Search results

The analysis of multimedia content is being applied in various real-time computer vision applications. In multimedia content, digital images constitute a significant part. The representation of digital images interpreted by humans is subjective in nature and complex. Hence, searching for relevant images from the archives is difficult. Thus, electronic image analysis strategies have become effective tools in the process of image interpretation.

The traditional approach used is text-based, i.e. searching images using textual annotations. It consumes time in the manual process of annotating images and is difficult to reduce the dependency in textual annotations if the archive consists of large number of samples. Therefore, content-based image retrieval (CBIR) is adopted in which the high-level visuals of images are represented in terms of feature vectors, which contain numerical values. It is a commonly used approach to understand the content of query images in retrieving relevant images. Still, the performance is less than optimal due to the presence of semantic gap among the image content representation and human visual understanding perspective because of the image content photometric, geometric variations and occlusions in search environments.

The authors proposed an image retrieval framework to generate semantic response through the feature extraction with convolution network and optimization of extracted features using adaptive moment estimation algorithm towards enhancing the retrieval performance.

The proposed framework is tested on Corel-1k and ImageNet datasets resulted in an accuracy of 98 and 96%, respectively, compared to the state-of-the-art approaches.

The purpose of this chapter is to critically evaluate the development of technological infrastructure and the propensity of Latin America and the Caribbean (LAC) destinations to adopt digitalisation in their hotel sectors. This paper focuses on technological readiness, the types of digitalisation and its influence on the hotel sector in a post-pandemic environment.

The chapter is a critical and conceptual overview of digitalisation in the Latin America and the Caribbean hotel sectors, the contribution of the hotel sector to tourism economies, and digitalisation in LAC post COVID-19. A comprehensive examination of the academic literature is combined with the Network Readiness Index (NRI) and Travel and Tourism Competitiveness Index (TTCI) on several LAC countries focusing on different scopes including ICT readiness, ICT environment and business usage from 2010 to 2020. The paper uses empirical data collected from NRI and TTCI on several LAC countries examining changes in development of information and communications technology (ICT) by conducting a longitudinal analysis over a ten-year period.

The chapter argues that digitalisation in the hotel sector has been given a lot of attention regarding the adoption of digitalisation during the pandemic by a continuation of activities involving check-in and check-out, providing safety for guests and staff and the recovery of the hotel sector by both hoteliers and academics. Yet, LAC have been slow when it comes to the development of ICT. This is shown on the NRI and the TTCI by their position on ICT infrastructure, ICT environment and business usage. Nevertheless, while their positions on the indexes are less than favourable, their scores are improving, but at a very slow rate.

Limited academic literature is available on digitalisation in LAC countries. There is no consistent data on the NRI and TTCI year to year for some of the countries examined in this study.

This study provides a comprehensive review of technological infrastructure development of countries of Latin America and Caribbean countries with an emphasis on digitalisation in a hospitality context. The chapter is a critical examination of digitalisation in the hotel sector in a post-pandemic environment.

The authors look back and forward in terms of challenges and opportunities for marketing, viewed from the vantage point of the subsistence marketplaces stream. The authors discuss how marketing can evolve and expand to address the scale and scope of challenges that lie ahead. By way of challenges, the authors discuss the confluence of uncertainties, such as inherent in the Base of the Pyramid (BoP) contexts, in environmental issues, and in the arena of technological solutions, as well as the confluence of unfamiliarities among managers, students, and researchers. The authors discuss opportunities for marketing through a bottom-up approach and argue for evolving marketing with rapidly changing reality in BoP markets, a harbinger and an innovation laboratory for all contexts.

There is an ongoing debate about the purpose of business and profit maximisation. However, contemporary marketing thinking suggests that pro-social behaviour is a critical aspect of marketing strategy, wherein this type of behaviour leads to marketplace advantage. Amongst the many theories and frameworks (such as, creating shared value and subsistence marketplaces) that re-imagine the purpose of business to include pro-social behaviour, a prominent one is the base of the pyramid (BoP) thesis. This thesis challenges firms to simultaneously alleviate poverty and make a profit by targeting the poorest socioeconomic segment. However, it has encountered robust criticism, with some scholars suggesting a lopsided focus on profits to the detriment of poverty alleviation. Specifically, the criticisms centre on the marketers’ narrow focus on income poverty. In order to overcome these criticisms, as well as to envision a pathway to succeed at the BoP, this chapter makes the case to conceptualise poverty beyond an economic focus of income, assets and wealth to capture beyond economic factors such as equality, justice and freedom. The authors employ Amartya Sen’s capability approach as a starting point to reconceptualise poverty to facilitate marketers to genuinely alleviate poverty whilst making profits at the BoP.

The image that India carries in the minds of foreign investors is that of an orthodox, corrupt, poor country that is not investor friendly. Most of the international rating agencies have been inadvertently falling into this trap and have failed to objectively assess India vis‐a‐vis investor friendliness. The reality, however, is that India holds tremendous potential for the willing investor and is as investor friendly as any of the so‐called top countries. This paper attempts to offer an objective evaluation of India as an investment destination, and thereby shatter many of the myths about investing in India. There have been governments in power with diametrically opposite ideologies but the policy towards liberalization remains unchanged. The need for growth—and the strategies needed for that growth—is very well understood by Indian policymakers. Some decisions, like the one to go Nuclear, may have political overtones but, by and large, it is the economy that drives governance today.

Email spam classification is now becoming a challenging area in the domain of text classification. Precise and robust classifiers are not only judged by classification accuracy but also by sensitivity (correctly classified legitimate emails) and specificity (correctly classified unsolicited emails) towards the accurate classification, captured by both false positive and false negative rates. This paper aims to present a comparative study between various decision tree classifiers (such as AD tree, decision stump and REP tree) with/without different boosting algorithms (bagging, boosting with re-sample and AdaBoost).

Artificial intelligence and text mining approaches have been incorporated in this study. Each decision tree classifier in this study is tested on informative words/features selected from the two publically available data sets (SpamAssassin and LingSpam) using a greedy step-wise feature search method.

Outcomes of this study show that without boosting, the REP tree provides high performance accuracy with the AD tree ranking as the second-best performer. Decision stump is found to be the under-performing classifier of this study. However, with boosting, the combination of REP tree and AdaBoost compares favourably with other classification models. If the metrics false positive rate and performance accuracy are taken together, AD tree and REP tree with AdaBoost were both found to carry out an effective classification task. Greedy stepwise has proven its worth in this study by selecting a subset of valuable features to identify the correct class of emails.

This research is focussed on the classification of those email spams that are written in the English language only. The proposed models work with content (words/features) of email data that is mostly found in the body of the mail. Image spam has not been included in this study. Other messages such as short message service or multi-media messaging service were not included in this study.

In this research, a boosted decision tree approach has been proposed and used to classify email spam and ham files; this is found to be a highly effective approach in comparison with other state-of-the-art modes used in other studies. This classifier may be tested for different applications and may provide new insights for developers and researchers.

A comparison of decision tree classifiers with/without ensemble has been presented for spam classification.

This study aims to address the need to study salespersons’ thought self-leadership (TSL) and its effectiveness through the interplay of self-efficacy, skills and behavior at the individual level. It also advances the agenda of integrating self-leadership into marketing literature.

A model was tested using survey data collected from salespeople within pharmaceutical companies located in India and other Asian countries. A structural equation model was used to test the hypotheses.

The results suggest an interesting interplay between a salesperson’s TSL and his/her sales performance. The results also demonstrate the relationship between TSL and self-efficacy and the mediation mechanism through which self-efficacy influences sales performance. Results support the role of TSL as a distal predictor of performance and delineate the complexity of the mediation mechanism through theoretical grounding and empirical evidence.

The research suggests that a salesperson’s TSL relates positively with the sales performance through three process variables; self-efficacy, selling skills and adaptive selling behavior. The results should encourage managers to leverage salesperson’s TSL strategies to build a self-leading sales force and optimize supervision cost. Moreover, training the sales force for enhanced TSL has immediate payoffs in terms of increased selling effectiveness. The study also discusses theoretical implications.

By examining TSL in the sales context, the study makes an original contribution to the extant literature. The results of the study enrich the extant information on self-leadership and sales performance linkages by suggesting a mediation mechanism and proposing an integrated framework with selling skills and adaptive selling behavior.

Friction stir welding (FSW) is considered an environmentally sound process compared to traditional fusion welding processes. It is a complex process in which various parameters influence weld strength. Therefore, it is essential to identify the best parameter settings for achieving the desired weld quality. This paper aims to investigate the multi-response optimization of process parameters of the FSWed 6061-T6 aluminum (Al) alloy.

The input process parameters related to FSW have been sorted out from a detailed literature survey. The properties of weldments such as yield strength, ultimate tensile strength, percentage elongation and microhardness have been used to evaluate weld quality. The process parameters have been optimized using the Taguchi-based grey relational analysis (GRA) methodology. Taguchi L16 orthogonal array has been considered to design the experiments. The effect of input parameters on output responses was also determined by the analysis of variance (ANOVA) method. Finally, to corroborate the results, a confirmatory experiment was carried out using the optimized parameters from the study.

The ANOVA result indicates that the tool rotation speed was the most significant parameter followed by tool pin profile and welding speed. From the confirmation test, it was observed that the optimum FSW process parameters predicted by the Taguchi method improved the grey relational grade by 13.52%. The experimental result also revealed that the Taguchi-based GRA method is feasible in finding solutions to multi-response optimization problems in the FSW process.

The present study is unique in the multi-response optimization of FSWed 6061-T6 Al alloy using the Taguchi and GRA methodology. The weld material having better mechanical properties is essential for the material industry.

This paper aims to examine the impact of predictive gamification, through a sales simulator game, as a strategy to minimize sales problems in organizations.

This empirical study was executed in a large global company that produces and markets footwear for B2B and B2C markets. The company operates in more than 18 countries and adopts the gamification to train 7,600 sellers and 10,052 professionals directly linked to the sales area. The methodology adopted followed a qualitative and exploratory approach based on an in-depth case study analysis.

Results indicated that the use of predictive gamification allowed mitigating future problems, in average, four months in advance of the arrival of the new products at the points of sale. The main findings included improvement in sales arguments, product knowledge and sales closure. The company assigns the results to the implementation of the gamification and the possibility to simulate the evaluation of the level of readiness of the sales force resources. For this, the following three primary variables emerged: adequate definition of training content, analysis of the learning process and simulation of store reality.

The authors introduced to the sales training literature the concept of predictive gamification. The predictive gamification allows anticipating and reducing future problems before the arrival of the new products in the market, improving the argument of sales, the knowledge about the products and the ratio of sales conversion of stores.

First, the content design of gamification needs to be carefully projected before the simulator development, considering the target audience and its particularities. Second, the construction of evaluation activities needs to consider contexts, actions and results. Third, monitoring the paths taken by the seller in the simulator environment is requisite. Fourth, the analysis of the results of vendor responses in the simulator environment can be obtained through learning management system reports. Fifth, the development of game simulators with advanced technologies may be done with low investment.

The findings have implications for the field vis-à-vis three main research gaps identified in the literature discussing gamification in sales training. First, the authors identified best practices from the application of gamification in sales training. Second, they showed the sales qualification processes, which can be improved by applying gamification. Third, they presented strategies of use of gamification as an approach allied to the training of sales professionals to generate enhanced sales results.

Although the role of self-leadership is important, it remains understudied in business-to-business (B2B) selling context. This study aims to provide insights into the drivers and outcomes of behavioral self-leadership tested through a sample working in pharmaceutical sales in an emerging economy. In accord, the authors investigate the relationships between self-efficacy, behavioral self-leadership, adaptive selling and ultimately sales performance. This study also investigates the moderating role of technical knowledge.

Data were gathered from 208 salespeople working in pharmaceutical industry. AMOS 21.0 and SmartPLS3.0 were utilized to test the conceptual framework.

The study finds that self-efficacy is positively related to behavioral self-leadership that in turn is positively related to adaptive selling and sales performance. In addition, counter intuitive findings were uncovered related to salesperson’s technical knowledge. Those with high technical knowledge exhibited weaker relationship between self-efficacy and behavioral self-leadership, behavioral self-leadership and adaptive selling and that between behavioral self-leadership and sales performance than their counterparts with low technical knowledge.

This study extends work on self-leadership by exploring the effect of self-efficacy and behavioral self-leadership on sales performance. This study also extends the theory on salesperson’s knowledge by proposing the counter-intuitive effect of knowledge and self-efficacy and knowledge and behavioral self-leadership on adaptive selling and sales performance.

Sales managers should consider that not all employees indulging in behavioral self-leadership would reap benefits from the same. As such, sales managers should assess the level of technical knowledge of the salesforce and when determining their training programs that develop such self-leadership skills.

The study is one of the first to consider the drivers and outcomes of behavioral self-leadership and technical knowledge in a B2B sales context. By focusing on the interplay between knowledge and self-efficacy and knowledge and behavioral self-leadership, this study provides greater understanding of the effects of behavioral self-leadership than previously expected by sales researchers.