Evaluating the Impact of green practices on company performance in the Montenegro hotel industry

This study examines the impact of green practices on organisational performance, in the Montenegro hotel industry. A mixed-method approach is employed consisting of two phases a questionnaire-based survey administered to those in managerial positions in the hotel, followed by a qualitative analysis of interviews conducted with hotel managers. The results indicate a significant impact of green practices on hotels’ performance observed through three indicators: quality of service, guest satisfaction and financial performance. Interestingly, investment in green marketing strategies did not have any effect on the relationship between green practices and performance. Further, interviews identified financial constraints, lack of support from the state and inadequate employee awareness about sustainability as major barriers to implementation of green practices. Guest awareness of sustainability was seen as an important enabler. The study is the only study looking at the impact of the adoption of green practices in hotels in Montenegro. The findings can be valuable for policy planners, tourism investors, as well as researchers in this specific industry

Investigating the ‘mission and profit’ paradox: Case study of an ecopreneurial organisation in India

Based on a case study of a waste management services provider in India, this paper sets out to investigate how an ecopreneurial organisation balances the competing demands of environmental mission and profit generation. Results indicate that two internal organisational mechanisms, namely Leadership, and Organisational Processes, are instrumental in achieving the dual goals. The paper highlights the role of contextual factors in supporting such organisations, and in doing so, it responds to the call for research to examine social entrepreneurship in wider contexts including Asia, Latin America, and Africa, to address ‘marginalisation’ of studies in the field

Policy Brief: Bridging the Gender Pay Gap

Gender pay gap (GPG) is a complex issue that various forums, including the G20, have attempted to discuss. Mitigating GPG requires large-scale transformative changes, but constraints on financial resources and public spending, along with cultural norms and deep-seated societal beliefs, make it a difficult task. Proposed actions, therefore, must be economically prudent and actionable. This Policy Brief offers five recommendations for the G20 to help bridge the gender pay gap. These include: introducing pay transparency legislation; mandating data-driven gender budgeting; increasing emphasis on parental leave; promoting women in STEM subjects; and engaging with the industry by proposing initiatives such as exclusive women only portals, reporting on gender, facilitating leadership programmes, and ‘de-biasing’ organisations. These proposals can help policymakers move the needle on gender equity, promote social justice, and improve economic outcomes

Automatic Validation of User-contributed Content Using Learned Similarity Function

The quality of user-contributed content at online platforms can vary. Manual validation of such content for quality and relevance is difficult and not scalable. No current techniques utilize known high quality content to rank user-contributed content. This disclosure describes techniques for scalable automatic validation of user-contributed content (UCC) provided to an online platform. A similarity function is learned based on merchant media and known high-quality user submitted media. The similarity function is used to score new UCC media and determine whether the new UCC media is relevant and of sufficient quality to include on the platform. The techniques improve the experience of using online platforms by ensuring that UCC is relevant to the entity with reference to which it is contributed and is of good quality. Further, the techniques can generate feedback on improving UCC contributions

Towards Automating Type Changes

Developers frequently change the type of a program element and update all its references for performance, security, concurrency, library migration, or better maintainability. Despite type changes being a common program transformation, it is the least automated and the least studied. Manually performing type changes is tedious since the programmers have to reason about propagating the type constraints of the new type over assignments, method hierarchies and subtypes. Existing automation approaches for type changes are inadequate for large-scale code bases. Moreover these techniques require the user to encode the adaptations for the type change in a DSL, which might not be straight-forward if she is unfamiliar with the types. These challenges introduce barriers in the adoption of these techniques. The thesis of this dissertation is: it is possible to design and im- plement type change techniques and tools that are scalable and usable. For this purpose, we developed: (i) TypeChangeMiner: a tool that accurately and eciently detects type changes performed in the version history of a project (ii) T2R: a ultra-scalable MapReduce amenable framework to perform type changes safely and accurately, and (iii) TC-Infer: a technique that learns the task of performing type changes by analyzing how other developers have previously performed the same type change

Big data academic and learning analytics: connecting the dots for academic excellence in higher education

Purpose Although big data analytics have great benefits for higher education institutions, due to lack of sufficient evidence on how big data analytics investment can pay off, it is tough for HEIs practitioners to realize value from such adoption. The current study proposes a big data academic and learning analytics enabled business value model to explain big data analytics potential benefits and business value which can be obtained by developing such analytics capabilities in HEIs. Design/methodology/approach The study examined 47 case descriptions from 26 HEIs to investigate the causal association between the big data analytics current and potential benefits and business value creation path for big data academic and learning analytics success in higher education institutions. Findings The pressure of compliance with all legal & regulatory requirements and competition had pushed higher education institutions hard to adopt BDA tools. However, the study found out that application of risk & security and predictive analytics to higher education fields is still in its infancy. Using this theoretical model, our results provide new insights to higher education administrators on ways to create big data analytics capabilities for higher education institutions transformation and suggest an empirical foundation that can lead to more thorough analysis of big data analytics implementation. Originality/value A distinctive theoretical contribution of this study is its conceptualization of understanding business value from big data analytics in the typical setting of higher education. The study provides HEIs with an all-inclusive understanding of big data analytics and gives insights on how it helps to transform HEIs. The new perspectives associated with the big data academic and learning analytics enabled business value model will contribute to future research in this area

Making User-Generated Content Available When a Device is Offline

Some applications such as digital maps support offline use, including download of certain types of data, e.g., map data of a region for navigation. However, the downloaded information does not include user-generated content (UGC), reviews, external feeds, problem reports, geo-tagged news, etc. Such content can include timely and topical information which is unavailable to users if their device is offline. This disclosure describes techniques to make curated UGC and third-party feeds of specific types available when a device is offline. UGC is curated by topic and location using a multimodal large language model or other suitable technique. With user permission, a map annotated with recent, relevant UGC is downloaded or pushed to a mobile app on the user device prior to the loss of wireless connectivity. Summarized UGC content is provided to enable offline operation. Key pieces of information that can enhance safety and improve user experience are thus made available even in the absence of a network. The described techniques can also be of value to users on low-bandwidth networks or in remote areas

An evolutionary stage model of outsourcing and competence destruction : a Triad comparison of the consumer electronics industry

Outsourcing has gained much prominence in managerial practice and academic discussions in the last two decades or so. Yet, we still do not understand the full implications of outsourcing strategy for corporate performance. Traditionally outsourcing across borders is explained as a cost-cutting exercise, but more recently the core competency argument states that outsourcing also leads to an increased focus, thereby improving effectiveness. However, no general explanation has so far been provided for how outsourcing could lead to deterioration in a firm‟s competence base. We longitudinally analyze three cases of major consumer electronics manufacturers, Emerson Radio from the U.S., Japan‟s Sony and Philips from the Netherlands to understand the dynamic process related to their sourcing strategies. We develop an evolutionary stage model that relates outsourcing to competence development inside the firm and shows that a vicious cycle may emerge. Thus it is appropriate to look not only at how outsourcing is influenced by an organization‟s current set of competences, but also how it alters that set over time. The four stages of the model are offshore sourcing, phasing out, increasing dependence on foreign suppliers, and finally industry exit or outsourcing reduction. The evolutionary stage model helps managers understand for which activities and under which conditions outsourcing across borders is not a viable option. Results suggest that each of these firms had faced a loss of manufacturing competitiveness in its home country, to which it responded by offshoring and then outsourcing production. When a loss of competences occurred, some outsourcing decisions were reversed

Identifying the optimal exercise prescription for patients with coronary artery disease undergoing cardiac rehabilitation: protocol for a systematic review and network meta-analysis of randomized control trials

Coronary artery disease (CAD) is one of the leading causes of mortality and morbidity. Exercise-based cardiac rehabilitation (EBCR) has been shown to improve clinical outcomes in these patients, and yet clinicians are often challenged to prescribe the most effective type of exercise training. Therefore, this systematic review and network meta-analysis (NMA) aims to formally quantify the optimal dose of exercise training interventions to improve exercise capacity and quality of life by undertaking direct and indirect pooled comparisons of randomized controlled trials. A detailed search will be conducted on PubMed/MEDLINE, Cumulative Index to Nursing and Allied Health (CINAHL), EMBASE and Web of Science. Two reviewers will screen the existing literature and assess the quality of the studies. Disagreements will be resolved through consensus. We anticipate that the analysis will include pairwise and Bayesian network meta-analyses. Most of the trials have studied the impact of exercise training comparing one or two modalities. As a result, little evidence exists to support which interventions will be most effective. The current NMA will address this gap in the literature and assist clinicians and cardiac rehabilitation specialists in making an informed decision. Results will be disseminated through peer-reviewed journals. Ethical approval is not applicable, as no research participants will be involved. PROSPERO Registration number: CRD42022262644