58.33 percent for SO-CAL.

A more accurate sentiment score allows airline services to easily obtain a better understanding of the sentiments of their customers, hence providing opportunities in improving their airline services.

Proposed mechanism calculates sentiment intensity of social media text by assigning individual weightage to each repeated letter and exclamation mark thus producing a more accurate sentiment score.

Due to various historical differences in organising, documenting and managing information across cultural institutions, cross‐domain resource discovery in the cultural heritage sector remains problematic. Leveraging on the lessons learnt from current efforts to enhance access to networked digital cultural resources, this paper seeks to explore the development of a taxonomy for the same purpose.

A methodology was developed for the construction of a 500‐term taxonomy for a Singapore‐based cultural heritage network. Based on this taxonomy, the benefits and problems of developing such a knowledge organisation system for a cultural network are also discussed.

Collecting terms and concepts from various external and internal sources was sufficient to kick‐start the taxonomy development process. One of the key aspects of the Integrated Museum and Archives System (IMAS) taxonomy is its ability to show a broad overview of the all the resources held by the National Heritage Board, regardless of originating institution.

No single scheme currently best fits the needs of cultural networks, but all can benefit from each other's learning process. The development of the IMAS taxonomy particularly benefited from the insights provided by faceted classification and the possible relationships between terms and concepts in ontology. Such guidelines and recommendations also provide much ground and basis for the NHB to start a framework for standardising documentation practices.

The purpose of this paper is to study sarcasm in online text – specifically on twitter – to better understand customer opinions about social issues, products, services, etc. This can be immensely helpful in reducing incorrect classification of consumer sentiment toward issues, products and services.

In this study, 5,000 tweets were downloaded and analyzed. Relevant features were extracted and supervised learning algorithms were applied to identify the best differentiating features between a sarcastic and non-sarcastic sentence.

The results using two different classification algorithms, namely, Naïve Bayes and maximum entropy show that function words and content words together are most effective in identifying sarcasm in tweets. The most differentiating features between a sarcastic and a non-sarcastic tweet were identified.

Understanding the use of sarcasm in tweets let companies do better sentiment analysis and product recommendations for users. This could help businesses attract new customers and retain the old ones resulting in better customer management.

This paper uses novel features to identify sarcasm in online text which is one of the most challenging problems in natural language processing. To the authors’ knowledge, this is the first study on sarcasm detection from a customer management perspective.

Machine learning algorithms are useful to effectively analyse, and therefore automatically classify online reviews. The purpose of this paper is to demonstrate a novel text-mining framework and its potential for use in the classification of unstructured hotel reviews.

Well-known data mining methods (i.e. boosted decision trees (BDT), classification and regression trees (C&RT) and random forests (RF)) in conjunction with incorporating five-fold cross-validation are used to predict the star rating of the hotel reviews. To achieve this goal, extracted features are used to create a composite variable (CV) to deploy into machine learning algorithms as the main feature (variable) during the learning process.

BDT outperformed the other alternatives in the exact accuracy rate (EAR) and multi-class accuracy rate (MCAR) by reaching the accuracy rates of 0.66 and 0.899, respectively. Moreover, phrases such as “clean”, “friendly”, “nice”, “perfect” and “love” are shown to be associated with four and five stars, whereas, phrases such as “horrible”, “never”, “terrible” and “worst” are shown to be associated with one and two-star hotels, as it would be the intuitive expectation.

To the best of the knowledge, there is no study in the existent literature, which synthesizes the knowledge obtained from individual features and uses them to create a single composite variable that is powerful enough to predict the star rates of the user-generated reviews. This study believes that the proposed method also provides policymakers with a unique window in the thoughts and opinions of individual users, which may be used to augment the current decision-making process.

Strategic planning (SP) enables enterprises to plan management and operations activities efficiently in the medium and large term. During its implementation, many processes and methods are manually applied and may be time consuming. The purpose of this paper is to introduce an automatic method to define strategic plans by using text mining (TM) algorithms within a generic SP model especially suited for small- and medium-sized enterprises (SMEs).

Textual feedbacks were collected through a SWOT matrix during the implementation of a SP model in a company dedicated to the local distribution of food. A four-step TM process (performing acquisition, pre-processing, processing, and validation tasks) is applied via a framework developed under the cloud computer paradigm in order to determine the strategic plans.

The use of categorization and clustering algorithms show that unstructured textual information produced during the SP can be efficiently processed and capitalized. Collected evidence reveals the potential to enhance the strategic plans creation with less effort and time, improving the relevance, and producing new technological resources accessible to SMEs.

An innovative framework especially suited for the SMEs based on the synergy assumption of the coupling between TM and a generic SP model.

Literature has shown that phenolic acids and flavonoids are bearing with hypoglycemic and anti-adipogenic properties. Therefore, this study aims to evaluate the possibility of phenolic-rich soya bean husk powder extract (SHPE) in combating diabetes and obesity using in vitro models.

The hypoglycemic properties were evaluated by determining the ability of SHPE (25-100 µg/mL) in inhibiting a-amylase and a-glucosidase enzymes and in triggering insulin secretion in BRIN-BD11 cells. Murine 3T3-L1 adipocytes were used for evaluating the anti-adipogenic properties of SHPE through the determination of relative lipid accumulation, triglyceride content and glycerol-3-phosphate dehydrogenase (GPDH) activity.

The hypoglycemic properties of SHPE was in the dose-dependent manner, where 100 µg SHPE/mL exhibited a significant higher (p < 0.05) a-amylase inhibitory activity (56.8 ± 0.11 per cent) and insulin secretion activity (0.73 ± 0.02 µg/l) against other concentrations. In contrast to the aforementioned findings, a significant lower a-glucosidase inhibitory activity (52.0 ± 0.44 per cent) was also observed in 100 µg SHPE/mL. Nevertheless, findings revealed that all the SHPE were able to inhibit the activity of a-amylase and a-glucosidase and stimulated the insulin secretion in BRIN-BD11 cells. On the other hand, the anti-adipogenic properties of SHPE were in the reverse dose-dependent manner, where 100 µg SHPE/mL demonstrated a significant lower (p < 0.05) relative lipid accumulation (48.5 ± 0.03 per cent), intracellular triglyceride content (5.7 ± 0.07 mg/dL) and GPDH activity (1.0 ± 0.01 mU/mL). These findings reflected that 100 µg SHPE/mL was a potent anti-adipogenic agent when compared with other concentrations. In conclusion, soya husk could emerge as a potential hypoglycemic and anti-adipogenic agents in in vitro models.

This was the first study to explore the effectiveness of phytochemicals derived from soya bean husk in ameliorating hyperglycemia and adipogenesis. Promising findings that derived from the present study could enable the scientists to re-evaluate the potential use of agricultural wastes, especially in the formulation of nutraceuticals.

This paper aims to clarify the challenge of emerging countries’ firms regarding their brand policy. The purpose of the paper is to examine the branding options offered to emerging countries companies when expanding internationally.

After having clarified the two paradoxes faced by emerging countries’ brands by way of synthesizing various works, the author formulates a model that provides a representation for the possible brand strategy choices of emerging country companies.

The authors formulate a framework with four brand management options which may be put into practice in emerging countries’ companies and suggest how an emerging country’s company could create and develop the best-adapted international brand policy depending on its specific situation regarding localness emphasis and customers’ risk reduction acceptance.

Results lead to the conclusion that the notion that only global brands are associated with higher product quality or prestige (in relation to local brands) is not a universal truth and thus needs to be interpreted with caution. The research provides support for a branding strategy embedded in the local emerging countries context and tally with research showing that more and more firms from emerging economies are using foreign image association strategies as important components of their branding and marketing strategies.

The proposal reinforces the contingency perspective of international marketing according to which brand policy may depend on company criteria, as well as foreign market specificities. The research confirms the competitive capacity of emerging countries’ companies’ brands, broadens the scope of international branding knowledge by shifting the focus to under-researched regions of the world.