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ABSTRACTPhysical activity interventions are proven to mitigate job stress and improve work performance. However, the recommended threshold of physical activity is rarely considered when evaluating the effect of physical activity. This study adopted case study approach to investigate the moderating effect of the threshold of physical activity on the relationship between emotional exhaustion and work performance, based on the conservation of resources theory. The research site was an Asian university wherein the university developed a mHealth application as a physical activity intervention to promote Exercise-is-Medicine® On Campus. A longitudinal survey was conducted to collect quantitative data for analysis, and interviews were conducted for further insights. The results supported the hypothesis that the threshold of physical activity was an effective moderator in the exhaustion-performance relationship. This study enriches the burnout literature and substantiates literature on technology-enabled solutions for improving work performance by engaging users to do regular and sufficient physical activity.

ABSTRACTBased on the knowledge-based view, this paper examines how firms can achieve an enhanced new product performance from the absorptive capacity under technological turbulence. By examining 165 Chinese pharmaceutical firms' data, this paper found that under technological turbulence, the benefits of absorptive capacity on new product performance will decrease; however, if firms maintain complementary managerial mechanisms (technological environmental scanning mechanism and coordination mechanism in this paper), the decrease effect of technological turbulence could be mitigated. Theoretically, the negative moderating effect of technological turbulence extends the investigation of the under assumptions of absorptive capacity and enriches the research on the contingency context of ACAP. Moreover, the examination of the alleviation effect regarding the managerial mechanisms responds to the scholar's appeals for more research focusing on management factors in absorptive capacity and product development research. The results also highlight the managerial-environment fit in the new product development area. Practically, this paper offers managerial implications for managers: they should focus more attention on technological breakthroughs so as to prepare early for any potential uncertainties and opportunities; moreover, managers could cultivate the managerial mechanisms to enhance knowledge monitoring and sharing activities, which could be utilised to buffer the uncertainty and update the firm's ACAP.

Several surveys are conducted to determine the effectiveness of the lean communication mediums in enhancing the information exchange taking place in large-groups. The various approaches that can be adopted for increasing the effectiveness of these mediums in certain situations are also discussed.

Snow is the most reflective, and also the most insulative, natural material on Earth. Consequently, it is an integral part of the sea-ice and climate systems. However, the spatial and temporal heterogeneities of snow pose challenges for observing, understanding and modelling those systems under anthropogenic warming. Here, we survey the snow–ice system, then provide recommendations for overcoming present challenges. These include: collecting process-oriented observations for model diagnostics and understanding snow–ice feedbacks, and improving our remote sensing capabilities of snow for monitoring large-scale changes in snow on sea ice. These efforts could be achieved through stronger coordination between the observational, remote sensing and modelling communities, and would pay dividends through distinct improvements in predictions of polar environments.

The Arctic Oscillation (AO) is the leading mode of extratropical northern hemisphere atmospheric variability, affecting surface pressure, winds, temperature, and precipitation. Here we use an altimeter sea level record spanning 2003–2014, covering the ice‐covered and ice‐free ocean, to examine the influence of the AO on Arctic sea level and surface geostrophic circulation. AO‐driven alongshore wind anomalies drive cross‐shelf Ekman transport and opposing barotropic sea level anomalies between the shelf seas and deep basins of the Arctic Ocean, with maximum sea level anomaly differences across the shelf‐break of ~3 cm per unit AO index. This pattern of sea level variability generates topographically steered (generally along‐shelf) current anomalies of around 0.5 cm/s per unit AO index. AO‐driven wind variability modulates surface currents associated with Atlantic and Pacific water inflow, with opposing inflow anomalies between the Barents Sea Opening and Bering Strait. The Arctic Oscillation dominates large‐scale changes in atmospheric pressure, wind, temperature, and precipitation north of 20°N. This study uses satellite measurements to investigate the impact the Arctic Oscillation has on sea level and upper ocean currents in the Arctic Ocean. Winds associated with the Arctic Oscillation cause sea levels to rise in the shallow coastal shelf seas, and drop in the deeper ocean, or vice versa, depending if the Arctic Oscillation is in a positive or negative phase. These sea level patterns drive currents which generally follow the edge of the continental shelf. The Arctic Oscillation also influences the strength of the currents associated with the inflow of Atlantic Water through the Barents Sea and the inflow Pacific Water through the Bering Strait. Monthly record of ice‐covered and ice‐free Arctic sea level and surface currents used to examine the Arctic Ocean response to the AOAO drives cross‐shelf Ekman transport, leading to opposing barotropic sea level anomalies between the Arctic shelf seas and deeper basinsAO sea level variability induces along‐shelf current anomalies and modulates currents associated with Atlantic and Pacific Water inflows

This study combines sea surface height (SSH) estimates of the ice‐covered Southern Ocean with conventional open‐ocean SSH estimates from CryoSat‐2 to produce monthly composites of dynamic ocean topography (DOT) and sea level anomaly (SLA) on a 50 km grid spanning 2011–2016. This data set reveals the full Southern Ocean SSH seasonal cycle for the first time; there is an antiphase relationship between sea level on the Antarctic continental shelf and the deeper basins, with coastal SSH highest in autumn and lowest in spring. As a result of this pattern of seasonal SSH variability, the barotropic component of the Antarctic Slope Current (ASC) has speeds that are regionally up to twice as fast in the autumn. Month‐to‐month circulation variability of the Ross and Weddell Gyres is strongly influenced by the local wind field, and is correlated with the local wind curl (Ross: −0.58; Weddell: −0.67). SSH variability is linked to both the Southern Oscillation and the Southern Annular Mode, dominant modes of southern hemisphere climate variability. In particular, during the strong 2015–2016 El Niño, a sustained negative coastal SLA of up to −6 cm, implying a weakening of the ASC, was observed in the Pacific sector of the Southern Ocean. The ability to examine sea level variability in the seasonally ice‐covered regions of the Southern Ocean—climatically important regions with an acute sparsity of data—makes this new merged sea level record of particular interest to the Southern Ocean oceanography and glaciology communities. New monthly merged sea level record of the ice‐covered and ice‐free Southern Ocean between 2011 and 2016, from CryoSat‐2 radar altimetryAntarctic coastal sea level peaks in autumn, minimum in spring; Antarctic Slope Current speeds regionally up to twice as fast in autumnSouthern Oscillation and Southern Annular Mode drive significant Antarctic coastal sea level response, modulating the ASC strength

We examine 4 years (2011–2014) of sea surface heights (SSH) from CryoSat‐2 (CS‐2) over the ice‐covered Arctic and Southern Oceans. Results are from a procedure that identifies and determines the heights of sea surface returns. Along 25 km segments of satellite ground tracks, variability in the retrieved SSHs is between ∼2 and 3 cm (standard deviation) in the Arctic and is slightly higher (∼3 cm) in the summer and the Southern Ocean. Average sea surface tilts (along these 25 km segments) are 0.01 ± 3.8 cm/10 km in the Arctic, and slightly lower (0.01 ± 2.0 cm/10 km) in the Southern Ocean. Intra‐seasonal variability of CS‐2 dynamic ocean topography (DOT) in the ice‐covered Arctic is nearly twice as high as that of the Southern Ocean. In the Arctic, we find a correlation of 0.92 between 3 years of DOT and dynamic heights (DH) from hydrographic stations. Further, correlation of 4 years of area‐averaged CS‐2 DOT near the North Pole with time‐variable ocean‐bottom pressure from a pressure gauge and from GRACE, yields coefficients of 0.83 and 0.77, with corresponding differences of <3 cm (RMS). These comparisons contrast the length scale of baroclinic and barotropic features and reveal the smaller amplitude barotropic signals in the Arctic Ocean. Broadly, the mean DOT from CS‐2 for both poles compares well with those from the ICESat campaigns and the DOT2008A and DTU13MDT fields. Short length scale topographic variations, due to oceanographic signals and geoid residuals, are especially prominent in the Arctic Basin but less so in the Southern Ocean. Time‐varying dynamic topography of ice‐covered oceans from CryoSat‐2Assessment with dynamic height from hydrographic stations and ocean bottom pressureRelative length scale and amplitude of baroclinic and barotropic signals in Arctic Ocean

After the summer of 2013, a convergence‐induced tail in the thickness distribution of the ice cover is found along the Arctic coasts of Greenland and Canadian Arctic Archipelago. Prompted by this, a normalized ice convergence index (ICI) is introduced to examine the variability and extremes in convergence in a 23 year record (1992–2014) of monthly ice drift. Large‐scale composites of circulation patterns, characteristic of regional convergence and divergence, are examined. Indeed, the ICI shows the June 2013 convergence event to be an extreme (i.e., ICI > 2). Furthermore, there is a cluster of 9 months over a 17 month period with positive ICIs (i.e., >1) following the record summer minimum ice extent (SMIE) in 2012; the imprint of ice dynamics from this cluster of positive ICIs likely contributed to higher SMIEs in 2013 and 2014. The impact of convergence on SMIE is discussed, and the increase in Arctic ice volume in 2013 is underscored. Impact of regional ice convergence on thickness distribution and sea ice roughnessRecord of extremes and variability of regional sea ice convergence since 1992–2014Potential impact of ice dynamics on observed variability of summer ice extent in 2013 and 2014

Using ICESat‐2 and CryoSat‐2 freeboards, we examine the variability of monthly Arctic sea ice snow depth, thickness and volume between October 2018 and April 2021. For the 3 years, satellite‐derived estimates captured a decrease in mean April snow depth (∼2.50 cm) and ice thickness (∼0.28 m) equivalent to an ice volume loss of ∼12.5%. Results show greater thinning of multiyear ice with an end‐of‐season thickness in 2021 that is lower by ∼16.1% (0.50 m), with negligible changes over first‐year ice. For the period, sea ice thickness estimates using snow depth from climatology result in thicker ice (by up to ∼0.22 m) with a smaller decrease in multiyear ice thickness (∼0.38 m). An 18‐year satellite record, since the launch of ICESat, points to a loss of ∼6,000 km3or one‐third of the winter Arctic ice volume driven by decline in multiyear‐ice coverage in the multi‐decadal transition to a largely seasonal ice cover. Ice thickness and volume are critical variables for assessing the evolution and response of the polar sea ice cover to a warming climate. Retrieval of sea ice thickness from altimeter freeboards (i.e., the vertical height of the floating ice and snow above the local sea level) requires knowledge of loading due to snow. Until recently, snow depth has been prescribed with a climatology based on historical field records. Using freeboard differences from ICESat‐2 and CryoSat‐2, we are now able to derive snow depth estimates. In this paper we examine the differences between climatological and satellite‐derived snow depth as well as the retrieved ice thicknesses from the two altimeter missions. Their changes for three winters between 2018 and 2021 are documented. Derived ice volume estimates are placed within the context of an 18‐year satellite record. For 2018–2021, the decline in mean Arctic sea ice thickness of ∼0.28 m in spring is largely explained by ∼0.50 m thinning of multiyear iceSatellite‐derived snow depth estimates, when compared to climatology, provide a thinner Arctic ice thickness especially during the fallSatellite records since 2003 show a loss of one‐third of the winter sea ice volume driven by the decline in multiyear ice coverage For 2018–2021, the decline in mean Arctic sea ice thickness of ∼0.28 m in spring is largely explained by ∼0.50 m thinning of multiyear ice Satellite‐derived snow depth estimates, when compared to climatology, provide a thinner Arctic ice thickness especially during the fall Satellite records since 2003 show a loss of one‐third of the winter sea ice volume driven by the decline in multiyear ice coverage

In the era of Web 2.0, huge volumes of consumer reviews are posted to the Internet every day. Manual approaches to detecting and analyzing fake reviews (i.e., spam) are not practical due to the problem of information overload. However, the design and development of automated methods of detecting fake reviews is a challenging research problem. The main reason is that fake reviews are specifically composed to mislead readers, so they may appear the same as legitimate reviews (i.e., ham). As a result, discriminatory features that would enable individual reviews to be classified as spam or ham may not be available. Guided by the design science research methodology, the main contribution of this study is the design and instantiation of novel computational models for detecting fake reviews. In particular, a novel text mining model is developed and integrated into a semantic language model for the detection of untruthful reviews. The models are then evaluated based on a real-world dataset collected from amazon.com. The results of our experiments confirm that the proposed models outperform other well-known baseline models in detecting fake reviews. To the best of our knowledge, the work discussed in this article represents the first successful attempt to apply text mining methods and semantic language models to the detection of fake consumer reviews. A managerial implication of our research is that firms can apply our design artifacts to monitor online consumer reviews to develop effective marketing or product design strategies based on genuine consumer feedback posted to the Internet.

The changing Arctic freshwater content and distribution have a significant implications for ocean circulation, climate, and water and biogeochemical cycles. The paucity of in situ salinity measurements in the Arctic Ocean has limited our ability to study Arctic‐Ocean freshwater variability. Although satellite‐derived sea surface height (SSH) and ocean bottom pressure (OBP) have been used together to infer depth‐integrated freshwater content changes, these measurements are limited in sampling and resolution. Motivated by the recent development of sea surface salinity (SSS) remote sensing, we explore the use of SSS as a proxy for Arctic freshwater changes. As a first step, here we conduct a proof‐of‐concept study by analyzing the output of an ocean‐ice state estimation product. We find that SSS variations are coherent with those of SSH‐minus‐OBP across the Arctic basin (with R~ −0.8) except for those in the center of the Beaufort Gyre and the region affected by the subpolar North Atlantic inflow. On Arctic shelves, the linear regression coefficient between SSS and SSH‐minus‐OBP is −0.3 pss/cm or larger in magnitude. The results suggest that SSS is a good proxy for Arctic freshwater changes, and satellite SSH‐minus‐OBP can be used to evaluate satellite SSS quality. Sea surface salinity (SSS) is found to be a good proxy for characterizing fresher changes in much of the Arctic OceanSatellite altimetry‐gravimetry measurements can help evaluate satellite SSS, alleviating the paucity of in situ salinity data for validationThe findings will benefit ongoing cal/val of satellite SSS in the Arctic and ongoing field programs in the Arctic

The Fram Strait sea ice volume export 1992–2014 is derived by combining sea ice thickness from upward looking sonars (ULS) with satellite observations of sea ice drift and area. Fram Strait is the main gate for sea ice export from the Arctic. The average yearly sea ice export is 2,400 ± 640 km3. The mean and modal ULS ice thickness in Fram Strait decreased by 15% and 21% per decade, respectively, during 1990–2014. Combined with sea ice drift and area this leads to a decrease of the Arctic sea ice volume export of 27 ± 2% per decade between 1992 and 2014. Thus, for the given time period, changes in sea ice export do not drive the sea ice volume decrease in the Arctic Basin. However, for individual years like 2007 and 2012 the ice export likely has contributed to the loss of summer sea ice. Combined with PIOMAS model simulation we estimate that 14% of the total Arctic sea ice volume is exported every year through Fram Strait. This fraction of the total sea ice volume exported per year does not show a trend because the Arctic Basin ice volume is decreasing at a similar rate as the Fram Strait ice volume export. Compared to ice velocities from Acoustic Doppler Current Profiler (ADCP) the satellite ice drift shows good correspondence in variability but a negative bias. Ice volume transport estimates presented here thus should be considered a conservative estimate. We show, however, that the transport estimates are not sensitive to the exact flux gate location. A new, consistent, observation‐based time series of sea ice volume export through Fram Strait is derived for the years 1992 to 2014During that time period the sea ice volume export decreased by 54±4  km3month−1per decade or 27±2% per decadeThe percentage of total available Arctic sea ice volume exported per year stayed at a constant value of about 14%

National Aeronautics and Space Administration's (NASA's) Ice, Cloud, and land Elevation Satellite‐2 (ICESat‐2) mission was launched in September 2018 with the primary goal of monitoring our rapidly changing polar regions. The sole instrument onboard, the Advanced Topographic Laser Altimeter System, is now providing routine, very high‐resolution, surface elevation data across the globe, including the Arctic and Southern oceans. In this study, we demonstrate our new processing chain for converting the along‐track ICESat‐2 sea ice freeboard product (ATL10) into sea ice thickness, focusing our initial efforts on the Arctic Ocean. For this conversion, we primarily make use of snow depth and density data from the NASA Eulerian Snow on Sea Ice Model. The coarse resolution (~100 km) snow data are redistributed onto the high‐resolution (approximately 30–100 m) ATL10 freeboards using relationships obtained from snow depth and freeboard data collected by NASA's Operation IceBridge mission. We present regional sea ice thickness distributions and highlight their seasonal evolution through our first winter season of data collection. We include ice thickness uncertainty estimates, while also acknowledging the limitations of these estimates. We generate a gridded monthly thickness product and compare this with various monthly sea ice thickness estimates obtained from European Space Agency's CryoSat‐2 satellite mission, with ICESat‐2 showing consistently lower thicknesses. Finally, we compare our February/March 2019 thickness estimates to ICESat February/March (19 February to 21 March) 2008 ice thickness estimates using the same input assumptions, which show an ~0.37 m or ~20% thinning across an inner Arctic Ocean domain in this 11‐year time period. NASA's ICESat‐2 mission was launched in September 2018 with the primary goal of monitoring our rapidly changing polar regions. The sole instrument onboard is a highly precise laser, which is now providing routine, very high‐resolution, surface height measurements across the globe, including over the Arctic and Southern oceans. In this study, we show new estimates of Arctic sea ice thickness from the first winter season of data collected by ICESat‐2. Sea ice thickness is calculated by combining the measured ICESat‐2 freeboards—the extension of sea ice above sea level—with a new snow on sea ice model. Our derived thicknesses are consistently lower than the thicknesses calculated from ESA's CryoSat‐2 data and the original ICESat mission, which ended in 2008. More work is needed to verify these new thickness estimates. We present new estimates of sea ice thickness derived from ICESat‐2 freeboards for the first Arctic winter season of data collectionWe highlight the regional and seasonal variability in the Arctic winter sea ice freeboard, snow depth, and thickness distributionsOur IS‐2 thickness estimates are consistently thinner than those estimated from CryoSat‐2 data using the same input assumptions