Search results

This study aims to investigate the influence of the reviewed establishment’s price level and the user’s social network size and reputation status on consumers’ tendency to post more visual imagery content. Furthermore, it explores the moderating effects of user experiences and geographic distance on these dynamics.

This study adopts a multi-method approach to explore both the determinants behind the sharing of user-generated photos in online reviews and their internal mechanisms. Using a comprehensive secondary data set from Yelp.com, the authors focused on restaurant reviews from a prominent tourist destination to construct econometric models incorporating time-fixed effects. To enhance the robustness of the authors’ findings, the authors complemented the big data analysis with a series of controlled experiments.

The reviewed establishments price level and the users reputation status and social network size incite corresponding motivations conspicuous display “reputation seeking” and social approval motivating users to incorporate more images in reviews. “User experiences can amplify the influence of these factors on image sharing.” An increase in the users geographical distance lessens the impact of the price level on image sharing, but it heightens the influence of the users reputation and social network size on the number of shared images.

As a result of this study, high-end establishments can increase their online visibility by leveraging user-generated visual content. A structured rewards program could significantly boost engagement by incentivizing photo sharing, particularly among users with elite status and extensive social networks. Additionally, online review platforms can enhance users’ experiences and foster more dynamic interactions by developing personalized features that encourage visual content production.

This research, anchored in trait activation theory, offers an innovative examination of the determinants of photo-posting behavior in online reviews by enriching the understanding of how the intricate interplay between users’ characteristics and situational cues can shape online review practices.

Though academic study on the subject is still in its early stages, there is growing interest in using blockchain technology for transforming the supply chain. The academic literature is divided and yet only includes studies evaluating how the supply chain has changed organizations. To comprehend the new phenomena, this study aims to investigate the factors of blockchain technology in driving supply chain transformation. To be more precise, the authors developed from the literature the most prevalent criteria for determining if supply chain transformations are ready to be scaled up.

This study followed a combination of two multi-criteria decision making methods evaluation based on distance from average solution and complex proportional assessment) methodology in this research: planning, investigating, executing out, establishing a rating of the criteria and evaluating it.

The study shows that the “organizational driver” and the “technology driver” are the factors most important to the transformation of the supply chain, whereas the “financial driver” and the “regulatory driver” are less important. This study also makes some managerial recommendations to address the factors impeding the supply chain’s transformation. Each factor’s significance was explored, and a proposed study agenda was also presented.

Although the main forces behind the transformation of the supply chain have been recognized, further research into statistical correlation is required to confirm how the various elements interact.

This research aids decision-makers in comprehending the key forces behind supply chain transformation. Managers and decision-makers might better predict and allocate the necessary resources to start the road toward digitization and make well-informed choices once these aspects have been investigated and understood.

In light of the pandemic’s effects on the world and the increase in businesses embracing the digital economy, the supply chain transformation is more important than ever. Beyond blockchain deployment and the pilot studies on digital transformation, there is a gap. The topics and factors this study uncovered will operate as a framework and recommendations for more theoretical investigation and practical applications.

This study aims to highlight the inconsistency between conventional knowledge push judgements and the price of knowledge push. Also, a three-way decision-based relevant knowledge push algorithm was proposed.

Using a ratio of 80–20%, the experiment randomly splits the data into a training set and a test set. Each video is used as a knowledge unit (structure) in the research, and the category is used as a knowledge attribute. The limit is then determined using the user’s overall rating. To calculate the pertinent information obtained through experiments, the fusion coefficient is needed. The impact of the push model is then examined in comparison to the conventional push model. In the experiment, relevant knowledge is compared using three push models, two push models based on conventional International classification functioning (ICF), and three push models based on traditional ICF. The average push cost accuracy rate, recall rate and coverage rate are metrics used to assess the push effect.

The three-way knowledge push models perform better on average than the other push models in this research in terms of push cost, accuracy rate and recall rate. However, the three-way knowledge push models suggested in this study have a lower coverage rate than the two-way push model. So three-way knowledge push models condense the knowledge push and forfeit a particular coverage rate. As a result, improving knowledge results in higher accuracy rates and lower push costs.

This research has practical ramifications for the quick expansion of knowledge and its hegemonic status in value creation as the main methodology for knowledge services.

To the best of the authors’ knowledge, this is the first theory developed on the three-way decision-making process of knowledge push services to increase organizational effectiveness and efficiency.

The COVID-19 outbreak reached a critical stage when it became imperative for public health systems to act decisively and design potential behavioral operational strategies aimed at containing the pandemic. Isolation through social distancing played a key role in achieving this objective. This research study examines the factors affecting the intention of individuals toward social distancing in India.

A correlation study was conducted on residents from across Indian states (N = 499). Online questionnaires were floated, consisting of health belief model and theory of planned behavior model, with respect to social distancing behavior initially. Finally, structural equation modeling was used to test the hypotheses.

The results show that perceived susceptibility (PS), facilitating conditions (FC) and subjective norms are the major predictors of attitude toward social distancing, with the effect size of 0.277, 0.132 and 0.551, respectively. The result also confirms that the attitude toward social distancing, perceived usefulness of social distancing and subjective norms significantly predict the Intention of individuals to use social distancing with the effect size of 0.355, 0.197 and 0.385, respectively. The nonsignificant association of PS with social distancing intention (IN) (H1b) is rendering the fact that attitude (AT) mediates the relationship between PS and IN; similarly, the nonsignificant association of FC with IN (H5) renders the fact that AT mediates the relationship between FC and IN.

The results of the study are helpful to policymakers to handle operations management of nudges like social distancing.

The research is one of its kind that explores the behavioral aspects of handling social nudges through FC.

The advantages of applying big data analytics for organizations to boost innovation performance are enormous. By collecting and analysing substantial amounts of data, firms can discern what works for their customer needs and update existing products while innovating new ones. Notwithstanding the evidence about the effects of big data analytics, the link between big data analytics and innovation performance is still underestimated. Especially in today's fast-changing and complicated environments, companies cannot simply take big data analytics as one innovative technical tool without fully understanding how to deploy it effectively.

This study tries to investigate this relationship by building on the knowledge absorptive capacity perspective. The authors conceptualized effective use of big data analytics tools as one general absorptive capacity rather than a simple technical element or skill. Specifically, effectively utilize big data analytics tools can provide values and insights for new product innovation performance in a turbulent environment. Using online survey data from 108 managers, the authors assessed their hypotheses by applying the structural equation modelling method.

The authors found that big data analytics capacity, which can be conceptualized as one absorptive capacity, can positively influence product innovation performance. The authors also found that environmental turbulence has strong moderation effects on these two main relationships.

These results establish big data analytics can be regarded as one absorptive capacity, which can positively boost an organization's innovation performance.

The Islamic world spans many different languages with different language structures. This paper aims to explore one way in which language structure affects consumer response to the marketing of cobrands.

Two between subject experiments were conducted using samples of participants from Saudi Arabia and the USA. The first manipulated partner brand category similarity and brand name order, along with the structure of the language used to communicate with the market. The data for this study includes Arabic speakers in Saudi Arabia as well as English speakers in the USA. The second study explores how targeting a population fluent in multiple languages of varied structure nullifies the findings from the first study and uses Latino participants in the USA.

This study finds that when brands come from similar product categories, name order did not affect cobrand evaluations, but it did when the brands come from dissimilar product categories. Here, evaluations of the cobrand are enhanced when the invited brand is in the position that adjectives occupy in the participant’s language. The authors also find that being proficient in two languages, each with a different default order for adjectives and nouns, quashes the effect of name order otherwise seen when brands from dissimilar product categories engage in cobranding.

By examining the impact of language structure on the effects of cobrand evaluation and conducting studies among participants with differing dominant languages, this research can rule out simple primacy or recency effects.

This study sought to develop an aggregated assessment of the literature on the resource-based view (RBV). The main aim was to map the RBV field based on a systematic literature review (SLR) of 226 academic articles published in refereed journals from 1994 to 2022.

Two bibliometric analysis methods were used: bibliographic coupling and co-citation. These measures are complementary because bibliographic coupling is retrospective in nature and co-citation is forward-looking.

The analysis identified the most influential studies, top-cited articles and journals and six major thematic clusters: RBV, customer orientation and alliance portfolio, resource-based theory, firm performance, entrepreneurial orientation (EO) and dynamic capabilities.

This research was based on a combination of bibliographic coupling and co-citation analysis. The results provide a better understanding of the RBV field’s intellectual structure, which reveals potential new lines of future research.

From a relational maintenance perspective, this study explores the impact of being envied (benignly vs maliciously) on consumers' feelings of social anxiety and its influence on their tendencies toward inconspicuous consumption, based on the resource conservation theory and the model of “Sensitivity about Being the Target of a Threatening Upward Comparison.” (STTUC)

Four studies were conducted in this paper. Studies 1a and 1b tested the main hypothesis that being maliciously envied (vs benignly) can increase consumers' inconspicuous consumption of luxury products and luxury hotel experiences. Study 2 replicated this finding and examined the mediating role of social anxiety. Study 3 investigated the moderating effect of ideal self-congruity (low vs high).

The findings reveal that being maliciously envied (vs benignly) is associated with higher levels of inconspicuous consumption and social anxiety acts as a mediating role. Moreover, when individuals have a strong sense of ideal self-congruity, the positive impact of being maliciously envied (vs benignly) on inconspicuous consumption is further amplified, confirming the moderating role of ideal self-congruity.

This study sheds light on a novel mechanism that elucidates how different types of being envied influence consumers' inconspicuous consumption and the conditions under which this impact is heightened.

The study sheds light on the internal enabling factors towards emerging market (EM) small and medium-sized enterprises’ (SMEs) preparedness for Industry 4.0 (I4.0) using three dimensions: managerial, operational and technological readiness.

The study uses convenience sampling, having online and paper-based surveys and collecting 110 responses from manufacturing Malaysian SMEs. This sample allowed assessing the relationships of the hypothesized variables through the structural model of data analysis.

This study’s findings demonstrate that financial capability and perceived benefits enhance Malaysian SMEs' managerial, operational and technological readiness.

Using Malaysia's case, this paper extends the discussion of the key drivers that underline the decision of EM firms to adopt I4.0.

This study’s results provide valuable insights for policymakers to improve the digital ecosystem. Also, understanding critical drivers for I4.0 readiness would encourage SMEs in Malaysia to embrace new digital technologies.

Although digital transformation towards I4.0 for manufacturing SMEs would be decisive, little is known about how ready these Malaysian firms are to adopt it or the driving factors that motivate them. Meanwhile, inadequate readiness causes a high failure rate in implementing new technology, processes or organizational changes.

The authors investigate the adoption of nomination committees in Australia and identify the managerial power perspective as one explanation for firms not establishing nomination committees. A positive outcome of establishing a nomination committee from the perspective of board diversity is also examined.

The authors adopt an archival approach by collecting data for firms listed on the Australian Securities Exchange (ASX) during the period 2010 to 2018. The authors establish the prevalence of nomination committees for small medium and large Australian firms. Regression analyses are used to determine whether the power of the chief executive officer (CEO) influences the adoption of a nomination committee. The association between having nomination committee and board diversity is also analyzed using regression analyses.

Less than half of firms adopt a nomination committee. Larger firms are more likely to adopt a nomination committee than medium and smaller sized firms. Firms with less powerful CEOs are more likely to adopt a nomination committee. Adoption of a nomination committee is also associated with greater board tenure dispersion and board gender diversity in medium and smaller sized firms.

Evidence on nomination committees provides original research that extends previous research focusing on the audit, risk and remuneration committees and samples restricted to large firms. The nomination committee has an important role to play in the appointment of directors yet limited evidence exists of the adoption rate, explanation for non-adoption and benefits of adoption. The authors add to this evidence.